Industrial digital twin systems and methods with echelons of executive, advisory and operations messaging and visualization

Abstract

An industrial plant operation management platform integrating a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including AI-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an industrial plant operation, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to the following U.S. Provisional Patent Applications: Serial No. 63 / 087,293, filed 4 October 2020, entitled “ENTERPRISE MANAGEMENT PLATFORM FOR INDUSTRIAL PLANT OPERATIONS”; Serial No. 63 / 087,300, filed 5 October 2020, entitled “ENTERPRISE MANAGEMENT PLATFORM FOR INDUSTRIAL PLANT OPERATIONS”; Serial No. 63 / 111,526, filed 9 November 2020, entitled “DIGITAL TWIN VIBRATION VISUALIZATION SYSTEMS AND METHODS”; Serial No. 63 / 127,981, filed 18 December 2020, entitled “EXPERT SYSTEMS FOR MARKET ORCHESTRATION SYSTEMS FACILITATING ELECTRONIC VALUE CHAIN MARKETPLACE TRANSACTIONS”; and Serial No. 63 / 141,317, filed 25 January 2021, entitled “METHODS AND SYSTEMS FOR MANAGEMENT OF DIGITAL KNOWLEDGE” Each of the above applications is hereby incorporated by reference in its entirety as if fully set forth herein. BACKGROUND Field

[0002] The present disclosure relates to the field of enterprise management platforms, more particularly involving data management, artificial intelligence, network connectivity and digital twins. Description of the Related Art

[0003] Industrial environments, such as environments for large scale manufacturing (such as manufacturing of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Historically, data has been collected in industrial environments by human beings using dedicated data collectors, often recording batches of specific sensor data on media, such as tape or a hard drive, for later analysis. Batches of data have historically been returned to a central office for analysis, such as undertaking signal processing or other analysis on the data collected by various sensors, after which analysis can be used as a basis for diagnosing problems in an environment and / or suggesting ways to improve operations. This work has historically taken place on a time scale of weeks or months, and has been directed to limited data sets.

[0004] The emergence of the Internet of Things (loT) has made it possible to connect continuously to, and among, a much wider range of devices. Most such devices are consumer devices, such as lights, thermostats, and the like. More complex industrial environments remain more difficult, as the range of available data is often limited, and the complexity of dealing with data from multiple sensors makes it much more difficult to produce “smart” solutions that are effective for the industrial sector. A need exists for improved methods and systems for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments.

[0005] With the proliferation of vibration sensors and other Industrial Internet of Things (IIoT) sensors, there are vast amounts of data available relating to industrial environments. This data is useful in predicting the need for maintenance and for classifying potential issues in the industrial environments. There are, however, many unexplored uses for vibration sensor data and other IIoT sensor data that can improve the operation and uptime of the industrial environments and provide industrial entities with agility in responding to problems before the problems become catastrophic.

[0006] Organizations have access to an almost unlimited amount of data. With the advent of the Internet of Things (loT) the amount of data available to an organization has increased dramatically and will likely continue to do so. For example, in a manufacturing factory, warehouse, campus, or other operating environments, there may be hundreds to thousands of loT sensors that provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the factory, motion sensors that can track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many others. Additionally, as wearable technologies become more prevalent, wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers. Furthermore, as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets.

[0007] The presence of more data and data of new types offers many opportunities for organizations to achieve competitive advantages; however, it also presents problems, such as of complexity and volume, such that users can be overwhelmed, missing opportunities for insight. A need exists for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations. SUMMARY

[0007] One path to distilling information is digital twin technology, which can present large amounts of data in a digestible format that represents salient characteristics of an item, often updated in real time or near real time as the twin is updated to reflect the current state based on a pipeline of data about a represented item. While this is helpful, current digital twin technology has its limitations due to the fact that different roles within an organization may require different information to draw their insights. For example, a CEO of an industrial factory makes decisions based on a “10,000 foot view” of the company. The CEO may review profit and loss (P&L) data, industry trends, and employee trends (e.g., employee satisfaction or employee retention rates) to make overall decisions on behalf of the organization but does not necessarily need to see the granular data points to make decisions. In contrast, a different user, such as a CFO, may require more granular information, such as sales figures by region, marketing costs, maintenance costs, depreciation information, human capital costs, and costs of third-party vendors to draw her conclusions, but may not be as concerned with employee or industry trends. Similarly, a CTO may have no need for P&L data but may require an in-depth visualization of the processes within different manufacturing facilities to gain a better understanding of opportunities to improve process outcomes or to diagnose issues within processes, equipment or systems. Thus, a need exists for digital twins and other interfaces that are configured for particular roles.

[0008] As a further challenge, a given role may have varying needs based on context. For example, while the CEO might focus on higher-level data for many activities, such as strategic decision making or board communications, the same CEO may find more granular, micro-scale data useful for other activities, such as when an issue is escalated from a subdivision of the organization for input. Thus, a need exists for context-adaptive digital twins for each role, including ones that provide relevant displays and information of the right type at the right time for various situations and activities undertaken by the role.

[0009] More generally, ubiquitous connectivity and the proliferation of larger and larger data sets offer enterprise leaders opportunities for an unprecedented degree of awareness and control over enterprise assets and activities. A need and opportunity exist for an enterprise and industrial control tower by which executive leaders can, through various interfaces, including executive digital twins, dashboards, and similar systems, obtain timely information that is curated to invoke relevant awareness, support effective decisions and enable operational control.

[0010] According to some embodiments of the present disclosure, an enterprise management platform is disclosed. In some embodiments, the enterprise management platform integrates a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including Al-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an enterprise, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.

[0011] Further provided herein are methods and systems for enterprise and industrial control towers by which executive leaders can, through various interfaces, including executive digital twins, dashboards, and similar systems, obtain timely information (often in real-time or near real-time) that is curated to invoke relevant awareness, support effective decisions and enable operational control. The present disclosure further relates to an executive control tower and enterprise management platform that is configured to provide and use a converged technology stack that includes intelligent sensing and data collection, curation and handling of data through various stages of a distributed storage, networking and connectivity pipeline (from a set of local operational environments through information technology networks to various distributed onpremises and cloud computing environments), and deployment of various application-specific and general artificial intelligence capabilities in order to enable executive control towers, including role-specific executive digital twins, that are used by executives in management of the industrial plant operations of an enterprise.

[0012] In embodiments of the present disclosure, a method is provided for configuring rolebased digital twins, comprising: receiving, by a processing system having one or more processors, an organizational definition of an industrial plant operation, wherein the organizational definition defines a set of roles within the industrial plant operation; generating, by the processing system, an organizational digital twin of the industrial plant operation based on the organizational definition, wherein the organizational digital twin is a digital representation of an organizational structure of the industrial plant operation; determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational definition; determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships; linking an identity of a respective individual to the role; determining, by the processing system, a configuration of a presentation layer of a role-based digital twin corresponding to the role based on the settings of the role that is linked to the identity. The configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role. In embodiments, the method further includes determining, by the processing system, a set of data sources that provide data corresponding to the set of states. Each data source provides one or more respective types of data; and configuring one or more data structures that is received from the one or more data sources. The one or more data structures are configured to provide data used to populate one or more of the set of states in the role-based digital twin.

[0013] In embodiments, an organizational definition may further identify a set of physical assets of the industrial plant operation. In embodiments, determining a set of relationships may include parsing the organizational definition to identify a reporting structure and one or more business units of the industrial plant operation. In embodiments, a set of relationships may be inferred from a reporting structure and a business unit. In embodiments, a set of identities may be linked to a set of roles, wherein each identity corresponds to a respective role from the set of roles. In embodiments, a role-based digital twin may integrate with an industrial plant operation resource planning system that operates on the organizational digital twin that represents a set of roles in the industrial plant operation, such that changes in an industrial plant operation resource planning system are automatically reflected in the organizational digital twin.

[0014] In embodiments, an organizational structure may include hierarchical components, which may be embodied in a graph data structure. In embodiments, a set of settings for the set of roles may include role-based permission settings.

[0015] In embodiments, a role-based permission setting may be based on hierarchical components defined in the organizational definition.

[0016] In embodiments, a set of settings for a set of roles may include role-based preference settings. In embodiments, a role-based preference setting may be configured based on a set of role-specific templates. In embodiments, a set of templates may include at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data officer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, and a business development template.

[0017] In embodiments, a set of settings for the set of roles may include role-based taxonomy settings. In embodiments, a taxonomy setting may identify a taxonomy that is used to characterize data that is presented in a role-based digital twin, such that the data is presented in a taxonomy that is linked to the role corresponding to the role-based digital twin.

[0018] In embodiments, a set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy.

[0019] In embodiments, at least one role of the set of roles may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, a human resources manager role, an investor role, an engineering manager role, an accountant role, an auditor role, a resource planning role, a public relations manager role, a project manager role, an operations manager role, a research and development role, an engineer role, including but not limited to mechanical engineer, electrical engineer, semiconductor engineer, chemical engineer, computer science engineer, data science engineer, network engineer, or some other type of engineer, and a business development role.

[0020] In embodiments, at least one role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0021] In embodiments, a method for configuring role-based digital twins includes receiving, by a processing system having one or more processors, an organizational definition of an industrial plant operation. The organizational definition defines a set of roles within the industrial plant operation. The method further includes generating, by the processing system, an organizational digital twin of the industrial plant operation based on the organizational definition. The organizational digital twin is a digital representation of an organizational structure of the industrial plant operation. The method further includes determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational definition. The method further includes determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships; linking an identity of a respective individual to the role; determining, by the processing system, a configuration of a presentation layer of a role-based digital twin corresponding to the role based on the settings of the role that is linked to the identity. The configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role. The method further includes determining, by the processing system, a set of data sources that provide data corresponding to the set of states. Each data source provides one or more respective types of data; and configuring one or more data structures that is received from the one or more data sources. The one or more data structures are configured to provide data used to populate one or more of the set of states in the role-based digital twin.

[0022] In embodiments, the organizational definition further identifies a set of physical assets of the industrial plant operation. In embodiments, determining the set of relationships includes parsing the organizational definition to identify a reporting structure and one or more business units of the industrial plant operation. In embodiments, the set of relationships are inferred from the reporting structure and the business units. In embodiments, the method further includes linking a set of identities to the set of roles. Each identity corresponds to a respective role from the set of roles.

[0023] In embodiments, the role-based digital twin integrates with an industrial plant operation resource planning system that operates on the organizational digital twin that represents the set of roles in the industrial plant operation, such that changes in the industrial plant operation resource planning system are automatically reflected in the organizational digital twin. In embodiments, the organizational structure includes hierarchical components. In embodiments, the method further includes the hierarchical components are embodied in a graph data structure.

[0024] In embodiments, the set of settings for the set of roles includes role-based permission settings. In embodiments, the method further includes the role-based permission settings are based on hierarchical components defined in the organizational definition. In embodiments, the set of settings for the set of roles includes role-based preference settings. In embodiments, the role-based preference settings are configured based on a set of role-specific templates. In embodiments, the set of templates includes at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data officer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, or a business development template.

[0025] In embodiments, the set of settings for the set of roles includes role-based taxonomy settings.

[0026] In embodiments, the taxonomy settings identify a taxonomy that is used to characterize data that is presented in the role-based digital twin, such that the data is presented in ataxonomy that is linked to the role corresponding to the role-based digital twin. In embodiments, the set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, or a business development taxonomy.

[0027] In embodiments, at least one role of the set of roles is selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, a human resources manager role, an investor role, an engineering manager role, an accountant role, an auditor role, a resource planning role, a public relations manager role, a project manager role, an operations manager role, a research and development role, an engineer role, including but not limited to mechanical engineer, electrical engineer, semiconductor engineer, chemical engineer, computer science engineer, data science engineer, network engineer, or some other type of engineer, and a business development role.

[0028] In embodiments, the at least one role is selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role. In embodiments, a system for data collection in an industrial environment includes an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components; a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group; a pattern recognition circuit structured to determine a recognized pattern value in response to at least a portion of the plurality of sensor data values; a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value. The sensor communication circuit is further structured to adjust the interpreting of the plurality of sensor data values in response to the updated sensed parameter group. The sensor learning circuit is further structured to update the sensed parameter group by updating a network path configuration corresponding to at least one sensor from the sensed parameter group; and a reporting circuit. The sensed parameter group is associated with a rolebased taxonomy setting, and the role-based taxonomy setting is further associated with at least one reporting rule for reporting the update to the sensed parameter group to at least one role taxonomy including among the role-based taxonomy setting.

[0029] In embodiments, the sensed parameter group comprises a fused plurality of sensors. The recognized pattern value further includes a secondary value comprising a value determined in response to the fused plurality of sensors. In embodiments, the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value. In embodiments, the sensing performance value comprises at least one performance determination including: a signal-to-noise performance for detecting a value of interest in the industrial system; a network utilization of the plurality of sensors in the industrial system; an effective sensing resolution for a value of interest in the industrial system; or a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors. In embodiments, the sensing performance value comprises a signal-to-noise performance for detecting a value of interest in the industrial system.

[0030] In embodiments, the sensing performance value comprises a network utilization of the plurality of sensors in the industrial system. In embodiments, the sensing performance value comprises an effective sensing resolution for a value of interest in the industrial system. In embodiments, the sensing performance value comprises a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors. In embodiments, the sensing performance value comprises a calculation efficiency for determining the secondary value. In embodiments, the calculation efficiency comprises at least one of: processor operations to determine the secondary value, memory utilization for determining the secondary value, a number of sensor inputs from the plurality of sensors for determining the secondary value, and supporting data long-term storage for supporting the secondary value.

[0031] In embodiments, the sensing performance value comprises one of an accuracy or a precision of the secondary value. In embodiments, the sensing performance value comprises a redundancy capacity for determining the secondary value. In embodiments, the sensing performance value comprises a lead time value for determining the secondary value. In embodiments, the secondary value comprises a component overtemperature value. In embodiments, the secondary value comprises one of a component maintenance time, a component failure time, or a component service life. In embodiments, the secondary value comprises an off nominal operating condition affecting a product quality produced by an operation of the industrial system.

[0032] In embodiments, the plurality of sensors comprises at least one analog sensor. In embodiments, at least one of the sensors comprises a remote sensor. In embodiments, the secondary value comprises at least one of a virtual sensor output value; a process prediction value; a process state value; a component prediction value; a component state value; or a model output value having the sensor data values from the fused plurality of sensors as an input.

[0033] In embodiments, the fused plurality of sensors further comprises at least one of a vibration sensor and a temperature sensor; a vibration sensor and a pressure sensor; a vibration sensor and an electric field sensor; a vibration sensor and a heat flux sensor; a vibration sensor and a galvanic sensor; or a vibration sensor and a magnetic sensor.

[0034] In embodiments, the sensor learning circuit is further structured to update the sensed parameter group to form the updated sensed parameter group by performing at least one additional operation including updating a sensor selection of the sensed parameter group; updating a sensor sampling rate of at least one sensor from the sensed parameter group; updating a sensor resolution of at least one sensor from the sensed parameter group; updating a storage value corresponding to at least one sensor from the sensed parameter group; updating a priority corresponding to at least one sensor from the sensed parameter group; and updating at least one of a sampling rate, sampling order, or sampling phase.

[0035] In embodiments, the pattern recognition circuit is further structured to determine the recognized pattern value by at least one of determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest; determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; or updating the recognized pattern value in response to external feedback.

[0036] In embodiments, the value of interest comprises at least one value including a virtual sensor output value; a process prediction value; a process state value; a component prediction value; a component state value; or a model output value having the sensor data values from a fused plurality of sensors as an input.

[0037] In embodiments, the pattern recognition circuit is further structured to access cloudbased data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system.

[0038] In embodiments, the sensor learning circuit is further structured to access the cloudbased data comprising a second updated sensor parameter group corresponding to the at least one offset industrial system.

[0039] In embodiments, the at least one role taxonomy is at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, or a business development taxonomy.

[0040] In embodiments, a system for data collection in an industrial environment having a selfsufficient data acquisition box for capturing and analyzing data in an industrial process includes a data circuit for analyzing a plurality of sensor inputs; and a network control circuit for sending and receiving information related to the plurality of sensor inputs to an external system. The information sent and received by the network control system is based at least in part on a rolebased reporting rule and encoded with role-based information that is used by the external system to report the information sent and received to a specified role taxonomy. The system provides sensor data to one or more other systems for data collection. The data circuit dynamically reconfigures a route by which the system sends the sensor data based on how many devices are requesting the information.

[0041] In embodiments, the system further comprises a plurality of network communication interfaces.

[0042] In embodiments, the network control circuit bridges another system for data collection from one network to another using the plurality of network communication interfaces.

[0043] In embodiments, the network control circuit implements a network of systems for data collection using an intercommunication protocol selected from a list comprising: multi-hop, mesh, serial, parallel, ring, real-time and hub-and-spoke.

[0044] In embodiments, the system continuously provides a single copy of its information to another system for data collection and directs requesters of its information to the another system for data collection.

[0045] In embodiments, at least one of the one or more other systems for data collection has different operational characteristics than the system. In embodiments, the different operational characteristics are selected from a list comprising: power, storage, network connectivity, proximity, reliability, duty cycle. In embodiments, after a configurable time period, the system stores only digests of the information and discards underlying information.

[0046] In embodiments, the data circuit dynamically nominates one of the other systems for data collection capable of providing the sensor data to replace the system. In embodiments, the nomination is triggered by a detection of a system failure mode.

[0047] In embodiments, the network control circuit arranges the system into a redundant storage network.

[0048] In embodiments, the system accumulates data received from the other systems for data collection while an upstream network connection is unavailable, and then sends all accumulated data once the upstream network connection is restored.

[0049] In embodiments, the system further comprises an intelligent agent circuit that combines the sensor data between the system and the other systems for data collection. In embodiments, the intelligent agent circuit chooses what sensor data to collect or store based on a machine learning algorithm.

[0050] In embodiments, the analyzing further comprises detecting anomalies in the information.

[0051] In embodiments, the system dynamically reconfigures where it sends data and what quantity of data it sends based on an availability of the other systems for data collection. In embodiments, the system further comprises a plurality of network communication interfaces. In embodiments, the system dynamically reconfigures where it sends data and what quantity of data it sends based on requests received over one or more of the plurality of network communication interfaces.

[0052] In embodiments, the system further includes a data filter circuit configured to dynamically adjust what portion of the information is sent based on instructions received over one or more of the plurality of network communication interfaces. In embodiments, the system dynamically reconfigures where it sends data and what quantity of data it sends based on an operating parameter of at least one of the system or one of the other systems for data collection.

[0053] In embodiments, the specified role type is at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, or a business development taxonomy.

[0054] A method for data collection in an industrial environment including receiving, at a switch, analog data from a plurality of variable groups of analog sensor inputs; monitoring the analog data from the plurality of variable groups of analog sensor inputs; adaptively scheduling data collection of the switch; determining a noise value including at least one of ambient noise, local noise, or vibration noise; using machine learning to forecast a mechanical state of a machine based at least in part on the determined noise value; and reporting the forecasted mechanical state of the machine to an entity associated with a role type stored within a role taxonomy.

[0055] In embodiments, the method further includes performing internet protocol (IP) frontend end signal conditioning to improve a signal-to-noise ratio of at least one of the analog sensor inputs. In embodiments, the method further includes predicting a state of at least one of a component or a process of the industrial environment in response to the determined noise value. In embodiments, the method further includes utilizing a transfer function to determine a relative phase of a first one of the analog sensor inputs to a second one of the analog sensor inputs. In embodiments, the role taxonomy is at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy.

[0056] In embodiments, a data collection and processing system includes variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment; a switch comprising multiple sensor channels configured to receive long blocks of high-sampling rate data from one or more of the variable groups of sensor inputs; and a data management feature that includes multiple routes communicatively coupling the variable groups of sensor inputs to the multiple sensor channels. The switch is configured to use hierarchical templates to perform one of creating or adjusting one of the multiple routes. The data management feature includes multiple reporting routes for distributing data from the multiple sensor channels to at least one entity associated with a role type stored within a role taxonomy.

[0057] In embodiments, the system further includes a controller configured to store calibration data and a maintenance history of one or more sensors associated with the variable groups of sensor inputs. In embodiments, the switch is further configured to provide a continuously monitored alarm in response to one of more inputs from the variable groups of sensor inputs. In embodiments, the switch is configured to perform internet protocol (IP) front-end end signal conditioning to improve a signal-to-noise ratio of one or more of the sensor channels.

[0058] In embodiments, a data collection and processing system including variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment; a switch comprising multiple sensor channels configured to receive long blocks of high-sampling rate data from one or more of the variable groups of sensor inputs; and a data management feature that includes a controller configured to perform intelligent management of data collection frequency bands obtained by one or more of the variable groups of sensor inputs. The intelligent management of data collection is based at least in part on a data collection rule relating to a data requirement of at least one entity associated with a role type stored within a role taxonomy.

[0059] In embodiments, the controller is configured to store calibration data and a maintenance history of one or more sensors associated with the variable groups of sensor inputs.

[0060] In embodiments, the switch is further configured to provide a continuously monitored alarm in response to one or more inputs from the variable groups of sensor inputs. In embodiments, the switch is configured to perform internet protocol (IP) front-end end signal conditioning to improve a signal-to-noise ratio of one or more of the sensor channels. In embodiments, a data collection and processing system including variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment; a switch comprising multiple sensor channels configured to receive long blocks of high-sampling rate data from one or more of the variable groups of sensor inputs; and a data management feature that includes a controller comprising a neural net expert system. The neural net expert system is configured to perform intelligent management of data collection frequency bands obtained by at least one of the variable groups of sensor inputs. The neural net expert system is configured to manage data based at least in part on a rule associated with a role type stored within a role taxonomy.

[0061] In embodiments, the controller is configured to store calibration data and a maintenance history of one or more sensors associated with the variable groups of sensor inputs. In embodiments, the switch is further configured to provide a continuously monitored alarm in response to one or more inputs from the variable groups of sensor inputs. In embodiments, the system further includes at least one additional switch. Each of the switches comprises a corresponding complex programmable logic device (CPLD), each CPLD configured to control a corresponding switch.

[0062] In embodiments, a data collection and processing system including variable groups of sensor inputs operationally coupled to an industrial environment; a switch communicatively coupled to the variable groups of sensor inputs; and a controller configured to continuously monitor data from multiple variable groups of sensor inputs. Continuously monitoring comprises adaptively scheduling data collection of the switch. The system further includes reporting a future state of the industrial environment based on a rule associated with a role type stored within a role taxonomy.

[0063] In embodiments, the controller is further configured to determine a noise value from one or more of an ambient noise, a local noise, or a vibration noise. The controller is further configured to predict a state of at least one of a component or a process of the industrial environment in response to the determined noise value. In embodiments, the controller is further configured to use smart route changes based on at least one of incoming data or alarms, and to provide simultaneous dynamic data from the multiple variable groups of sensor inputs in response to the one of incoming data or alarms.

[0064] In embodiments, the controller is further configured to perform machine pattern recognition based on a fusion of a selected plurality of the variable groups of sensor inputs. In embodiments, the controller is further configured to utilize a transfer function to determine a relative phase of a first one of the sensor inputs to a second one of the sensor inputs. In embodiments, the controller is further configured to perform machine pattern analysis based on a selected plurality of the variable groups of sensor inputs. The machine pattern analysis comprises anticipated state information for the industrial environment.

[0065] In embodiments, the system further includes a cloud-based policy automation engine comprising at least one of: a sensor selection of the variable groups of sensor inputs; a sensor deployment of variable groups of sensor inputs; a sensor fusion of the variable groups of sensor inputs; or a data storage profile of the variable groups of sensor inputs. The controller is further configured to selectively communicate at least a portion of the data of the multiple variable groups of sensor inputs to a cloud-based storage. The communication to the cloud-based storage is organized by a quantitative weighting of relevance of the data of the multiple variable groups of sensor to at least one role type stored within a role taxonomy.

[0066] In embodiments, the controller is further configured to implement a self-organizing data marketplace, the self-organizing data marketplace including at least a portion of the data from the variable groups of sensor inputs. In embodiments, the controller is further configured to implement a self-organizing data pool based on at least one of: sensor utilization, data utilization, or yield metrics. In embodiments, the controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment.

[0067] In embodiments, a method for data collection in an industrial environment including receiving, at a switch, data from one or more variable groups of sensor inputs; monitoring the data from the one or more variable groups of sensor inputs; adaptively scheduling data collection at the switch; determining one or more noise values including one of an ambient noise, a local noise, or a vibration noise; using machine learning to forecast a future state of the industrial environment based at least in part on the determined one or more noise values; and reporting the forecasted future state of the industrial environment to an entity associated with a role type stored within a role taxonomy.

[0068] In embodiments, the method further includes performing internet protocol (IP) frontend end signal conditioning to improve a signal-to-noise ratio of the one or more variable groups of sensor inputs. In embodiments, the method further includes predicting a state of at least one of a component or a process of the industrial environment in response to one or more of the determined noise values. In embodiments, the method further includes utilizing a transfer function to determine a relative phase between a first sensor input from one of the variable groups of sensor inputs and a second sensor input from one of the variable groups of sensor inputs.

[0069] In embodiments, the method includes by a sensor communication circuit, interpreting data from a plurality of input sensors. Each of the plurality of input sensors is operationally coupled to a component of an industrial environment; by a controller, operating a selforganizing network on the data from the plurality of input sensors, thereby determining a structure in the data; by the controller, determining a relevance of the determined structure in the data to at least one role type stored within a role taxonomy; by the controller, determining a reduced dimensionality view of the data in response to the determined structure in the data. The reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors. The reduced dimensionality view further comprises a graphical element representing at least one of: mechanical portions of a machine of the industrial environment, or a sensor from the plurality of input sensors that provided data; and providing the reduced dimensionality view to a user interface that is associated with at least one entity associated with the at least one role type stored within the role taxonomy.

[0070] In embodiments, the method further includes determining that a condition of interest has occurred within the industrial environment. Operating the self-organizing network on the data from the plurality of input sensors further comprises applying back calculation to the condition of interest to determine a failed part related to the condition of interest. The reduced dimensionality view comprises a graphical representation of the failed part related to the condition of interest.

[0071] In embodiments, the graphical representation of the failed part related to the condition of interest further comprises a map of highlighted data collection routes that reflect paths of data from at least one of the plurality of input sensors that contributes to calculating the condition of interest.

[0072] In embodiments, the method further includes determining that a condition of interest has occurred within the industrial environment. Operating the self-organizing network on the data from the plurality of input sensors further comprises applying back calculation to the condition of interest to determine a data collection system configuration template associated with the condition of interest. The reduced dimensionality view comprises a graphical representation of the data collection system configuration template associated with the condition of interest.

[0073] In embodiments, the graphical representation of a failed part related to the condition of interest further comprises a map of data collection configured by the data collection system configuration template, with highlighted routes in a data collection system that reflect paths of data from at least one sensor to at least one data collector for data that contributes to calculating the condition of interest.

[0074] In embodiments, the reduced dimensionality view comprises a heat map.

[0075] In embodiments, operating the self-organizing network comprises applying competitive learning to the data from the plurality of input sensors to determine the structure in the data. In embodiments, determining the reduced dimensionality view comprises at least one operation including determining an unlabeled data value of the data from the plurality of input sensors; the method further comprising determining that the data from the plurality of input sensors comprises vibration data from an unknown source within the industrial environment; or providing a frequency value corresponding to an unlabeled data value of the data from the plurality of input sensors.

[0076] In embodiments, determining the reduced dimensionality view comprises determining an unlabeled data value of the data from the plurality of input sensors, the method further comprising accepting a user input on the user interface, and labeling the unlabeled data value in response to a user input on the user interface.

[0077] In embodiments, a system includes a sensor communication circuit structured to interpret a plurality of sensor data values from a plurality of input sensors. Each of the plurality of input sensors is operationally coupled to a component of an industrial environment. The system further includes a controller configured to: operate a self-organizing network on the data from the plurality of input sensors, thereby determining a structure in the data; and determine a reduced dimensionality view of the data in response to the determined structure in the data and in response to a relevance of the determined structure in the data to at least one role type stored within a role taxonomy. The reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors. The reduced dimensionality view further comprises a plurality of graphical elements representing mechanical portions of a machine of the industrial environment; and a user interface configured to display the reduced dimensionality view.

[0078] In embodiments, the plurality of graphical elements further comprises mechanical portions of the machine of the industrial environment that are associated with a condition of interest.

[0079] In embodiments, the reduced dimensionality view further comprises a second plurality of graphical elements representing data collectors in the industrial environment associated with the plurality of input sensors. In embodiments, the reduced dimensionality view further comprises a plurality of highlighted graphical elements representing sensors from the plurality of input sensors that provided data outside an acceptable range of data. In embodiments, the user interface is further configured to provide a list of conditions of interest to a user, and to select the condition of interest in response to a user selection from the list of conditions. In embodiments, the controller is further configured to provide a data collection system configuration template in response to the user selection from the list of conditions. The data collection system configuration template facilitates configuring a data collection system to collect data for calculating the condition of interest.

[0080] In embodiments, the mechanical portions comprise at least one of a bearing, a shaft, a rotor, or a housing of the machine of the industrial environment.

[0081] In embodiments, an apparatus includes a user interface configured to provide a list of conditions of interest to at least one entity associated with a role type stored within a role taxonomy, and to select a condition of interest in response to a user selection from the list of conditions; and a controller configured to: operate a self-organizing network on data from a plurality of input sensors, the plurality of input sensors operationally coupled to a component of an industrial environment; and determine a reduced dimensionality view of the data in response to a determined structure in the data and further in response to the selected condition of interest. The reduced dimensionality view a plurality of graphical elements representing mechanical portions of a machine of the industrial environment associated with the condition of interest. The reduced dimensionality view further comprises a plurality of highlighted graphical elements representing sensors from the plurality of input sensors that provided data outside an acceptable range of data. The user interface is further configured to display the reduced dimensionality view.

[0082] In embodiments, the controller is further configured to provide a data collection system configuration template in response to the user selection from the list of conditions. The data collection system configuration template facilitates configuring a data collection system to collect data for calculating the condition of interest.

[0083] In embodiments, a system for data collection in an industrial environment includes a plurality of wearable haptic stimulators that produce stimuli; a plurality of sensors operationally coupled to a machine of the industrial environment; a processor logically disposed between the plurality of sensors and the wearable haptic stimulators, the processor configured to: receive data from the plurality of sensors representative of a sensed condition of the machine; determine a relevance of the sensed condition of the machine to at least one role type stored within a role taxonomy; determine at least one haptic stimulation that corresponds to the received data; and send at least one signal in response to the at least one haptic stimulation. At least one of the plurality of wearable haptic stimulators is responsive to the at least one signal. The at least one haptic stimulation represents an effect on the machine in the industrial environment resulting from the sensed condition. An effect on the machine in the industrial environment is presented as an effect on at least one of the plurality of wearable haptic stimulators and reported to at least one entity associated with the at least one role type stored within the role taxonomy.

[0084] In embodiments, the effect from at least one haptic stimulation at least one of a list of stimuli including tactile, vibration, heat, sound, force, odor, or motion. In embodiments, a vibrating effect on the machine in the industrial environment is presented as vibrating at least one of the plurality of wearable haptic stimulators. In embodiments, a heating effect on the machine in the industrial environment is presented as an increase in temperature of at least one of the plurality of wearable haptic stimulators. In embodiments, an electrical effect on the machine in the industrial environment is presented as a change in sound produced by at least one of the plurality of wearable haptic stimulators.

[0085] In embodiments, at least one of the plurality of wearable haptic stimulators including at least one of a glove, a ring, a wrist band, a wristwatch, an arm band, head gear, a belt, a necklace, a shirt, footwear, pants, overalls, coveralls, or safety goggles. In embodiments, the at least one signal comprises an alert of a condition of interest in the industrial environment. In embodiments, the at least one haptic stimulation produced in response to the alert of a condition of interest is repeated by at least one of the plurality of wearable haptic stimulators until an acceptable response is detected.

[0086] In embodiments, a monitoring system for collecting data related to an industrial environment including a data collector communicatively coupled to a plurality of input channels. The plurality of input channels comprises data relating to an aspect of an industrial production process and data relating to at least one role type stored within a role taxonomy; a data storage structured to store a plurality of detection values that corresponds to the plurality of input channels; a data analysis circuit structured to interpret at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state; an optimization circuit structured to analyze a subset of the plurality of detection values and the state value using at least one of a neural net or an expert system to determine a signal effectiveness of at least one of the plurality of input channels relative to the state value, and to provide an adjustment recommendation based, at least in part, on the signal effectiveness; and an analysis response circuit structured to adjust the industrial production process in response to the adjustment recommendation. In embodiments, the optimization circuit is further structured to provide the adjustment recommendation as one of an equipment change for a component of the industrial production process, or an equipment operating parameter change for the component of the industrial production process.

[0087] In embodiments, the optimization circuit is further structured to provide the adjustment recommendation as a process parameter change for the industrial production process. In embodiments, the process parameter change comprises a command to rebalance process loads between components of the industrial production process. In embodiments, the optimization circuit is further structured to provide the process parameter change to achieve at least one of: extending a life of one of the components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the components of the industrial production process.

[0088] In embodiments, the optimization circuit is further structured to provide the process parameter change to facilitate maintenance of one of the components of the industrial production process.

[0089] In embodiments, the optimization circuit is further structured to facilitate the maintenance of one of the components by performing at least one operation including extending a maintenance interval of one of the components; synchronizing a first maintenance interval of a first one of the components with a second maintenance interval of a second one of the components; and differentiating the first maintenance interval of the first one of the components from the second maintenance interval of the second one of the components. In embodiments, the optimization circuit is further structured to facilitate the maintenance of one of the components by aligning a maintenance interval of one of the components with an external reference time. In embodiments, the external reference time comprises at least one time including a planned shutdown time for the industrial production process, a time that is past an expected completion time of the industrial production process, or a scheduled maintenance time for one of the components. In embodiments, the optimization circuit is further structured to determine a sensitivity of at least one of the plurality of input channels relative to the state value.

[0090] In embodiments, the optimization circuit is further structured to determine a predictive accuracy of at least one of the plurality of input channels relative to the state value. In embodiments, a computer-implemented method for collecting data related to an industrial environment including collecting data, using a data collector communicatively coupled to a plurality of input channels. The plurality of input channels comprises data relating to an aspect of an industrial production process; storing a plurality of detection values that corresponds to the plurality of input channels; interpreting at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state; analyzing a subset of the plurality of detection values and the state value, using at least one of a neural net or an expert system, and providing an adjustment recommendation for the industrial production process, the adjustment recommendation, at least in part, in response to a sensitivity of at least one of the plurality of input channels relative to the state value; adjusting the industrial production process in response to the adjustment recommendation; determining a relevance of the adjustment recommendation to at least one role type stored within a role taxonomy; and reporting the adjustment to the industrial production process to at least one entity associated with the role type stored within the role taxonomy.

[0091] In embodiments, the method further includes providing the adjustment recommendation in response to a signal effectiveness of at least one of the plurality of input channels relative to the state value. In embodiments, the method further includes providing the adjustment recommendation in response to a predictive confidence of at least one of the plurality of input channels relative to the state value. In embodiments, the method further includes providing the adjustment recommendation in response to a predictive accuracy of at least one of the plurality of input channels relative to the state value. In embodiments, adjusting the industrial production process comprises rebalancing process loads between components of the industrial production process to achieve at least one of: extending a life of one of a plurality of components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the plurality of components of the industrial production process. In embodiments, adjusting the industrial production process comprises facilitating maintenance on a component of the industrial production process to achieve at least one of extending a maintenance interval of the component; synchronizing a first maintenance interval of the component with a second maintenance interval of a second component of the industrial production process; and differentiating the first maintenance interval of the component from the second maintenance interval of the second component of the industrial production process.

[0092] In embodiments, adjusting the industrial production process comprises facilitating maintenance on a component of the industrial production process to align a maintenance interval of a component of the industrial production process with an external reference time.

[0093] In embodiments, the external reference time comprises at least one time including a planned shutdown time for the industrial production process, a time that is past an expected completion time of the industrial production process, or a scheduled maintenance time for a second component of the industrial production process. In embodiments, an apparatus for collecting data related to an industrial environment including a data collector component, communicatively coupled to a plurality of input channels. The plurality of input channels comprises data from and data about an element of an industrial production process, the element comprising at least one of: a machine, a component, a system, a sub-system, an ambient condition, a state, a workflow, or a process; a data storage component configured to store a plurality of detection values that corresponds to the plurality of input channels; a data analysis component configured to interpret at least a subset of the plurality of detection values to determine a state value. The state value comprises at least one of: a sensor state, a process state, or a component state; an optimization component configured to analyze a subset of the plurality of detection values, and the state value using at least one of a neural net or an expert system, and to determine a predictive accuracy of at least one of the plurality of input channels relative to the state value, and to provide an adjustment recommendation based, at least in part, on the predictive accuracy; an analysis response component configured to adjust the industrial production process in response to the adjustment recommendation; a relevance calculation component to determine a relevance of the adjustment recommendation to at least one role type stored within a role taxonomy; and a reporting component to report the adjustment to the industrial production process to at least one entity associated with the role type stored within the role taxonomy.

[0094] In embodiments, adjusting the industrial production process comprises rebalancing process loads between components to achieve at least one of: extending a life of one of a plurality of components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the plurality of components of the industrial production process.

[0095] In embodiments, the optimization component is further configured to provide the adjustment recommendation as a process parameter change for the industrial production process. In embodiments, the process parameter change comprises a command to rebalance process loads between components of the industrial production process. In embodiments, the optimization component is further configured to provide the process parameter change to achieve at least one of extending a life of one of the components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the components of the industrial production process. In embodiments, the optimization component is further configured to facilitate the maintenance of one of the components by performing at least one operation including extending a maintenance interval of one of the components; synchronizing a first maintenance interval of a first one of the components with a second maintenance interval of a second one of the components; differentiating the first maintenance interval of the first one of the components from the second maintenance interval of the second one of the components; and aligning a maintenance interval of one of the components with an external reference time.

[0096] In embodiments, the external reference time comprises at least one time including a planned shutdown time for the industrial production process, a time that is past an expected completion time of the industrial production process, or a scheduled maintenance time for one of the components.

[0097] A more complete understanding of the disclosure will be appreciated from the description and accompanying drawings and the claims, which follow. Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the disclosure. BRIEF DESCRIPTION OF THE FIGURES

[0098] Figures 1 through Figure 5 are diagrammatic views that each depicts portions of an overall view of an industrial Internet of Things (loT) data collection, monitoring and control system in accordance with the present disclosure.

[0099] Figure 6 is a diagrammatic view of a platform including a local data collection system disposed in an industrial environment for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements in accordance with the present disclosure.

[0100] Figure 7 is a diagrammatic view that depicts elements of an industrial data collection system for collecting analog sensor data in an industrial environment in accordance with the present disclosure.

[0101] Figure 8 is a diagrammatic view of a rotating or oscillating machine having a data acquisition module that is configured to collect waveform data in accordance with the present disclosure.

[0102] Figure 9 is a diagrammatic view of an exemplary tri-axial sensor mounted to a motor bearing of an exemplary rotating machine in accordance with the present disclosure.

[0103] Figure 10 is a diagrammatic view of components and interactions of a data collection architecture involving application of cognitive and machine learning systems to data collection and processing in accordance with the present disclosure.

[0104] Figure 11 is a diagrammatic view of components and interactions of a data collection architecture involving application of a platform having a cognitive data marketplace in accordance with the present disclosure.

[0105] Figure 12 is a diagrammatic view of components and interactions of a data collection architecture involving application of a self-organizing swarm of data collectors in accordance with the present disclosure.

[0106] Figure 13 is a diagrammatic view of components and interactions of a data collection architecture involving application of a haptic user interface in accordance with the present disclosure.

[0107] Figure 14 is a diagrammatic view of a multi-format streaming data collection system in accordance with the present disclosure.

[0108] Figure 15 is a diagrammatic view of combining legacy and streaming data collection and storage in accordance with the present disclosure.

[0109] Figure 16 is a diagrammatic view of industrial machine sensing using both legacy and updated streamed sensor data processing in accordance with the present disclosure.

[0110] Figure 17 is a diagrammatic view of an industrial machine sensed data processing system that facilitates portal algorithm use and alignment of legacy and streamed sensor data in accordance with the present disclosure.

[0111] Figure 18 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0112] Figure 19 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument having an alarms module, expert analysis module, and a driver API to facilitate communication with a cloud network facility in accordance with the present disclosure.

[0113] Figure 20 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument and first in, first out memory architecture to provide a real time operating system in accordance with the present disclosure.

[0114] Figure 21 is a diagrammatic view of components and interactions of a data collection architecture involving a multiple streaming data acquisition instrument receiving analog sensor signals and digitizing those signals to be obtained by a streaming hub server in accordance with the present disclosure.

[0115] Figure 22 is a diagrammatic view of components and interactions of a data collection architecture involving a master raw data server that processes new streaming data and data already extracted and processed in accordance with the present disclosure.

[0116] Figure 23, Figure 24, and Figure 25 are diagrammatic views of components and interactions of a data collection architecture involving a processing, analysis, report, and archiving server that processes new streaming data and data already extracted and processed in accordance with the present disclosure.

[0117] Figure 26 is a diagrammatic view of components and interactions of a data collection architecture involving a relation database server and data archives and their connectivity with a cloud network facility in accordance with the present disclosure.

[0118] Figure 27 through Figure 32 are diagrammatic views of components and interactions of a data collection architecture involving a virtual streaming data acquisition instrument receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0119] Figure 33 through Figure 40 are diagrammatic views of components and interactions of a data collection architecture involving data channel methods and systems for data collection of industrial machines in accordance with the present disclosure.

[0120] Figure 41 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0121] Figure 42 and Figure 43 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0122] Figure 44 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0123] Figures 45 and 46 are diagrammatic views that depict an embodiment of a system for data collection in accordance with the present disclosure.

[0124] Figures 47 and 48 are diagrammatic views that depict an embodiment of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0125] Figure 49 depicts an embodiment of a data monitoring device incorporating sensors in accordance with the present disclosure.

[0126] Figures 50 and 51 are diagrammatic views that depict embodiments of a data monitoring device in communication with external sensors in accordance with the present disclosure.

[0127] Figure 52 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0128] Figure 53 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0129] Figure 54 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0130] Figure 55 is a diagrammatic view that depicts embodiments of a system for data collection in accordance with the present disclosure.

[0131] Figure 56 is a diagrammatic view that depicts embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0132] Figure 57 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0133] Figures 58 and 59 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0134] Figures 60 and 61 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0135] Figures 62 and 63 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0136] Figures 64 and 65 is a diagrammatic view that depicts embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0137] Figure 66 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0138] Figures 67 and 68 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0139] Figure 69 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0140] Figure 70 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0141] Figures 71 and 72 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0142] Figures 73 and 74 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0143] Figure 75 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0144] Figures 76 and 77 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0145] Figure 78 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0146] Figures 79 and 80 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0147] Figures 81 and 82 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0148] Figure 83 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0149] Figures 84 and 85 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0150] Figure 86 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0151] Figures 87 and 88 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0152] Figures 89 and 90 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0153] Figure 91 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0154] Figures 92 and 93 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0155] Figure 94 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0156] Figures 95 and 96 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0157] Figures 97 and 98 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0158] Figures 99 through Figure 101 are diagrammatic views of components and interactions of a data collection architecture involving a collector of route templates and the routing of data collectors in an industrial environment in accordance with the present disclosure.

[0159] Figure 102 is a diagrammatic view that depicts a monitoring system that employs data collection bands in accordance with the present disclosure.

[0462] Figure 103 is a diagrammatic view that depicts a system that employs vibration and other noise in predicting states and outcomes in accordance with the present disclosure.

[0463] Figure 104 is a diagrammatic view that depicts a system for data collection in an industrial environment in accordance with the present disclosure.

[0464] Figure 105 is a diagrammatic view that depicts an apparatus for data collection in an industrial environment in accordance with the present disclosure.

[0465] Figure 106 is a schematic flow diagram of a procedure for data collection in an industrial environment in accordance with the present disclosure.

[0466] Figure 107 is a diagrammatic view that depicts a system for data collection in an industrial environment in accordance with the present disclosure.

[0467] Figure 108 is a diagrammatic view that depicts an apparatus for data collection in an industrial environment in accordance with the present disclosure.

[0468] Figure 109 is a schematic flow diagram of a procedure for data collection in an industrial environment in accordance with the present disclosure.

[0469] Figure 110 is a diagrammatic view that depicts industry-specific feedback in an industrial environment in accordance with the present disclosure.

[0470] Figure 111 is a diagrammatic view that depicts an exemplary user interface for smart band configuration of a system for data collection in an industrial environment is depicted in accordance with the present disclosure.

[0471] Figure 112 is a diagrammatic view that depicts a graphical approach 11300 for back-calculation in accordance with the present disclosure.

[0472] Figure 113 is a diagrammatic view that depicts a wearable haptic user interface device for providing haptic stimuli to a user that is responsive to data collected in an industrial environment by a system adapted to collect data in the industrial environment in accordance with the present disclosure.

[0473] Figure 114 is a diagrammatic view that depicts an augmented reality display of heat maps based on data collected in an industrial environment by a system adapted to collect data in the environment in accordance with the present disclosure.

[0474] Figure 115 is a diagrammatic view that depicts an augmented reality display including real time data overlaying a view of an industrial environment in accordance with the present disclosure.

[0475] Figure 116 is a diagrammatic view that depicts a user interface display and components of a neural net in a graphical user interface in accordance with the present disclosure.

[0476] Figure 117 is a diagrammatic view of components and interactions of a data collection architecture involving swarming data collectors and sensor mesh protocol in an industrial environment in accordance with the present disclosure.

[0477] Figure 118 is a diagrammatic view that depicts data collection system according to some aspects of the present disclosure.

[0478] Figure 119 is a diagrammatic view that depicts a system for self-organized, networksensitive data collection in an industrial environment in accordance with the present disclosure.

[0479] Figure 120 is a diagrammatic view that depicts an apparatus for self-organized, network-sensitive data collection in an industrial environment in accordance with the present disclosure.

[0480] Figure 121 is a diagrammatic view that depicts an apparatus for self-organized, network-sensitive data collection in an industrial environment in accordance with the present disclosure.

[0481] Figure 122 is a diagrammatic view that depicts an apparatus for self-organized, network-sensitive data collection in an industrial environment in accordance with the present disclosure.

[0482] Figure 123 and Figure 124 are diagrammatic views that depict embodiments of transmission conditions in accordance with the present disclosure.

[0483] Figure 125 is a diagrammatic view that depicts embodiments of a sensor data transmission protocol in accordance with the present disclosure.

[0484] Figure 126 and Figure 127 are diagrammatic views that depict embodiments of benchmarking data in accordance with the present disclosure.

[0485] Figure 128 is a diagrammatic view that depicts embodiments of a system for data collection and storage in an industrial environment in accordance with the present disclosure.

[0486] Figure 129 is a diagrammatic view that depicts embodiments of an apparatus for selforganizing storage for data collection for an industrial system in accordance with the present disclosure.

[0487] Figure 130 is a diagrammatic view that depicts embodiments of a storage time definition in accordance with the present disclosure.

[0488] Figure 131 is a diagrammatic view that depicts embodiments of a data resolution description in accordance with the present disclosure.

[0489] Figure 132 and Figure 133 diagrammatic views of an apparatus for self-organizing network coding for data collection for an industrial system in accordance with the present disclosure.

[0490] Figure 134 and Figure 135 diagrammatic views of data marketplace interacting with data collection in an industrial system in accordance with the present disclosure.

[0491] Figure 136 is a diagrammatic view that depicts a smart heating system as an element in a network for in an industrial Internet of Things ecosystem in accordance with the present disclosure.

[0492] Figure 137 is a diagrammatic view that depicts an architecture, its components and functional relationships for an industrial Internet of Things solution in accordance with the present disclosure.

[0493] Figure 138 is a schematic illustrating an example of a sensor kit deployed in an industrial setting according to some embodiments of the present disclosure.

[0494] Figure 139 is a schematic illustrating an example of a sensor kit network having a star network topology according to some embodiments of the present disclosure.

[0495] Figure 140 is a schematic illustrating an example of a sensor kit network having a mesh network topology according to some embodiments of the present disclosure.

[0496] Figure 141 is a schematic illustrating an example of a sensor kit network having a hierarchical network topology according to some embodiments of the present disclosure.

[0497] Figure 142 is a schematic illustrating an example of a sensor according to some embodiments of the present disclosure.

[0498] Figure 143 is a schematic illustrating an example schema of a reporting packet according to some embodiments of the present disclosure.

[0499] Figure 144 is a schematic illustrating an example of an edge device of a sensor kit according to some embodiments of the present disclosure.

[0500] Figure 145 is a schematic illustrating an example of a backend system that receives sensor data from sensor kits deployed in industrial settings according to some embodiments of the present disclosure.

[0501] Figure 146 is a flow chart illustrating an example set of operations of a method for encoding sensor data captured by a sensor kit according to some embodiments of the present disclosure.

[0502] Figure 147 is a flow chart illustrating an example set of operations of a method for decoding sensor data provided to a backend system by a sensor kit according to some embodiments of the present disclosure.

[0503] Figure 148 is a flow chart illustrating an example set of operations of a method for encoding sensor data captured by a sensor kit using a media codec according to some embodiments of the present disclosure.

[0504] Figure 149 is a flow chart illustrating an example set of operations of a method for decoding sensor data provided to a backend system by a sensor kit using a media codec according to some embodiments of the present disclosure.

[0505] Figure 150 is a flow chart illustrating an example set of operations of a method for determining a transmission strategy and / or a storage strategy for sensor data collected by a sensor kit based on the sensor data, according to some embodiments of the present disclosure

[0506] Figures 151-155 are schematics illustrating different configurations of sensor kits according to some embodiments of the present disclosure.

[0507] Figure 156 is a flowchart illustrating an example set of operations of a method for monitoring industrial settings using an automatically configured backend system, according to some embodiments of the present disclosure.

[0508] Figure 157 is a plan view of a manufacturing facility illustrating an exemplary implementation of a sensor kit including an edge device, according to some embodiments of the present disclosure.

[0509] Figure 158 is a plan view of a surface portion of an underwater industrial facility illustrating an exemplary implementation of a sensor kit including an edge device, according to some embodiments of the present disclosure.

[0510] Figure 159 is a plan view of an indoor agricultural facility illustrating an exemplary implementation of a sensor kit including an edge device, according to some embodiments of the present disclosure.

[0511] Figure 160 is a schematic illustrating an example of a sensor kit in communication with a data handling platform according to some embodiments of the present disclosure.

[0512] Figures 161-164 are diagrammatic views that depict embodiments of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure.

[0513] Figures 165-167 are diagrammatic views that depict embodiments of a system for using one or more mobile robots and / or mobile vehicles for mobile data collection in accordance with the present disclosure.

[0514] Figures 168-171 are diagrammatic views that depict embodiments of a system for using one or more handheld devices for mobile data collection in accordance with the present disclosure.

[0515] Figures 172-174 are diagrammatic views that depict embodiments of a computer vision system in accordance with the present disclosure.

[0516] Figures 175-176 are diagrammatic views that depict embodiments of a deep learning system for training a computer vision system in accordance with the present disclosure.

[0517] Figure 177 depicts a predictive maintenance eco system network architecture.

[0518] Figure 178 depicts finding service workers using machine learning for the predictive maintenance eco-system of Figure 177.

[0519] Figure 179 depicts ordering parts and service in a predictive maintenance eco-system.

[0520] Figure 180 depicts deployment of smart RFID elements in an industrial machine environment.

[0521] Figure 181 depicts a generalized data structure for machine information in a smart RFID.

[0522] Figure 182 depicts a block level diagram of the storage structure of a smart RFID.

[0523] Figure 183 depicts an example of data stored in a smart RFID.

[0524] Figure 184 depicts a flow diagram of a method for collecting information from a machine.

[0525] Figure 185 depicts a flow diagram of a method for collecting data from a production environment.

[0526] Figure 186 depicts an on-line maintenance management system with interfaces for data sources updating information in the on-line maintenance management system data storage.

[0527] Figure 187 depicts a distributed ledger for predictive maintenance information with role-specific access thereof.

[0528] Figure 188 depicts a process for capturing images of portions of an industrial machine.

[0529] Figure 189 depicts a process that uses machine learning on images to recognize a likely internal structure of an industrial machine.

[0530] Figure 190 depicts a knowledge graph of the predictive maintenance gathering information.

[0531] Figure 191 depicts an artificial intelligence system generating service recommendations and the like based on predictive maintenance analysis.

[0532] Figure 192 depicts a predictive maintenance timeline superimposed on a preventive maintenance timeline.

[0533] Figure 193 depicts a block diagram of potential sources of diagnostic information.

[0534] Figure 194 depicts a diagram of a process for rating vendors.

[0535] Figure 195 depicts a diagram of a process for rating procedures

[0536] Figure 196 depicts a diagram of Blockchain applied to transactions of a predictive maintenance eco-system.

[0537] Figure 197 depicts a transfer function that facilitates converting vibration data into severity units.

[0538] Figure 198 depicts a table that facilitates mapping vibration data to severity units.

[0539] Figure 199 depicts a composite frequency graph for conventional vibration assessment and severity unit-based assessment.

[0540] Figure 200 depicts a rendering of a portion of an industrial machine for use in an electronic user interface for depicting and discovering severity units and related information about a rotating component of the industrial machine.

[0541] Figure 201 depicts a data table of rotating component design parameters for use in predicting maintenance events.

[0542] Figure 202 is a flow chart of predicting maintenance of at least one of a gear, motor and roller bearing based on severity unit and actuator count, such as count of teeth in a gear.

[0543] Figure 203 is a schematic diagram of an example platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system according to some aspects of the present disclosure.

[0544] Figure 204 is a schematic diagram showing additional details, components, subsystems, and other elements of an optional implementation of the example platform of Figure 203;

[0545] Figure 205 is a schematic diagram showing a robotic process automation (“RPA”) system of the example platform of Figure 203;

[0546] Figure 206 is a schematic diagram showing an opportunity mining system and an adaptive intelligence layer of the example platform of Figure 203;

[0547] Figure 207 is a schematic diagram showing optional elements of the adaptive intelligent systems layer that facilitate improved edge intelligence of the example platform of Figure 203;

[0548] Figure 208 is a schematic diagram showing optional elements of an industrial entity-oriented data storage systems layer of the example platform of Figure 203;

[0549] Figure 209 is a schematic diagram showing an example Robotic Process Automation system of the example platform of Figure 203;

[0550] Figure 210 is a schematic diagram of an example system for data processing in an industrial environment that utilizes protocol adaptors according to some aspects of the present disclosure;

[0551] Figure 211 is another schematic diagram illustrating further components and elements of the example system of Figure 210; and

[0552] Figure 212 illustrates an example connect attempt of the example system of Figure 210 according to some aspects of the present disclosure.

[0553] FIG. 213 is a schematic illustrating examples of architecture of a digital twin system according to embodiments of the present disclosure.

[0554] FIG. 214 is a schematic illustrating exemplary components of a digital twin management system according to embodiments of the present disclosure.

[0555] FIG. 215 is a schematic illustrating examples of a digital twin I / O system that interfaces with an environment, the digital twin system, and / or components thereof to provide bi-directional transfer of data between coupled components according to embodiments of the present disclosure.

[0556] FIG. 216 is a schematic illustrating examples of sets of identified states related to industrial environments that the digital twin system may identify and / or store for access by intelligent systems (e.g., a cognitive intelligence system) or users of the digital twin system according to embodiments of the present disclosure.

[0557] FIG. 217 is a schematic illustrating example embodiments of methods for updating a set of properties of a digital twin of the present disclosure on behalf of a client application and / or one or more embedded digital twins according to embodiments of the present disclosure.

[0558] FIG. 218 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to the dryer centrifuge according to embodiments of the present disclosure.

[0559] FIG. 219 is a schematic illustrating example embodiments of methods for updating a set of vibration fault level states of machine components such as bearings in the digital twin of an industrial machine, on behalf of a client application according to embodiments of the present disclosure.

[0560] FIG. 220 is a schematic illustrating example embodiments of methods for updating a set of vibration severity unit values of machine components such as bearings in the digital twin of a machine on behalf of a client application according to embodiments of the present disclosure.

[0561] FIG. 221 is a schematic illustrating example embodiments of a method for updating a set of probability of failure values in the digital twins of machine components on behalf of a client application according to embodiments of the present disclosure.

[0562] FIG. 222 is a schematic illustrating example embodiments of methods for updating a set of probability of downtime values of machines in the digital twin of a manufacturing facility on behalf of a client application according to embodiments of the present disclosure.

[0563] FIG. 223 is a schematic illustrating example embodiments of methods for updating a set of probability of shutdown values of manufacturing facilities in the digital twin of an enterprise on behalf of a client application according to embodiments of the present disclosure.

[0564] FIG. 224 is a schematic illustrating example embodiments of methods for updating a set of cost of downtime values of machines in the digital twin of a manufacturing facility according to embodiments of the present disclosure.

[0565] FIG. 225 is a schematic illustrating example embodiments of methods for updating one or more manufacturing KPI values in a digital twin of a manufacturing facility, on behalf of a client application according to embodiments of the present disclosure.

[0566] FIG. 226 is a view of a display illustrating further example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to its drive components according to embodiments of the present disclosure.

[0567] FIG. 227 is a view of a display illustrating further example embodiments of a display interface of the present disclosure that provides a digital twin showing components of vibration according to embodiments of the present disclosure.

[0568] FIG. 228 is a view of a display illustrating further example embodiments of a display interface of the present disclosure that provides selections of digital twins showing various components experiencing faults according to embodiments of the present disclosure.

[0569] FIG. 229 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin whose view incorporates connected machines each having drive bearings according to embodiments of the present disclosure.

[0570] FIG. 230 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin whose view incorporates connected machines each having drive bearings showing motion outside of nominal according to embodiments of the present disclosure.

[0571] FIG. 231 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin showing drive bearings corrected to nominal motion according to embodiments of the present disclosure.

[0572] FIG. 232 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin whose view incorporates connected machines such as a motor and mill each having drive bearings showing motion outside of nominal according to embodiments of the present disclosure.

[0573] FIG. 233 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin showing drive bearings corrected to nominal motion according to embodiments of the present disclosure.

[0574] FIG. 234 is a schematic illustrating an example of a portion of an information technology system for manufacturing artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[0575] FIG. 235 is a schematic illustrating an example environment of the enterprise and industrial control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0576] FIG. 236 is a schematic illustrating an example implementation of the enterprise and industrial control tower and management platform according to some embodiments of the present disclosure.

[0577] FIG. 237 is a schematic illustrating an example set of components of the enterprise control tower and management platform according to some embodiments of the present disclosure.

[0578] FIG. 238 is a schematic illustrating an example of an enterprise data model according to some embodiments of the disclosure.

[0579] FIG. 239 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of the enterprise digital twin framework according to some embodiments of the present disclosure.

[0580] FIG. 240 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin. DETAILED DESCRIPTION

[0581] Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.

[0582] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate with existing data collection, processing, and storage systems while preserving access to existing format / frequency range / resolution compatible data. While the industrial machine sensor data streaming facilities described herein may collect a greater volume of data (e.g., longer duration of data collection) from sensors at a wider range of frequencies and with greater resolution than existing data collection systems, methods and systems may be employed to provide access to data from the stream of data that represents one or more ranges of frequency and / or one or more lines of resolution that are purposely compatible with existing systems. Further, a portion of the streamed data may be identified, extracted, stored, and / or forwarded to existing data processing systems to facilitate operation of existing data processing systems that substantively matches operation of existing data processing systems using existing collection-based data. In this way, a newly deployed system for sensing aspects of industrial machines, such as aspects of moving parts of industrial machines, may facilitate continued use of existing sensed data processing facilities, algorithms, models, pattern recognizers, user interfaces, and the like.

[0583] Through identification of existing frequency ranges, formats, and / or resolution, such as by accessing a data structure that defines these aspects of existing data, higher resolution streamed data may be configured to represent a specific frequency, frequency range, format, and / or resolution. This configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data. One approach to adapting streamed data for compatibility with existing sensed data may include aligning the streamed data with existing data so that portions of the streamed data that align with the existing data can be extracted, stored, and made available for processing with existing data processing methods. Alternatively, data processing methods may be configured to process portions of the streamed data that correspond, such as through alignment, to the existing data, with methods that implement functions substantially similar to the methods used to process existing data, such as methods that process data that contain a particular frequency range or a particular resolution and the like.

[0584] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range. This method can thusly be at least partially identifiable by these characteristics of the data being processed. Therefore, given a set of conditions, such as moving device being sensed, industrial machine type, frequency of data being sensed, and the like, a data processing system may select an appropriate method. Also, given such a set of conditions, an industrial machine data sensing and processing facility may configure elements, such as data filters, routers, processors, and the like, to handle data meeting the conditions.

[0585] Figures 1 through 5 depict portions of an overall view of an industrial Internet of Things (loT) data collection, monitoring and control system 10. Figure 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. This allows the industrial environment to use the benefits of networking and control technologies, while also providing security, such as preventing cyber-attacks. The MANET 20 may use cognitive radio technologies 40, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In certain embodiments, the system depicted in Figures 1 through 5 provides network-sensitive or network-aware transport of data over the network to and from a data collection device or a heavy industrial machine.

[0586] Figures 3-4 depict intelligent data collection technologies deployed locally, at the edge of an loT deployment, where heavy industrial machines are located. This includes various sensors 52, loT devices 54, data storage capabilities (e.g., data pools 60, or distributed ledger 62) (including intelligent, self-organizing storage), sensor fusion (including self-organizing sensor fusion), and the like. Interfaces for data collection, including multi-sensory interfaces, tablets, smartphones 58, and the like are shown. Figure 3 also shows data pools 60 that may collect data published by machines or sensors that detect conditions of machines, such as for later consumption by local or remote intelligence. A distributed ledger system 62 may distribute storage across the local storage of various elements of the environment, or more broadly throughout the system. Figure 4 also shows on-device sensor fusion 80, such as for storing on a device data from multiple analog sensors 82, which may be analyzed locally or in the cloud, such as by machine learning 84, including by training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success) when operating the methods and systems disclosed herein.

[0587] Figure 1 depicts a server based portion of an industrial loT system that may be deployed in the cloud or on an enterprise owner’s or operator’s premises. The server portion includes network coding (including self-organizing network coding and / or automated configuration) that may configure a network coding model based on feedback measures, network conditions, or the like, for highly efficient transport of large amounts of data across the network to and from data collection systems and the cloud. Network coding may provide a wide range of capabilities for intelligence, analytics, remote control, remote operation, remote optimization, various storage configurations and the like, as depicted in Figure 1. The various storage configurations may include distributed ledger storage for supporting transactional data or other elements of the system.

[0588] Figure 5 depicts a programmatic data marketplace 70, which may be a self-organizing marketplace, such as for making available data that is collected in industrial environments, such as from data collectors, data pools, distributed ledgers, and other elements disclosed herein. Additional detail on the various components and sub-components of Figures 1 through 5 is provided throughout this disclosure.

[0589] With reference to Figure 6, an embodiment of platform 100 may include a local data collection system 102, which may be disposed in an environment 104, such as an industrial environment similar to that shown in Figure 3, for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements. The platform 100 may connect to or include portions of the industrial loT data collection, monitoring and control system 10 depicted in Figures 1-5. The platform 100 may include a network data transport system 108, such as for transporting data to and from the local data collection system 102 over a network 110, such as to a host processing system 112, such as one that is disposed in a cloud computing environment or on the premises of an enterprise, or that consists of distributed components that interact with each other to process data collected by the local data collection system 102. The host processing system 112, referred to for convenience in some cases as the host system 112, may include various systems, components, methods, processes, facilities, and the like for enabling automated, or automation-assisted processing of the data, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements in a local environment 104 or in a network 110. The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116, which may comprise information feeds and inputs from a wide array of sources, including those in the local environment 104, in a network 110, in the host system 112, or in one or more external systems, databases, or the like. The platform 100 may include one or more intelligent systems 118, which may be disposed in, integrated with, or acting as inputs to one or more components of the platform 100. Details of these and other components of the platform 100 are provided throughout this disclosure.

[0590] Intelligent systems 118 may include cognitive systems 120, such as enabling a degree of cognitive behavior as a result of the coordination of processing elements, such as mesh, peer-to-peer, ring, serial, and other architectures, where one or more node elements is coordinated with other node elements to provide collective, coordinated behavior to assist in processing, communication, data collection, or the like. The MANET 20 depicted in Figure 2 may also use cognitive radio technologies, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In one example, the cognitive system technology stack can include examples disclosed in U.S. Patent Number 8,060,017 to Schlicht et al., issued 15 November 2011 and hereby incorporated by reference as if fully set forth herein.

[0591] Intelligent systems may include machine learning systems 122, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like that may, in turn, be used for processing by the host system 112 as inputs to components of the platform 100 and portions of the industrial loT data collection, monitoring and control system 10, or the like. Learning may be human-supervised or fully-automated, such as using one or more input sources 116 to provide a data set, along with information about the item to be learned. Machine learning may use one or more models, rules, semantic understandings, workflows, or other structured or semistructured understanding of the world, such as for automated optimization of control of a system or process based on feedback or feed forward to an operating model for the system or process. One such machine learning technique for semantic and contextual understandings, workflows, or other structured or semi-structured understandings is disclosed in U.S. Patent Number 8,200,775 to Moore, issued 12 June 2012, and hereby incorporated by reference as if fully set forth herein. Machine learning may be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, or the like (such as changing a function within a model) based on feedback (such as regarding the success of a model in a given situation) or based on iteration (such as in a recursive process). Where sufficient understanding of the underlying structure or behavior of a system is not known, insufficient data is not available, or in other cases where preferred for various reasons, machine learning may also be undertaken in the absence of an underlying model; that is, input sources may be weighted, structured, or the like within a machine learning facility without regard to any a priori understanding of structure, and outcomes (such as those based on measures of success at accomplishing various desired objectives) can be serially fed to the machine learning system to allow it to learn how to achieve the targeted objectives. For example, the system may learn to recognize faults, to recognize patterns, to develop models or functions, to develop rules, to optimize performance, to minimize failure rates, to optimize profits, to optimize resource utilization, to optimize flow (such as flow of traffic), or to optimize many other parameters that may be relevant to successful outcomes (such as outcomes in a wide range of environments). Machine learning may use genetic programming techniques, such as promoting or demoting one or more input sources, structures, datatypes, objects, weights, nodes, links, or other factors based on feedback (such that successful elements emerge over a series of generations). For example, alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like. In embodiments, local machine learning may turn on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters, identification of a pattern (such as relating to a threat, a failure mode, a success mode, or the like) or the like. For example, a system may learn what sets of sensors should be turned on or off under given conditions to achieve the highest value utilization of a data collector 102. In embodiments, similar techniques may be used to handle optimization of transport of data in the platform 100 (such as in the network 110) by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring network security elements), and the like.

[0592] In embodiments, the local data collection system 102 may include a high-performance, multi-sensor data collector having a number of novel features for collection and processing of analog and other sensor data. In embodiments, a local data collection system 102 may be deployed to the industrial facilities depicted in Figure 3. A local data collection system 102 may also be deployed monitor other machines such as the machine 2200. The data collection system 102 may have on-board intelligent systems 118 (such as for learning to optimize the configuration and operation of the data collector, such as configuring permutations and combinations of sensors based on contexts and conditions). In one example, the data collection system 102 includes a crosspoint switch 130 or other analog switch. Automated, intelligent configuration of the local data collection system 102 may be based on a variety of types of information, such as information from various input sources, including those based on available power, power requirements of sensors, the value of the data collected (such as based on feedback information from other elements of the platform 100), the relative value of information (such as values based on the availability of other sources of the same or similar information), power availability (such as for powering sensors), network conditions, ambient conditions, operating states, operating contexts, operating events, and many others.

[0593] Figure 7 shows elements and sub-components of a data collection and analysis system 1100 for sensor data (such as analog sensor data) collected in industrial environments. As depicted in Figure 7, embodiments of the methods and systems disclosed herein may include hardware that has several different modules starting with the multiplexer (“MUX”) main board 1104. In embodiments, there may be a MUX option board 1108. The MUX 114 main board is where the sensors connect to the system. These connections are on top to enable ease of installation. Then there are numerous settings on the underside of this board as well as on the Mux option board 1108, which attaches to the MUX main board 1104 via two headers one at either end of the board. In embodiments, the Mux option board has the male headers, which mesh together with the female header on the main Mux board. This enables them to be stacked on top of each other taking up less real estate.

[0594] In embodiments, the main Mux board and / or the MUX option board then connects to the mother (e.g., with 4 simultaneous channels) and daughter (e.g., with 4 additional channels for 8 total channels) analog boards 1110 via cables where some of the signal conditioning (such as hardware integration) occurs. The signals then move from the analog boards 1110 to an antialiasing board (not shown) where some of the potential aliasing is removed. The rest of the aliasing removal is done on the delta sigma board 1112. The delta sigma board 1112 provides more aliasing protection along with other conditioning and digitizing of the signal. Next, the data moves to the Jennie™ board 1114 for more digitizing as well as communication to a computer via USB or Ethernet. In embodiments, the Jennie™ board 1114 may be replaced with a pic board 1118 for more advanced and efficient data collection as well as communication. Once the data moves to the computer software 1102, the computer software 1102 can manipulate the data to show trending, spectra, waveform, statistics, and analytics.

[0595] In embodiments, the system is meant to take in all types of data from volts to 4-20 mA signals. In embodiments, open formats of data storage and communication may be used. In some instances, certain portions of the system may be proprietary especially some of research and data associated with the analytics and reporting. In embodiments, smart band analysis is a way to break data down into easily analyzed parts that can be combined with other smart bands to make new more simplified yet sophisticated analytics. In embodiments, this unique information is taken and graphics are used to depict the conditions because picture depictions are more helpful to the user. In embodiments, complicated programs and user interfaces are simplified so that any user can manipulate the data like an expert.

[0596] In embodiments, the system in essence, works in a big loop. The system starts in software with a general user interface (“GUI”) 1124. In embodiments, rapid route creation may take advantage of hierarchical templates. In embodiments, a GUI is created so any general user can populate the information itself with simple templates. Once the templates are created the user can copy and paste whatever the user needs. In addition, users can develop their own templates for future ease of use and to institutionalize the knowledge. When the user has entered all of the user’s information and connected all of the user’s sensors, the user can then start the system acquiring data.

[0597] Embodiments of the methods and systems disclosed herein may include unique electrostatic protection fortrigger and vibration inputs. In many critical industrial environments where large electrostatic forces, which can harm electrical equipment, may build up, for example rotating machinery or low-speed balancing using large belts, proper transducer and trigger input protection is required. In embodiments, a low-cost but efficient method is described for such protection without the need for external supplemental devices.

[0598] Typically, vibration data collectors are not designed to handle large input voltages due to the expense and the fact that, more often than not, it is not needed. A need exists for these data collectors to acquire many varied types of RPM data as technology improves and monitoring costs plummet. In embodiments, a method is using the already established OptoMOS™ technology which permits the switching up front of high voltage signals rather than using more conventional reed-relay approaches. Many historic concerns regarding nonlinear zero crossing or other non-linear solid-state behaviors have been eliminated with regard to the passing through of weakly buffered analog signals. In addition, in embodiments, printed circuit board routing topologies place all of the individual channel input circuitry as close to the input connector as possible. In embodiments, a unique electrostatic protection for trigger and vibration inputs may be placed upfront on the Mux and DAQ hardware in order to dissipate the built up electric charge as the signal passed from the sensor to the hardware. In embodiments, the Mux and analog board may support high-amperage input using a design topology comprising wider traces and solid state relays for upfront circuitry.

[0599] In some systems multiplexers are afterthoughts and the quality of the signal coming from the multiplexer is not considered. As a result of a poor quality multiplexer, the quality of the signal can drop as much as 30 dB or more. Thus, substantial signal quality may be lost using a 24-bit DAQ that has a signal to noise ratio of 110 dB and if the signal to noise ratio drops to 80 dB in the Mux, it may not be much better than a 16-bit system from 20 years ago. In embodiments of this system, an important part at the front of the Mux is upfront signal conditioning on Mux for improved signal-to-noise ratio. Embodiments may perform signal conditioning (such as range / gain control, integration, filtering, etc.) on vibration as well as other signal inputs up front before Mux switching to achieve the highest signal-to-noise ratio.

[0600] In embodiments, in addition to providing a better signal, the multiplexer may provide a continuous monitor alarming feature. Truly continuous systems monitor every sensor all the time but tend to be expensive. Typical multiplexer systems only monitor a set number of channels at one time and switch from bank to bank of a larger set of sensors. As a result, the sensors not being currently collected are not being monitored; if a level increases the user may never know. In embodiments, a multiplexer may have a continuous monitor alarming feature by placing circuitry on the multiplexer that can measure input channel levels against known alarm conditions even when the data acquisition (“DAQ”) is not monitoring the input. In embodiments, continuous monitoring Mux bypass offers a mechanism whereby channels not being currently sampled by the Mux system may be continuously monitored for significant alarm conditions via a number of trigger conditions using filtered peak-hold circuits or functionally similar that are in turn passed on to the monitoring system in an expedient manner using hardware interrupts or other means. This, in essence, makes the system continuously monitoring, although without the ability to instantly capture data on the problem like a true continuous system. In embodiments, coupling this capability to alarm with adaptive scheduling techniques for continuous monitoring and the continuous monitoring system’s software adapting and adjusting the data collection sequence based on statistics, analytics, data alarms and dynamic analysis may allow the system to quickly collect dynamic spectral data on the alarming sensor very soon after the alarm sounds.

[0601] Another restriction of typical multiplexers is that they may have a limited number of channels. In embodiments, use of distributed complex programmable logic device (“CPLD”) chips with dedicated bus for logic control of multiple Mux and data acquisition sections enables a CPLD to control multiple mux and DAQs so that there is no limit to the number of channels a system can handle. Interfacing to multiple types of predictive maintenance and vibration transducers requires a great deal of switching. This includes AC / DC coupling, 4-20 interfacing, integrated electronic piezoelectric transducer, channel power-down (for conserving op-amp power), single-ended or differential grounding options, and so on. Also required is the control of digital pots for range and gain control, switches for hardware integration, AA filtering and triggering. This logic can be performed by a series of CPLD chips strategically located for the tasks they control. A single giant CPLD requires long circuit routes with a great deal of density at the single giant CPLD. In embodiments, distributed CPLDs not only address these concerns but offer a great deal of flexibility. A bus is created where each CPLD that has a fixed assignment has its own unique device address. In embodiments, multiplexers and DAQs can stack together offering additional input and output channels to the system. For multiple boards (e.g., for multiple Mux boards), jumpers are provided for setting multiple addresses. In another example, three bits permit up to 8 boards that are jumper configurable. In embodiments, a bus protocol is defined such that each CPLD on the bus can either be addressed individually or as a group.

[0602] Typical multiplexers may be limited to collecting only sensors in the same bank. For detailed analysis, this may be limiting as there is tremendous value in being able to simultaneously review data from sensors on the same machine. Current systems using conventional fixed bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) that were assigned to a particular group at the time of installation. The only way to provide some flexibility is to either overlap channels or incorporate lots of redundancy in the system both of which can add considerable expense (in some cases an exponential increase in cost versus flexibility). The simplest Mux design selects one of many inputs and routes it into a single output line. A banked design would consist of a group of these simple building blocks, each handling a fixed group of inputs and routing to its respective output. Typically, the inputs are not overlapping so that the input of one Mux grouping cannot be routed into another. Unlike conventional Mux chips which typically switch a fixed group or banks of a fixed selection of channels into a single output (e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user to assign any input to any output. Previously, crosspoint multiplexers were used for specialized purposes such as RGB digital video applications and were as a practical matter too noisy for analog applications such as vibration analysis; however more recent advances in the technology now make it feasible. Another advantage of the crosspoint Mux is the ability to disable outputs by putting them into a high impedance state. This is ideal for an output bus so that multiple Mux cards may be stacked, and their output buses joined together without the need for bus switches.

[0603] In embodiments, this may be addressed by use of an analog crosspoint switch for collecting variable groups of vibration input channels and providing a matrix circuit so the system may access any set of eight channels from the total number of input sensors.

[0604] In embodiments, the ability to control multiple multiplexers with use of distributed CPLD chips with dedicated bus for logic control of multiple Mux and data acquisition sections is enhanced with a hierarchical multiplexer which allows for multiple DAQ to collect data from multiple multiplexers. A hierarchical Mux may allow modularly output of more channels, such as 16, 24 or more to multiple of eight channel card sets. In embodiments, this allows for faster data collection as well as more channels of simultaneous data collection for more complex analysis. In embodiments, the Mux may be configured slightly to make it portable and use data acquisition parking features, which turns SV3X DAQ into a protected system embodiment.

[0605] In embodiments, once the signals leave the multiplexer and hierarchical Mux they move to the analog board where there are other enhancements. In embodiments, power saving techniques may be used such as: power-down of analog channels when not in use; powering down of component boards; power-down of analog signal processing op-amps for non-selected channels ; powering down channels on the mother and the daughter analog boards. The ability to power down component boards and other hardware by the low-level firmware for the DAQ system makes high-level application control with respect to power-saving capabilities relatively easy. Explicit control of the hardware is always possible but not required by default. In embodiments, this power saving benefit may be of value to a protected system, especially if it is battery operated or solar powered.

[0606] In embodiments, in order to maximize the signal to noise ratio and provide the best data, a peak-detector for auto-scaling routed into a separate A / D will provide the system the highest peak in each set of data so it can rapidly scale the data to that peak. For vibration analysis purposes, the built-in A / D convertors in many microprocessors may be inadequate with regards to number of bits, number of channels or sampling frequency versus not slowing the microprocessor down significantly. Despite these limitations, it is useful to use them for purposes of auto-scaling. In embodiments, a separate A / D may be used that has reduced functionality and is cheaper. For each channel of input, after the signal is buffered (usually with the appropriate coupling: AC or DC) but before it is signal conditioned, the signal is fed directly into the microprocessor or low-cost A / D. Unlike the conditioned signal for which range, gain and filter switches are thrown, no switches are varied. This permits the simultaneous sampling of the auto-scaling data while the input data is signal conditioned, fed into a more robust external A / D, and directed into on-board memory using direct memory access (DMA) methods where memory is accessed without requiring a CPU. This significantly simplifies the autoscaling process by not having to throw switches and then allow for settling time, which greatly slows down the auto-scaling process. Furthermore, the data may be collected simultaneously, which assures the best signal-to-noise ratio. The reduced number of bits and other features is usually more than adequate for auto-scaling purposes. In embodiments, improved integration using both analog and digital methods create an innovative hybrid integration which also improves or maintains the highest possible signal to noise ratio.

[0607] In embodiments, a section of the analog board may allow routing of a trigger channel, either raw or buffered, into other analog channels. This may allow a user to route the trigger to any of the channels for analysis and trouble shooting. Systems may have trigger channels for the purposes of determining relative phase between various input data sets or for acquiring significant data without the needless repetition of unwanted input. In embodiments, digitally controlled relays may be used to switch either the raw or buffered trigger signal into one of the input channels. It may be desirable to examine the quality of the triggering pulse because it may be corrupted for a variety of reasons including inadequate placement of the trigger sensor, wiring issues, faulty setup issues such as a dirty piece of reflective tape if using an optical sensor, and so on. The ability to look at either the raw or buffered signal may offer an excellent diagnostic or debugging vehicle. It also can offer some improved phase analysis capability by making use of the recorded data signal for various signal processing techniques such as variable speed filtering algorithms.

[0608] In embodiments, once the signals leave the analog board, the signals move into the delta-sigma board where precise voltage reference for A / D zero reference offers more accurate direct current sensor data. The delta sigma’s high speeds also provide for using higher input oversampling for delta-sigma A / D for lower sampling rate outputs to minimize antialiasing filter requirements. Lower oversampling rates can be used for higher sampling rates. For example, a 3rd order AA filter set for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoff AA filter can then be used for Fmax ranges from 1 kHz and higher (with a secondary filter kicking in at 2.56x the highest sampling rate of 128 kHz). In embodiments, a CPLD may be used as a clockdivider for a delta-sigma A / D to achieve lower sampling rates without the need for digital resampling. In embodiments, a high-frequency crystal reference can be divided down to lower frequencies by employing a CPLD as a programmable clock divider. The accuracy of the divided down lower frequencies is even more accurate than the original source relative to their longer time periods. This also minimizes or removes the need for resampling processing by the delta-sigma A / D.

[0609] In embodiments, the data then moves from the delta-sigma board to the Jennie™ board where phase relative to input and trigger channels using on-board timers may be digitally derived. In embodiments, the Jennie™ board also has the ability to store calibration data and system maintenance repair history data in an on-board card set. In embodiments, the Jennie™ board will enable acquiring long blocks of data at high-sampling rate as opposed to multiple sets of data taken at different sampling rates so it can stream data and acquire long blocks of data for advanced analysis in the future.

[0610] In embodiments, after the signal moves through the Jennie™ board it may then be transmitted to the computer. In embodiments, the computer software will be used to add intelligence to the system starting with an expert system GUI. The GUI will offer a graphical expert system with simplified user interface for defining smart bands and diagnoses which facilitate anyone to develop complex analytics. In embodiments, this user interface may revolve around smart bands, which are a simplified approach to complex yet flexible analytics for the general user. In embodiments, the smart bands may pair with a self-learning neural network for an even more advanced analytical approach. In embodiments, this system may use the machine’s hierarchy for additional analytical insight. One critical part of predictive maintenance is the ability to learn from known information during repairs or inspections. In embodiments, graphical approaches for back calculations may improve the smart bands and correlations based on a known fault or problem.

[0611] In embodiments, there is a smart route which adapts which sensors it collects simultaneously in order to gain additional correlative intelligence. In embodiments, smart operational data store (“ODS”) allows the system to elect to gather data to perform operational deflection shape analysis in order to further examine the machinery condition. In embodiments, adaptive scheduling techniques allow the system to change the scheduled data collected for full spectral analysis across a number (e.g., eight), of correlative channels. In embodiments, the system may provide data to enable extended statistics capabilities for continuous monitoring as well as ambient local vibration for analysis that combines ambient temperature and local temperature and vibration levels changes for identifying machinery issues.

[0612] In embodiments, a data acquisition device may be controlled by a personal computer (PC) to implement the desired data acquisition commands. In embodiments, the DAQ box may be self-sufficient, and can acquire, process, analyze and monitor independent of external PC control. Embodiments may include secure digital (SD) card storage. In embodiments, significant additional storage capability may be provided by utilizing an SD card. This may prove critical for monitoring applications where critical data may be stored permanently. Also, if a power failure should occur, the most recent data may be stored despite the fact that it was not off-loaded to another system.

[0613] A current trend has been to make DAQ systems as communicative as possible with the outside world usually in the form of networks including wireless. In the past it was common to use a dedicated bus to control a DAQ system with either a microprocessor or microcontroller / microprocessor paired with a PC. In embodiments, a DAQ system may comprise one or more microprocessor / microcontrollers, specialized microcontrollers / microprocessors, or dedicated processors focused primarily on the communication aspects with the outside world. These include USB, Ethernet and wireless with the ability to provide an IP address or addresses in order to host a webpage. All communications with the outside world are then accomplished using a simple text based menu. The usual array of commands (in practice more than a hundred) such as InitializeCard, AcquireData, StopAcquisition, RetrieveCalibration Info, and so on, would be provided.

[0614] In embodiments, intense signal processing activities including resampling, weighting, filtering, and spectrum processing may be performed by dedicated processors such as field-programmable gate array (“FPGAs”), digital signal processor (“DSP”), microprocessors, micro-controllers, or a combination thereof. In embodiments, this subsystem may communicate via a specialized hardware bus with the communication processing section. It will be facilitated with dual-port memory, semaphore logic, and so on. This embodiment will not only provide a marked improvement in efficiency but can significantly improve the processing capability, including the streaming of the data as well other high-end analytical techniques. This negates the need for constantly interrupting the main processes which include the control of the signal conditioning circuits, triggering, raw data acquisition using the A / D, directing the A / D output to the appropriate on-board memory and processing that data.

[0615] Embodiments may include sensor overload identification. A need exists for monitoring systems to identify when the sensor is overloading. There may be situations involving high-frequency inputs that will saturate a standard 100 mv / g sensor (which is most commonly used in the industry) and having the ability to sense the overload improves data quality for better analysis. A monitoring system may identify when their system is overloading, but in embodiments, the system may look at the voltage of the sensor to determine if the overload is from the sensor, enabling the user to get another sensor better suited to the situation, or gather the data again.

[0616] Embodiments may include radio frequency identification (“RFID”) and an inclinometer or accelerometer on a sensor so the sensor can indicate what machine / bearing it is attached to and what direction such that the software can automatically store the data without the user input. In embodiments, users could put the system on any machine or machines and the system would automatically set itself up and be ready for data collection in seconds.

[0617] Embodiments may include ultrasonic online monitoring by placing ultrasonic sensors inside transformers, motor control centers, breakers and the like and monitoring, via a sound spectrum, continuously looking for patterns that identify arcing, corona and other electrical issues indicating a break down or issue. Embodiments may include providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. In embodiments, an analysis engine may be used in ultrasonic online monitoring as well as identifying other faults by combining the ultrasonic data with other parameters such as vibration, temperature, pressure, heat flux, magnetic fields, electrical fields, currents, voltage, capacitance, inductance, and combinations (e g., simple ratios) of the same, among many others.

[0618] Embodiments of the methods and systems disclosed herein may include use of an analog crosspoint switch for collecting variable groups of vibration input channels. For vibration analysis, it is useful to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or machines) in multiple directions. By obtaining the readings at the same time, for example, the relative phases of the inputs may be compared for the purpose of diagnosing various mechanical faults. Other types of cross channel analyses such as cross-correlation, transfer functions, Operating Deflection Shape (“ODS”) may also be performed.

[0619] Embodiments of the methods and systems disclosed herein may include precise voltage reference for A / D zero reference. Some A / D chips provide their own internal zero voltage reference to be used as a mid-scale value for external signal conditioning circuitry to ensure that both the A / D and external op-amps use the same reference. Although this sounds reasonable in principle, there are practical complications. In many cases these references are inherently based on a supply voltage using a resistor-divider. For many current systems, especially those whose power is derived from a PC via USB or similar bus, this provides for an unreliable reference, as the supply voltage will often vary quite significantly with load. This is especially true for delta-sigma A / D chips which necessitate increased signal processing. Although the offsets may drift together with load, a problem arises if one wants to calibrate the readings digitally. It is typical to modify the voltage offset expressed as counts coming from the A / D digitally to compensate for the DC drift. However, for this case, if the proper calibration offset is determined for one set of loading conditions, they will not apply for other conditions. An absolute DC offset expressed in counts will no longer be applicable. As a result, it becomes necessary to calibrate for all loading conditions which becomes complex, unreliable, and ultimately unmanageable. In embodiments, an external voltage reference is used which is simply independent of the supply voltage to use as the zero offset.

[0620] In embodiments, the system provides a phase-lock-loop band pass tracking filter method for obtaining slow-speed RPMs and phase for balancing purposes to remotely balance slow speed machinery, such as in paper mills, as well as offering additional analysis from its data. For balancing purposes, it is sometimes necessary to balance at very slow speeds. A typical tracking filter may be constructed based on a phase-lock loop or PLL design; however, stability and speed range are overriding concerns. In embodiments, a number of digitally controlled switches are used for selecting the appropriate RC and damping constants. The switching can be done all automatically after measuring the frequency of the incoming tach signal. Embodiments of the methods and systems disclosed herein may include digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, digital phase derivation uses digital timers to ascertain an exact delay from a trigger event to the precise start of data acquisition. This delay, or offset, then, is further refined using interpolation methods to obtain an even more precise offset which is then applied to the analytically determined phase of the acquired data such that the phase is “in essence” an absolute phase with precise mechanical meaning useful for among other things, one-shot balancing, alignment analysis, and so on.

[0621] Embodiments of the methods and systems disclosed herein may include signal processing firmware / hardware. In embodiments, long blocks of data may be acquired at high-sampling rate as opposed to multiple sets of data taken at different sampling rates. Typically, in modem route collection for vibration analysis, it is customary to collect data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary from route point to point based on the specific mechanical analysis requirements at hand. For example, a motor may require a relatively low sampling rate with high resolution to distinguish running speed harmonics from line frequency harmonics. The practical trade-off here though is that it takes more collection time to achieve this improved resolution. In contrast, some highspeed compressors or gear sets require much higher sampling rates to measure the amplitudes of relatively higher frequency data although the precise resolution may not be as necessary. Ideally, however, it would be better to collect a very long sample length of data at a very high-sampling rate. When digital acquisition devices were first popularized in the early 1980’s, the A / D sampling, digital storage, and computational abilities were not close to what they are today, so compromises were made between the time required for data collection and the desired resolution and accuracy. It was because of this limitation that some analysts in the field even refused to give up their analog tape recording systems, which did not suffer as much from these same digitizing drawbacks. A few hybrid systems were employed that would digitize the play back of the recorded analog data at multiple sampling rates and lengths desired, though these systems were admittedly less automated. The more common approach, as mentioned earlier, is to balance data collection time with analysis capability and digitally acquire the data blocks at multiple sampling rates and sampling lengths and digitally store these blocks separately. In embodiments, a long data length of data can be collected at the highest practical sampling rate (e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This long block of data can be acquired in the same amount of time as the shorter length of the lower sampling rates utilized by a priori methods so that there is no effective delay added to the sampling at the measurement point, always a concern in route collection. In embodiments, analog tape recording of data is digitally simulated with such a precision that it can be in effect considered continuous or “analog” for many purposes, including for purposes of embodiments of the present disclosure, except where context indicates otherwise.

[0622] Embodiments of the methods and systems disclosed herein may include storage of calibration data and maintenance history on-board card sets. Many data acquisition devices which rely on interfacing to a PC to function store their calibration coefficients on the PC. This is especially true for complex data acquisition devices whose signal paths are many and therefore whose calibration tables can be quite large. In embodiments, calibration coefficients are stored in flash memory which will remember this data or any other significant information for that matter, for all practical purposes, permanently. This information may include nameplate information such as serial numbers of individual components, firmware or software version numbers, maintenance history, and the calibration tables. In embodiments, no matter which computer the box is ultimately connected to, the DAQ box remains calibrated and continues to hold all of this critical information. The PC or external device may poll for this information at any time for implantation or information exchange purposes.

[0623] Embodiments of the methods and systems disclosed herein may include rapid route creation taking advantage of hierarchical templates. In the field of vibration monitoring, as well as parametric monitoring in general, it is necessary to establish in a database or functional equivalent the existence of data monitoring points. These points are associated a variety of attributes including the following categories: transducer attributes, data collection settings, machinery parameters and operating parameters. The transducer attributes would include probe type, probe mounting type and probe mounting direction or axis orientation. Data collection attributes associated with the measurement would involve a sampling rate, data length, integrated electronic piezoelectric probe power and coupling requirements, hardware integration requirements, 4-20 or voltage interfacing, range and gain settings (if applicable), filter requirements, and so on. Machinery parametric requirements relative to the specific point would include such items as operating speed, bearing type, bearing parametric data which for a rolling element bearing includes the pitch diameter, number of balls, inner race, and outer race diameters. For a tilting pad bearing, this would include the number of pads and so on. For measurement points on a piece of equipment such as a gearbox, needed parameters would include, for example, the number of gear teeth on each of the gears. For induction motors, it would include the number of rotor bars and poles; for compressors, the number of blades and / or vanes; for fans, the number of blades. For belt / pulley systems, the number of belts as well as the relevant belt-passing frequencies may be calculated from the dimensions of the pulleys and pulley center-to-center distance. For measurements near couplings, the coupling type and number of teeth in a geared coupling may be necessary, and so on. Operating parametric data would include operating load, which may be expressed in megawatts, flow (either air or fluid), percentage, horsepower, feet-per-minute, and so on. Operating temperatures both ambient and operational, pressures, humidity, and so on, may also be relevant. As can be seen, the setup information required for an individual measurement point can be quite large. It is also crucial to performing any legitimate analysis of the data. Machinery, equipment, and bearing specific information are essential for identifying fault frequencies as well as anticipating the various kinds of specific faults to be expected. The transducer attributes as well as data collection parameters are vital for properly interpreting the data along with providing limits for the type of analytical techniques suitable. The traditional means of entering this data has been manual and quite tedious, usually at the lowest hierarchical level (for example, at the bearing level with regards to machinery parameters), and at the transducer level for data collection setup information. It cannot be stressed enough, however, the importance of the hierarchical relationships necessary to organize data— both for analytical and interpretive purposes as well as the storage and movement of data. Here, we are focusing primarily on the storage and movement of data. By its nature, the aforementioned setup information is extremely redundant at the level of the lowest hierarchies; however, because of its strong hierarchical nature, it can be stored quite efficiently in that form. In embodiments, hierarchical nature can be utilized when copying data in the form of templates. As an example, hierarchical storage structure suitable for many purposes is defined from general to specific of company, plant or site, unit or process, machine, equipment, shaft element, bearing, and transducer. It is much easier to copy data associated with a particular machine, piece of equipment, shaft element or bearing than it is to copy only at the lowest transducer level. In embodiments, the system not only stores data in this hierarchical fashion, but robustly supports the rapid copying of data using these hierarchical templates. Similarity of elements at specific hierarchical levels lends itself to effective data storage in hierarchical format. For example, so many machines have common elements such as motors, gearboxes, compressors, belts, fans, and so on. More specifically, many motors can be easily classified as induction, DC, fixed or variable speed. Many gearboxes can be grouped into commonly occurring groupings such as input / output, input pinion / intermediate pinion / output pinion, 4-posters, and so on. Within a plant or company, there are many similar types of equipment purchased and standardized on for both cost and maintenance reasons. This results in an enormous overlapping of similar types of equipment and, as a result, offers a great opportunity for taking advantage of a hierarchical template approach.

[0624] Embodiments of the methods and systems disclosed herein may include smart bands. Smart bands refer to any processed signal characteristics derived from any dynamic input or group of inputs for the purposes of analyzing the data and achieving the correct diagnoses. Furthermore, smart bands may even include mini or relatively simple diagnoses for the purposes of achieving a more robust and complex one. Historically, in the field of mechanical vibration analysis, Alarm Bands have been used to define spectral frequency bands of interest for the proposes of analyzing and / or trending significant vibration patterns. The Alarm Band typically consists of a spectral (amplitude plotted against frequency) region defined between a low and high frequency border. The amplitude between these borders is summed in the same manner for which an overall amplitude is calculated. A Smart Band is more flexible in that it not only refers to a specific frequency band but can also refer to a group of spectral peaks such as the harmonics of a single peak, a true-peak level or crest factor derived from a time waveform, an overall derived from a vibration envelope spectrum or other specialized signal analysis technique or a logical combination (AND, OR, XOR, etc.) of these signal attributes. In addition, a myriad assortment of other parametric data, including system load, motor voltage and phase information, bearing temperature, flow rates, and the like, can likewise be used as the basis for forming additional smart bands. In embodiments, Smart Band symptoms may be used as building blocks for an expert system whose engine would utilize these inputs to derive diagnoses. Some of these mini-diagnoses may then in turn be used as Smart-Band symptoms (smart bands can include even diagnoses) for more generalized diagnoses.

[0625] Embodiments of the methods and systems disclosed herein may include a neural net expert system using smart bands. Typical vibration analysis engines are rule-based (i.e., they use a list of expert rules which, when met, trigger specific diagnoses). In contrast, a neural approach utilizes the weighted triggering of multiple input stimuli into smaller analytical engines or neurons which in turn feed a simplified weighted output to other neurons. The output of these neurons can be also classified as smart bands which in turn feed other neurons. This produces a more layered approach to expert diagnosing as opposed to the one-shot approach of a rule-based system. In embodiments, the expert system utilizes this neural approach using smart bands; however, it does not preclude rule-based diagnoses being reclassified as smart bands as further stimuli to be utilized by the expert system. From this point-of-view, it can be overviewed as a hybrid approach, although at the highest level it is essentially neural.

[0626] Embodiments of the methods and systems disclosed herein may include use of database hierarchy in analysis smart band symptoms and diagnoses may be assigned to various hierarchical database levels. For example, a smart band may be called “Looseness” at the bearing level, trigger “Looseness” at the equipment level, and trigger “Looseness” at the machine level. Another example would be having a smart band diagnosis called “Horizontal Plane Phase Flip” across a coupling and generate a smart band diagnosis of “Vertical Coupling Misalignment” at the machine level.

[0627] Embodiments of the methods and systems disclosed herein may include expert system GUIs. In embodiments, the system undertakes a graphical approach to defining smart bands and diagnoses for the expert system. The entry of symptoms, rules, or more generally smart bands for creating a particular machine diagnosis, may be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring. The proposed graphical interface consists of four major components: a symptom parts bin, diagnoses bin, tools bin, and graphical wiring area (“GWA”). In embodiments, a symptom parts bin includes various spectral, waveform, envelope and any type of signal processing characteristic or grouping of characteristics such as a spectral peak, spectral harmonic, waveform true-peak, waveform crest-factor, spectral alarm band, and so on. Each part may be assigned additional properties. For example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a lx, 2x, 3x running speed, lx, 2x, 3x gear mesh, lx, 2x, 3x blade pass, number of motor rotor bars x running speed, and so on.

[0628] In embodiments, the diagnoses bin includes various pre-defined as well as user-defined diagnoses such as misalignment, imbalance, looseness, bearing faults, and so on. Like parts, diagnoses may also be used as parts for the purposes of building more complex diagnoses. In embodiments, the tools bin includes logical operations such as AND, OR, XOR, etc. or other ways of combining the various parts listed above such as Find Max, Find Min, Interpolate, Average, other Statistical Operations, etc. In embodiments, a graphical wiring area includes parts from the parts bin or diagnoses from the diagnoses bin and may be combined using tools to create diagnoses. The various parts, tools and diagnoses will be represented with icons which are simply graphically wired together in the desired manner.

[0629] Embodiments of the methods and systems disclosed herein may include a graphical approach for back-calculation definition. In embodiments, the expert system also provides the opportunity for the system to leam. If one already knows that a unique set of stimuli or smart bands corresponds to a specific fault or diagnosis, then it is possible to back-calculate a set of coefficients that when applied to a future set of similar stimuli would arrive at the same diagnosis. In embodiments, if there are multiple sets of data, a best-fit approach may be used. Unlike the smart band GUI, this embodiment will self-generate a wiring diagram. In embodiments, the user may tailor the back-propagation approach settings and use a database browser to match specific sets of data with the desired diagnoses. In embodiments, the desired diagnoses may be created or custom tailored with a smart band GUI. In embodiments, after that, a user may press the GENERATE button and a dynamic wiring of the symptom-to-diagnosis may appear on the screen as it works through the algorithms to achieve the best fit. In embodiments, when complete, a variety of statistics are presented which detail how well the mapping process proceeded. In some cases, no mapping may be achieved if, for example, the input data was all zero or the wrong data (mistakenly assigned) and so on. Embodiments of the methods and systems disclosed herein may include bearing analysis methods. In embodiments, bearing analysis methods may be used in conjunction with a computer aided design (“CAD”), predictive deconvolution, minimum variance distortionless response (“MVDR”) and spectrum sum-of-harmonics.

[0630] In recent years, there has been a strong drive to save power which has resulted in an influx of variable frequency drives and variable speed machinery. In embodiments, a bearing analysis method is provided. In embodiments, torsional vibration detection and analysis is provided utilizing transitory signal analysis to provide an advanced torsional vibration analysis for a more comprehensive way to diagnose machinery where torsional forces are relevant (such as machinery with rotating components). Due primarily to the decrease in cost of motor speed control systems, as well as the increased cost and consciousness of energy-usage, it has become more economically justifiable to take advantage of the potentially vast energy savings of load control. Unfortunately, one frequently overlooked design aspect of this issue is that of vibration. When a machine is designed to run at only one speed, it is far easier to design the physical structure accordingly so as to avoid mechanical resonances both structural and torsional, each of which can dramatically shorten the mechanical health of a machine. This would include such structural characteristics as the types of materials to use, their weight, stiffening member requirements and placement, bearing types, bearing location, base support constraints, etc. Even with machines running at one speed, designing a structure so as to minimize vibration can prove a daunting task, potentially requiring computer modeling, finite-element analysis, and field testing. By throwing variable speeds into the mix, in many cases, it becomes impossible to design for all desirable speeds. The problem then becomes one of minimization, e.g., by speed avoidance. This is why many modem motor controllers are typically programmed to skip or quickly pass through specific speed ranges or bands. Embodiments may include identifying speed ranges in a vibration monitoring system. Non-torsional, structural resonances are typically fairly easy to detect using conventional vibration analysis techniques. However, this is not the case for torsion. One special area of current interest is the increased incidence of torsional resonance problems, apparently due to the increased torsional stresses of speed change as well as the operation of equipment at torsional resonance speeds. Unlike non-torsional structural resonances which generally manifest their effect with dramatically increased casing or external vibration, torsional resonances generally show no such effect. In the case of a shaft torsional resonance, the twisting motion induced by the resonance may only be discernible by looking for speed and / or phase changes. The current standard methodology for analyzing torsional vibration involves the use of specialized instrumentation. Methods and systems disclosed herein allow analysis of torsional vibration without such specialized instrumentation. This may consist of shutting the machine down and employing the use of strain gauges and / or other special fixturing such as speed encoder plates and / or gears. Friction wheels are another alternative, but they typically require manual implementation and a specialized analyst. In general, these techniques can be prohibitively expensive and / or inconvenient. An increasing prevalence of continuous vibration monitoring systems due to decreasing costs and increasing convenience (e.g., remote access) exists. In embodiments, there is an ability to discern torsional speed and / or phase variations with just the vibration signal. In embodiments, transient analysis techniques may be utilized to distinguish torsionally induced vibrations from mere speed changes due to process control. In embodiments, factors for discernment might focus on one or more of the following aspects: the rate of speed change due to variable speed motor control would be relatively slow, sustained and deliberate; torsional speed changes would tend to be short, impulsive and not sustained; torsional speed changes would tend to be oscillatory, most likely decaying exponentially, process speed changes would not; and smaller speed changes associated with torsion relative to the shaft’s rotational speed which suggest that monitoring phase behavior would show the quick or transient speed bursts in contrast to the slow phase changes historically associated with ramping a machine’s speed up or down (as typified with Bode or Nyquist plots).

[0631] Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When a signal is digitally integrated using software, essentially the spectral low-end frequency data has its amplitude multiplied by a function which quickly blows up as it approaches zero and creates what is known in the industry as a “ski-slope” effect. The amplitude of the ski-slope is essentially the noise floor of the instrument. The simple remedy for this is the traditional hardware integrator, which can perform at signal-to-noise ratios much greater than that of an already digitized signal. It can also limit the amplification factor to a reasonable level so that multiplication by very large numbers is essentially prohibited. However, at high frequencies where the frequency becomes large, the original amplitude which may be well above the noise floor is multiplied by a very small number (1 / f) that plunges it well below the noise floor. The hardware integrator has a fixed noise floor that although low floor does not scale down with the now lower amplitude high-frequency data. In contrast, the same digital multiplication of a digitized high-frequency signal also scales down the noise floor proportionally. In embodiments, hardware integration may be used below the point of unity gain where (at a value usually determined by units and / or desired signal to noise ratio based on gain) and software integration may be used above the value of unity gain to produce an ideal result. In embodiments, this integration is performed in the frequency domain. In embodiments, the resulting hybrid data can then be transformed back into a waveform which should be far superior in signal-to-noise ratio when compared to either hardware integrated or software integrated data. In embodiments, the strengths of hardware integration are used in conjunction with those of digital software integration to achieve the maximum signal-to-noise ratio. In embodiments, the first order gradual hardware integrator high pass filter along with curve fitting allow some relatively low frequency data to get through while reducing or eliminating the noise, allowing very useful analytical data that steep filters kill to be salvaged.

[0632] Embodiments of the methods and systems disclosed herein may include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is often performed with an up-front Mux whose purpose it is to select a few channels of data among many to feed the hardware signal processing, A / D, and processing components of a DAQ system. This is done primarily out of practical cost considerations. The tradeoff is that all of the points are not monitored continuously (although they may be monitored to a lesser extent via alternative hardware methods). In embodiments, multiple scheduling levels are provided. In embodiments, at the lowest level, which is continuous for the most part, all of the measurement points will be cycled through in round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 points, then each point is serviced once every 15 minutes; however, if a point should alarm by whatever criteria the user selects, its priority level can be increased so that it is serviced more often. As there can be multiple grades of severity for each alarm, so can there me multiple levels of priority with regards to monitoring. In embodiments, more severe alarms will be monitored more frequently. In embodiments, a number of additional high-level signal processing techniques can be applied at less frequent intervals. Embodiments may take advantage of the increased processing power of a PC and the PC can temporarily suspend the round-robin route collection (with its multiple tiers of collection) process and stream the required amount of data for a point of its choosing. Embodiments may include various advanced processing techniques such as envelope processing, wavelet analysis, as well as many other signal processing techniques. In embodiments, after acquisition of this data, the DAQ card set will continue with its route at the point it was interrupted. In embodiments, various PC scheduled data acquisitions will follow their own schedules which will be less frequency than the DAQ card route. They may be set up hourly, daily, by number of route cycles (for example, once every 10 cycles) and also increased scheduling-wise based on their alarm severity priority or type of measurement (e.g., motors may be monitored differently than fans).

[0633] Embodiments of the methods and systems disclosed herein may include data acquisition parking features. In embodiments, a data acquisition box used for route collection, real time analysis and in general as an acquisition instrument can be detached from its PC (tablet or otherwise) and powered by an external power supply or suitable battery. In embodiments, the data collector still retains continuous monitoring capability and its on-board firmware can implement dedicated monitoring functions for an extended period of time or can be controlled remotely for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical capabilities for continuous monitoring.

[0634] Embodiments of the methods and systems disclosed herein may include ambient sensing plus local sensing plus vibration for analysis. In embodiments, ambient environmental temperature and pressure, sensed temperature and pressure may be combined with long / medium term vibration analysis for prediction of any of a range of conditions or characteristics. Variants may add infrared sensing, infrared thermography, ultrasound, and many other types of sensors and input types in combination with vibration or with each other. Embodiments of the methods and systems disclosed herein may include a smart route. In embodiments, the continuous monitoring system’s software will adapt / adjust the data collection sequence based on statistics, analytics, data alarms and dynamic analysis. Typically, the route is set based on the channels the sensors are attached to. In embodiments, with the crosspoint switch, the Mux can combine any input Mux channels to the (e.g., eight) output channels. In embodiments, as channels go into alarm or the system identifies key deviations, it will pause the normal route set in the software to gather specific simultaneous data, from the channels sharing key statistical changes, for more advanced analysis. Embodiments include conducting a smart ODS or smart transfer function.

[0635] Embodiments of the methods and systems disclosed herein may include smart ODS and one or more transfer functions. In embodiments, due to a system’s multiplexer and crosspoint switch, an ODS, a transfer function, or other special tests on all the vibration sensors attached to a machine / structure can be performed and show exactly how the machine’s points are moving in relationship to each other. In embodiments, 40-50 kHz and longer data lengths (e.g., at least one minute) may be streamed, which may reveal different information than what a normal ODS or transfer function will show. In embodiments, the system will be able to determine, based on the data / statistics / analytics to use, the smart route feature that breaks from the standard route and conducts an ODS across a machine, structure or multiple machines and structures that might show a correlation because the conditions / data directs it. In embodiments, for the transfer functions there may be an impact hammer used on one channel and then compared against other vibration sensors on the machine. In embodiments, the system may use the condition changes such as load, speed, temperature or other changes in the machine or system to conduct the transfer function. In embodiments, different transfer functions may be compared to each other over time. In embodiments, difference transfer functions may be strung together like a movie that may show how the machinery fault changes, such as a bearing that could show how it moves through the four stages of bearing failure and so on. Embodiments of the methods and systems disclosed herein may include a hierarchical Mux.

[0636] With reference to Figure 8, the present disclosure generally includes digitally collecting or streaming waveform data 2010 from a machine 2020 whose operational speed can vary from relatively slow rotational or oscillational speeds to much higher speeds in different situations. The waveform data 2010, at least on one machine, may include data from a single axis sensor 2030 mounted at an unchanging reference location 2040 and from a three-axis sensor 2050 mounted at changing locations (or located at multiple locations), including location 2052. In embodiments, the waveform data 2010 can be vibration data obtained simultaneously from each sensor 2030, 2050 in a gap-free format for a duration of multiple minutes with maximum resolvable frequencies sufficiently large to capture periodic and transient impact events. By way of this example, the waveform data 2010 can include vibration data that can be used to create an operational deflecting shape. It can also be used, as needed, to diagnose vibrations from which a machine repair solution can be prescribed.

[0637] In embodiments, the machine 2020 can further include a housing 2100 that can contain a drive motor 2110 that can drive a shaft 2120. The shaft 2120 can be supported for rotation or oscillation by a set of bearings 2130, such as including a first bearing 2140 and a second bearing 2150. A data collection module 2160 can connect to (or be resident on) the machine 2020. In one example, the data collection module 2160 can be located and accessible through a cloud network facility 2170, can collect the waveform data 2010 from the machine 2020, and deliver the waveform data 2010 to a remote location. A working end 2180 of the drive shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, a drill, a gear system, a drive system, or other working element, as the techniques described herein can apply to a wide range of machines, equipment, tools, or the like that include rotating or oscillating elements. In other instances, a generator can be substituted for the motor 2110, and the working end of the drive shaft 2120 can direct rotational energy to the generator to generate power, rather than consume it.

[0638] In embodiments, the waveform data 2010 can be obtained using a predetermined route format based on the layout of the machine 2020. The waveform data 2010 may include data from the single axis sensor 2030 and the three-axis sensor 2050. The single-axis sensor 2030 can serve as a reference probe with its one channel of data and can be fixed at the unchanging location 2040 on the machine under survey. The three-axis sensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes) with its three channels of data and can be moved along a predetermined diagnostic route format from one test point to the next test point. In one example, both sensors 2030, 2050 can be mounted manually to the machine 2020 and can connect to a separate portable computer in certain service examples. The reference probe can remain at one location while the user can move the tri-axial vibration probe along the predetermined route, such as from bearing-to-bearing on a machine. In this example, the user is instructed to locate the sensors at the predetermined locations to complete the survey (or portion thereof) of the machine.

[0639] With reference to Figure 9, a portion of an exemplary machine 2200 is shown having a tri-axial sensor 2210 mounted to a location 2220 associated with a motor bearing of the machine 2200 with an output shaft 2230 and output member 2240 in accordance with the present disclosure.

[0640] In further examples, the sensors and data acquisition modules and equipment can be integral to, or resident on, the rotating machine. By way of these examples, the machine can contain many single axis sensors and many tri-axial sensors at predetermined locations. The sensors can be originally installed equipment and provided by the original equipment manufacturer or installed at a different time in a retrofit application. The data collection module 2160, or the like, can select and use one single axis sensor and obtain data from it exclusively during the collection of waveform data 2010 while moving to each of the tri-axial sensors. The data collection module 2160 can be resident on the machine 2020 and / or connect via the cloud network facility 2170.

[0641] With reference to Figure 8, the various embodiments include collecting the waveform data 2010 by digitally recording locally, or streaming over, the cloud network facility 2170. The waveform data 2010 can be collected so as to be gap-free with no interruptions and, in some respects, can be similar to an analog recording of waveform data. The waveform data 2010 from all of the channels can be collected for one to two minutes depending on the rotating or oscillating speed of the machine being monitored. In embodiments, the data sampling rate can be at a relatively high-sampling rate relative to the operating frequency of the machine 2020.

[0642] In embodiments, a second reference sensor can be used, and a fifth channel of data can be collected. As such, the single-axis sensor can be the first channel and tri-axial vibration can occupy the second, the third, and the fourth data channels. This second reference sensor, like the first, can be a single axis sensor, such as an accelerometer. In embodiments, the second reference sensor, like the first reference sensor, can remain in the same location on the machine for the entire vibration survey on that machine. The location of the first reference sensor (i.e., the single axis sensor) may be different than the location of the second reference sensors (i.e., another single axis sensor). In certain examples, the second reference sensor can be used when the machine has two shafts with different operating speeds, with the two reference sensors being located on the two different shafts. In accordance with this example, further single-axis reference sensors can be employed at additional but different unchanging locations associated with the rotating machine.

[0643] In embodiments, the waveform data can be transmitted electronically in a gap-free free format at a significantly high rate of sampling for a relatively longer period of time. In one example, the period of time is 60 seconds to 120 seconds. In another example, the rate of sampling is 100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will be appreciated in light of this disclosure that the waveform data can be shown to approximate more closely some of the wealth of data available from previous instances of analog recording of waveform data.

[0644] In embodiments, sampling, band selection, and filtering techniques can permit one or more portions of a long stream of data (i.e., one to two minutes in duration) to be under sampled or over sampled to realize varying effective sampling rates. To this end, interpolation and decimation can be used to further realize varying effective sampling rates. For example, oversampling may be applied to frequency bands that are proximal to rotational or oscillational operating speeds of the sampled machine, or to harmonics thereof, as vibration effects may tend to be more pronounced at those frequencies across the operating range of the machine. In embodiments, the digitally-sampled data set can be decimated to produce a lower sampling rate. It will be appreciated in light of the disclosure that decimate in this context can be the opposite of interpolate. In embodiments, decimating the data set can include first applying a low-pass filter to the digitally-sampled data set and then undersampling the data set.

[0645] In one example, a sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz, but the remaining nine points of that portion of the waveform are effectively discarded and not included in the modeling of the sample waveform. Moreover, this type of bare undersampling can create ghost frequencies due to the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample waveform.

[0646] Most hardware for analog-to-digital conversions uses a sample-and-hold circuit that can charge up a capacitor for a given amount of time such that an average value of the waveform is determined over a specific change in time. It will be appreciated in light of the disclosure that the value of the waveform over the specific change in time is not linear but more similar to a cardinal sinusoidal (“sine”) function; therefore, it can be shown that more emphasis can be placed on the waveform data at the center of the sampling interval with exponential decay of the cardinal sinusoidal signal occurring from its center.

[0647] By way of the above example, the sample waveform at 100 Hz can be hardware-sampled at 10 Hz and therefore each sampling point is averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz can have each point averaged over 10 milliseconds). In contrast to the effective discarding of nine out of the ten data points of the sampled waveform as discussed above, the present disclosure can include weighing adjacent data. The adjacent data can refer to the sample points that were previously discarded and the one remaining point that was retained. In one example, a low pass filter can average the adjacent sample data linearly, i.e., determining the sum of every ten points and then dividing that sum by ten. In a further example, the adjacent data can be weighted with a sine function. The process of weighting the original waveform with the sine function can be referred to as an impulse function, or can be referred to in the time domain as a convolution.

[0648] The present disclosure can be applicable to not only digitizing a waveform signal based on a detected voltage, but can also be applicable to digitizing waveform signals based on current waveforms, vibration waveforms, and image processing signals including video signal rasterization. In one example, the resizing of a window on a computer screen can be decimated, albeit in at least two directions. In these further examples, it will be appreciated that undersampling by itself can be shown to be insufficient. To that end, oversampling or upsampling by itself can similarly be shown to be insufficient, such that interpolation can be used like decimation but in lieu of only undersampling by itself.

[0649] It will be appreciated in light of the disclosure that interpolation in this context can refer to first applying a low pass filter to the digitally-sampled waveform data and then upsampling the waveform data. It will be appreciated in light of the disclosure that real-world examples can often require the use of use non-integer factors for decimation or interpolation, or both. To that end, the present disclosure includes interpolating and decimating sequentially in order to realize a non-integer factor rate for interpolating and decimating. In one example, interpolating and decimating sequentially can define applying a low-pass filter to the sample waveform, then interpolating the waveform after the low-pass filter, and then decimating the waveform after the interpolation. In embodiments, the vibration data can be looped to purposely emulate conventional tape recorder loops, with digital filtering techniques used with the effective splice to facilitate longer analyses. It will be appreciated in light of the disclosure that the above techniques do not preclude waveform, spectrum, and other types of analyses to be processed and displayed with a GUI of the user at the time of collection. It will be appreciated in light of the disclosure that newer systems can permit this functionality to be performed in parallel to the high-performance collection of the raw waveform data.

[0650] With respect to time of collection issues, it will be appreciated that older systems using the compromised approach of improving data resolution, by collecting at different sampling rates and data lengths, do not in fact save as much time as expected. To that end, every time the data acquisition hardware is stopped and started, latency issues can be created, especially when there is hardware auto-scaling performed. The same can be true with respect to data retrieval of the route information (i.e., test locations) that is often in a database format and can be exceedingly slow. The storage of the raw data in bursts to disk (whether solid state or otherwise) can also be undesirably slow.

[0651] In contrast, the many embodiments include digitally streaming the waveform data 2010, as disclosed herein, and also enjoying the benefit of needing to load the route parameter information while setting the data acquisition hardware only once. Because the waveform data 2010 is streamed to only one file, there is no need to open and close files, or switch between loading and writing operations with the storage medium. It can be shown that the collection and storage of the waveform data 2010, as described herein, can be shown to produce relatively more meaningful data in significantly less time than the traditional batch data acquisition approach. An example of this includes an electric motor about which waveform data can be collected with a data length of 4K points (i.e., 4,096) for sufficiently high resolution in order to, among other things, distinguish electrical sideband frequencies. For fans or blowers, a reduced resolution of IK (i.e., 1,024) can be used. In certain instances, IK can be the minimum waveform data length requirement. The sampling rate can be 1,280 Hz and that equates to an Fmax of 500 Hz. It will be appreciated in light of the disclosure that oversampling by an industry standard factor of 2.56 can satisfy the necessary two-times (2x) oversampling for the Nyquist Criterion with some additional leeway that can accommodate anti-aliasing filterrolloff. The time to acquire this waveform data would be 1,024 points at 1,280 hertz, which are 800 milliseconds.

[0652] To improve accuracy, the waveform data can be averaged. Eight averages can be used with, for example, fifty percent overlap. This would extend the time from 800 milliseconds to 3.6 seconds, which is equal to 800 msec x 8 averages x 0.5 (overlap ratio) + 0.5 x 800 msec (non-overlapped head and tail ends). After collection at Fmax = 500 Hz waveform data, a higher sampling rate can be used. In one example, ten times (lOx) the previous sampling rate can be used and Fmax = 10 kHz. By way of this example, eight averages can be used with fifty percent (50%) overlap to collect waveform data at this higher rate that can amount to a collection time of 360 msec or 0.36 seconds. It will be appreciated in light of the disclosure that it can be necessary to read the hardware collection parameters for the higher sampling rate from the route list, as well as permit hardware auto-scaling, or the resetting of other necessary hardware collection parameters, or both. To that end, a few seconds of latency can be added to accommodate the changes in sampling rate. In other instances, introducing latency can accommodate hardware autoscaling and changes to hardware collection parameters that can be required when using the lower sampling rate disclosed herein. In addition to accommodating the change in sampling rate, additional time is needed for reading the route point information from the database (i.e., where to monitor and where to monitor next), displaying the route information, and processing the waveform data. Moreover, display ofthe waveform data and / or associated spectra can also consume significant time. In light of the above, 15 seconds to 20 seconds can elapse while obtaining waveform data at each measurement point.

[0653] In further examples, additional sampling rates can be added but this can make the total amount time for the vibration survey even longer because time adds up from changeover time from one sampling rate to another and from the time to obtain additional data at different sampling rate. In one example, a lower sampling rate is used, such as a sampling rate of 128 Hz where Fmax = 50 Hz. By way ofthis example, the vibration survey would, therefore, require an additional 36 seconds for the first set of averaged data at this sampling rate, in addition to others mentioned above, and consequently the total time spent at each measurement point increases even more dramatically. Further embodiments include using similar digital streaming of gap free waveform data as disclosed herein for use with wind turbines and other machines that can have relatively slow speed rotating or oscillating systems. In many examples, the waveform data collected can include long samples of data at a relatively high-sampling rate. In one example, the sampling rate can be 100 kHz and the sampling duration can be for two minutes on all of the channels being recorded. In many examples, one channel can be for the single axis reference sensor and three more data channels can be for the tri-axial three channel sensor. It will be appreciated in light of the disclosure that the long data length can be shown to facilitate detection of extremely low frequency phenomena. The long data length can also be shown to accommodate the inherent speed variability in wind turbine operations. Additionally, the long data length can further be shown to provide the opportunity for using numerous averages such as those discussed herein, to achieve very high spectral resolution, and to make feasible tape loops for certain spectral analyses. Many multiple advanced analytical techniques can now become available because such techniques can use the available long uninterrupted length of waveform data in accordance with the present disclosure.

[0654] It will also be appreciated in light of the disclosure that the simultaneous collection of waveform data from multiple channels can facilitate performing transfer functions between multiple channels. Moreover, the simultaneous collection of waveform data from multiple channels facilitates establishing phase relationships across the machine so that more sophisticated correlations can be utilized by relying on the fact that the waveforms from each of the channels are collected simultaneously. In other examples, more channels in the data collection can be used to reduce the time it takes to complete the overall vibration survey by allowing for simultaneous acquisition of waveform data from multiple sensors that otherwise would have to be acquired, in a subsequent fashion, moving sensor to sensor in the vibration survey.

[0655] The present disclosure includes the use of at least one of the single-axis reference probe on one of the channels to allow for acquisition of relative phase comparisons between channels. The reference probe can be an accelerometer or other type of transducer that is not moved and, therefore, fixed at an unchanging location during the vibration survey of one machine. Multiple reference probes can each be deployed as at suitable locations fixed in place (i.e., at unchanging locations) throughout the acquisition of vibration data during the vibration survey. In certain examples, up to seven reference probes can be deployed depending on the capacity of the data collection module 2160 or the like. Using transfer functions or similar techniques, the relative phases of all channels may be compared with one another at all selected frequencies. By keeping the one or more reference probes fixed at their unchanging locations while moving or monitoring the other tri-axial vibration sensors, it can be shown that the entire machine can be mapped with regard to amplitude and relative phase. This can be shown to be true even when there are more measurement points than channels of data collection. With this information, an operating deflection shape can be created that can show dynamic movements of the machine in 3 D, which can provide an invaluable diagnostic tool. In embodiments, the one or more reference probes can provide relative phase, rather than absolute phase. It will be appreciated in light of the disclosure that relative phase may not be as valuable absolute phase for some purposes, but the relative phase the information can still be shown to be very useful.

[0656] In embodiments, the sampling rates used during the vibration survey can be digitally synchronized to predetermined operational frequencies that can relate to pertinent parameters of the machine such as rotating or oscillating speed. Doing this, permits extracting even more information using synchronized averaging techniques. It will be appreciated in light of the disclosure that this can be done without the use of a key phasor or a reference pulse from a rotating shaft, which is usually not available for route collected data. As such, non-synchronous signals can be removed from a complex signal without the need to deploy synchronous averaging using the key phasor. This can be shown to be very powerful when analyzing a particular pinion in a gearbox or generally applied to any component within a complicated mechanical mechanism. In many instances, the key phasor or the reference pulse is rarely available with route collected data, but the techniques disclosed herein can overcome this absence. In embodiments, there can be multiple shafts running at different speeds within the machine being analyzed. In certain instances, there can be a single-axis reference probe for each shaft. In other instances, it is possible to relate the phase of one shaft to another shaft using only one single axis reference probe on one shaft at its unchanging location. In embodiments, variable speed equipment can be more readily analyzed with relatively longer duration of data relative to single speed equipment. The vibration survey can be conducted at several machine speeds within the same contiguous set of vibration data using the same techniques disclosed herein. These techniques can also permit the study of the change of the relationship between vibration and the change of the rate of speed that was not available before.

[0657] In embodiments, there are numerous analytical techniques that can emerge from because raw waveform data can be captured in a gap-free digital format as disclosed herein. The gap-free digital format can facilitate many paths to analyze the waveform data in many ways after the fact to identify specific problems. The vibration data collected in accordance with the techniques disclosed herein can provide the analysis of transient, semi-periodic and very low frequency phenomena. The waveform data acquired in accordance with the present disclosure can contain relatively longer streams of raw gap-free waveform data that can be conveniently played back as needed, and on which many and varied sophisticated analytical techniques can be performed. A large number of such techniques can provide for various forms of filtering to extract low amplitude modulations from transient impact data that can be included in the relatively longer stream of raw gap-free waveform data. It will be appreciated in light of the disclosure that in past data collection practices, these types of phenomena were typically lost by the averaging process of the spectral processing algorithms because the goal of the previous data acquisition module was purely periodic signals; or these phenomena were lost to file size reduction methodologies due to the fact that much of the content from an original raw signal was typically discarded knowing it would not be used.

[0658] In embodiments, there is a method of monitoring vibration of a machine having at least one shaft supported by a set of bearings. The method includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method also includes monitoring a second, third, and fourth data channel assigned to a three-axis sensor. The method further includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation; and determining a change in relative phase based on the digital waveform data. The method also includes the tri-axial sensor being located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors on all of their channels simultaneously.

[0659] The method also includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location of the reference sensor is a position associated with a shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with a shaft of the machine and, wherein, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine. The various embodiments include methods of sequentially monitoring vibration or similar process parameters and signals of a rotating or oscillating machine or analogous process machinery from a number of channels simultaneously, which can be known as an ensemble. In various examples, the ensemble can include one to eight channels. In further examples, an ensemble can represent a logical measurement grouping on the equipment being monitored whether those measurement locations are temporary for measurement, supplied by the original equipment manufacturer, retrofit at a later date, or one or more combinations thereof.

[0660] In one example, an ensemble can monitor bearing vibration in a single direction. In a further example, an ensemble can monitor three different directions (e.g., orthogonal directions) using a tri-axial sensor. In yet further examples, an ensemble can monitor four or more channels where the first channel can monitor a single axis vibration sensor, and the second, the third, and the fourth channels can monitor each of the three directions of the tri-axial sensor. In other examples, the ensemble can be fixed to a group of adjacent bearings on the same piece of equipment or an associated shaft. The various embodiments provide methods that include strategies for collecting waveform data from various ensembles deployed in vibration studies or the like in a relatively more efficient manner. The methods also include simultaneously monitoring of a reference channel assigned to an unchanging reference location associated with the ensemble monitoring the machine. The cooperation with the reference channel can be shown to support a more complete correlation of the collected waveforms from the ensembles. The reference sensor on the reference channel can be a single axis vibration sensor, or a phase reference sensor that can be triggered by a reference location on a rotating shaft or the like. As disclosed herein, the methods can further include recording gap-free digital waveform data simultaneously from all of the channels of each ensemble at a relatively high rate of sampling so as to include all frequencies deemed necessary for the proper analysis of the machinery being monitored while it is in operation. The data from the ensembles can be streamed gap-free to a storage medium for subsequent processing that can be connected to a cloud network facility, a local data link, Bluetooth™ connectivity, cellular data connectivity, or the like.

[0661] In embodiments, the methods disclosed herein include strategies for collecting data from the various ensembles including digital signal processing techniques that can be subsequently applied to data from the ensembles to emphasize or better isolate specific frequencies or waveform phenomena. This can be in contrast with current methods that collect multiple sets of data at different sampling rates, or with different hardware filtering configurations including integration, that provide relatively less post-processing flexibility because of the commitment to these same (known as a priori hardware configurations). These same hardware configurations can also be shown to increase time of the vibration survey due to the latency delays associated with configuring the hardware for each independent test. In embodiments, the methods for collecting data from various ensembles include data marker technology that can be used for classifying sections of streamed data as homogenous and belonging to a specific ensemble. In one example, a classification can be defined as operating speed. In doing so, a multitude of ensembles can be created from what conventional systems would collect as only one. The many embodiments include post-processing analytic techniques for comparing the relative phases of all the frequencies of interest not only between each channel of the collected ensemble but also between all of the channels of all of the ensembles being monitored, when applicable.

[0662] The present disclosure can include markers that can be applied to a time mark or a sample length within the raw waveform data. The markers generally fall into two categories: preset or dynamic. The preset markers can correlate to preset or existing operating conditions (e.g., load, head pressure, air flow cubic feet per minute, ambient temperature, RPMs, and the like.). These preset markers can be fed into the data acquisition system directly. In certain instances, the preset markers can be collected on data channels in parallel with the waveform data (e.g., waveforms for vibration, current, voltage, etc.). Alternatively, the values for the preset markers can be entered manually.

[0663] For dynamic markers such as trending data, it can be important to compare similar data like comparing vibration amplitudes and patterns with a repeatable set of operating parameters. One example of the present disclosure includes one of the parallel channel inputs being a key phasor trigger pulse from an operating shaft that can provide RPM information at the instantaneous time of collection. In this example of dynamic markers, sections of collected waveform data can be marked with appropriate speeds or speed ranges.

[0664] The present disclosure can also include dynamic markers that can correlate to data that can be derived from post processing and analytics performed on the sample waveform. In further embodiments, the dynamic markers can also correlate to post-collection derived parameters including RPMs, as well as other operationally derived metrics such as alarm conditions like a maximum RPM. In certain examples, many modem pieces of equipment that are candidates for a vibration survey with the portable data collection systems described herein do not include tachometer information. This can be true because it is not always practical or cost-justifiable to add a tachometer even though the measurement of RPM can be of primary importance for the vibration survey and analysis. It will be appreciated that for fixed speed machinery obtaining an accurate RPM measurement can be less important especially when the approximate speed of the machine can be ascertained before-hand; however, variable-speed drives are becoming more and more prevalent. It will also be appreciated in light of the disclosure that various signal processing techniques can permit the derivation of RPM from the raw data without the need for a dedicated tachometer signal.

[0665] In many embodiments, the RPM information can be used to mark segments of the raw waveform data over its collection history. Further embodiments include techniques for collecting instrument data following a prescribed route of a vibration study. The dynamic markers can enable analysis and trending software to utilize multiple segments of the collection interval indicated by the markers (e g., two minutes) as multiple historical collection ensembles, rather than just one as done in previous systems where route collection systems would historically store data for only one RPM setting. This could, in turn, be extended to any other operational parameter such as load setting, ambient temperature, and the like, as previously described. The dynamic markers, however, that can be placed in a type of index file pointing to the raw data stream can classify portions of the stream in homogenous entities that can be more readily compared to previously collected portions of the raw data stream

[0666] The many embodiments include the hybrid relational metadata-binary storage approach that can use the best of pre-existing technologies for both relational and raw data streams. In embodiments, the hybrid relational metadata - binary storage approach can marry them together with a variety of marker linkages. The marker linkages can permit rapid searches through the relational metadata and can allow for more efficient analyses of the raw data using conventional SQL techniques with pre-existing technology. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional database technologies do not provide.

[0667] The marker linkages can also permit rapid and efficient storage of the raw data using conventional binary storage and data compression techniques. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional raw data technologies provide such as TMDS (National Instruments), UFF (Universal File Format such as UFF58), and the like. The marker linkages can further permit using the marker technology links where a vastly richer set of data from the ensembles can be amassed in the same collection time as more conventional systems. The richer set of data from the ensembles can store data snapshots associated with predetermined collection criterion and the proposed system can derive multiple snapshots from the collected data streams utilizing the marker technology. In doing so, it can be shown that a relatively richer analysis of the collected data can be achieved. One such benefit can include more trending points of vibration at a specific frequency or order of running speed versus RPM, load, operating temperature, flow rates, and the like, which can be collected for a similar time relative to what is spent collecting data with a conventional system.

[0668] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines, elements of the machines and the environment of the machines including heavy duty machines deployed at a local job site or at distributed job sites under common control. The heavy-duty machines may include earthmoving equipment, heavy duty on-road industrial vehicles, heavy duty off-road industrial vehicles, industrial machines deployed in various settings such as turbines, turbomachinery, generators, pumps, pulley systems, manifold and valve systems, and the like. In embodiments, heavy industrial machinery may also include earth-moving equipment, earth-compacting equipment, hauling equipment, hoisting equipment, conveying equipment, aggregate production equipment, equipment used in concrete construction, and piledriving equipment. In examples, earth moving equipment may include excavators, backhoes, loaders, bulldozers, skid steer loaders, trenchers, motor graders, motor scrapers, crawler loaders, and wheeled loading shovels. In examples, construction vehicles may include dumpers, tankers, tippers, and trailers. In examples, material handling equipment may include cranes, conveyors, forklift, and hoists. In examples, construction equipment may include tunnel and handling equipment, road rollers, concrete mixers, hot mix plants, road making machines (compactors), stone crashers, pavers, slurry seal machines, spraying and plastering machines, and heavy-duty pumps. Further examples of heavy industrial equipment may include different systems such as implement traction, structure, power train, control, and information. Heavy industrial equipment may include many different powertrains and combinations thereof to provide power for locomotion and to also provide power to accessories and onboard functionality. In each of these examples, the platform 100 may deploy the local data collection system 102 into the environment 104 in which these machines, motors, pumps, and the like, operate and directly connected integrated into each of the machines, motors, pumps, and the like.

[0669] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines in operation and machines in being constructed such as turbine and generator sets like Siemens™ SGT6-5000F™ gas turbine, an SST-900™ steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator, and the like. In embodiments, the local data collection system 102 may be deployed to monitor steam turbines as they rotate in the currents caused by hot water vapor that may be directed through the turbine but otherwise generated from a different source such as from gas-fired burners, nuclear cores, molten salt loops and the like. In these systems, the local data collection system 102 may monitor the turbines and the water or other fluids in a closed loop cycle in which water condenses and is then heated until it evaporates again. The local data collection system 102 may monitor the steam turbines separately from the fuel source deployed to heat the water to steam. In examples, working temperatures of steam turbines may be between 500 and 650 °C. In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement.

[0670] The local data collection system 102 may also be deployed in a gas turbines arrangement and therefore not only monitor the turbine in operation but also monitor the hot combustion gases feed into the turbine that may be in excess of 1,500 °C. Because these gases are much hotter than those in steam turbines, the blades may be cooled with air that may flow out of small openings to create a protective film or boundary layer between the exhaust gases and the blades. This temperature profile may be monitored by the local data collection system 102. Gas turbine engines, unlike typical steam turbines, include a compressor, a combustion chamber, and a turbine all of which are journaled for rotation with a rotating shaft. The construction and operation of each of these components may be monitored by the local data collection system 102.

[0671] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from water turbines serving as rotary engines that may harvest energy from moving water and are used for electric power generation. The type of water turbine or hydro-power selected for a project may be based on the height of standing water, often referred to as head, and the flow (or volume of water) at the site. In this example, a generator may be placed at the top of a shaft that connects to the water turbine. As the turbine catches the naturally moving water in its blade and rotates, the turbine sends rotational power to the generator to generate electrical energy. In doing so, the platform 100 may monitor signals from the generators, the turbines, the local water system, flow controls such as dam windows and sluices. Moreover, the platform 100 may monitor local conditions on the electric grid including load, predicted demand, frequency response, and the like, and include such information in the monitoring and control deployed by platform 100 in these hydroelectric settings.

[0672] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources. In embodiments, elements in such systems may include transmission lines, heat exchangers, desulphurization scrubbers, pumps, coolers, recuperators, chillers, and the like. In embodiments, certain implementations of turbomachinery, turbines, scroll compressors, and the like may be configured in arrayed control so as to monitor large facilities creating electricity for consumption, providing refrigeration, creating steam for local manufacture and heating, and the like, and that arrayed control platforms may be provided by the provider of the industrial equipment such as Honeywell and their Experion™ PKS platform. In embodiments, the platform 100 may specifically communicate with and integrate the local manufacturer-specific controls and may allow equipment from one manufacturer to communicate with other equipment. Moreover, the platform 100 provides allows for the local data collection system 102 to collect information across systems from many different manufacturers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from marine industrial equipment, marine diesel engines, shipbuilding, oil and gas plants, refineries, petrochemical plant, ballast water treatment solutions, marine pumps and turbines, and the like.

[0673] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from heavy industrial equipment and processes including monitoring one or more sensors. By way of this example, sensors may be devices that may be used to detect or respond to some type of input from a physical environment, such as an electrical, heat, or optical signal. In embodiments, the local data collection system 102 may include multiple sensors such as, without limitation, a temperature sensor, a pressure sensor, a torque sensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, a radiation sensor, a position sensor, an acceleration sensor, a strain sensor, a pressure cycle sensor, a pressure sensor, an air temperature sensor, and the like. The torque sensor may encompass a magnetic twist angle sensor. In one example, the torque and speed sensors in the local data collection system 102 may be similar to those discussed in U.S. Patent Number 8,352,149 to Meachem, issued 8 January 2013 and hereby incorporated by reference as if fully set forth herein. In embodiments, one or more sensors may be provided such as a tactile sensor, a biosensor, a chemical sensor, an image sensor, a humidity sensor, an inertial sensor, and the like.

[0674] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors that may provide signals for fault detection including excessive vibration, incorrect material, incorrect material properties, trueness to the proper size, trueness to the proper shape, proper weight, trueness to balance. Additional fault sensors include those for inventory control and for inspections such as to confirm that parts are packaged to plan, parts are to tolerance in a plan, occurrence of packaging damage or stress, and sensors that may indicate the occurrence of shock or damage in transit. Additional fault sensors may include detection of the lack of lubrication, over lubrication, the need for cleaning of the sensor detection window, the need for maintenance due to low lubrication, the need for maintenance due to blocking or reduced flow in a lubrication region, and the like.

[0675] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 that includes aircraft operations and manufacture including monitoring signals from sensors for specialized applications such as sensors used in an aircraft’s Attitude and Heading Reference System (AHRS), such as gyroscopes, accelerometers, and magnetometers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from image sensors such as semiconductor charge coupled devices (CCDs), active pixel sensors, in complementary metal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as an infra-red (IR) sensor, an ultraviolet (UV) sensor, a touch sensor, a proximity sensor, and the like. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for optical character recognition (OCR), reading barcodes, detecting surface acoustic waves, detecting transponders, communicating with home automation systems, medical diagnostics, health monitoring, and the like.

[0676] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as a Micro-ElectroMechanical Systems (MEMS) sensor, such as ST Microelectronic’s™ LSM303AH smart MEMS sensor, which may include an ultra-low-power high-performance system-in-package featuring a 3D digital linear acceleration sensor and a 3D digital magnetic sensor.

[0677] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines such as turbines, windmills, industrial vehicles, robots, and the like. These large mechanical machines include multiple components and elements providing multiple subsystems on each machine. To that end, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from individual elements such as axles, bearings, belts, buckets, gears, shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets, sleeves, valves, wheels, actuators, motors, servomotor, and the like. Many of the machines and their elements may include servomotors. The local data collection system 102 may monitor the motor, the rotary encoder, and the potentiometer of the servomechanism to provide three-dimensional detail of position, placement, and progress of industrial processes.

[0678] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from gear drives, powertrains, transfer cases, multispeed axles, transmissions, direct drives, chain drives, belt-drives, shaft-drives, magnetic drives, and similar meshing mechanical drives. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from fault conditions of industrial machines that may include overheating, noise, grinding gears, locked gears, excessive vibration, wobbling, under-inflation, over-inflation, and the like. Operation faults, maintenance indicators, and interactions from other machines may cause maintenance or operational issues may occur during operation, during installation, and during maintenance. The faults may occur in the mechanisms of the industrial machines but may also occur in infrastructure that supports the machine such as its wiring and local installation platforms. In embodiments, the large industrial machines may face different types of fault conditions such as overheating, noise, grinding gears, excessive vibration of machine parts, fan vibration problems, problems with large industrial machines rotating parts.

[0679] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from industrial machinery including failures that may be caused by premature bearing failure that may occur due to contamination or loss of bearing lubricant. In another example, a mechanical defect such as misalignment of bearings may occur. Many factors may contribute to the failure such as metal fatigue, therefore, the local data collection system 102 may monitor cycles and local stresses. By way of this example, the platform 100 may monitor the incorrect operation of machine parts, lack of maintenance and servicing of parts, corrosion of vital machine parts, such as couplings or gearboxes, misalignment of machine parts, and the like. Though the fault occurrences cannot be completely stopped, many industrial breakdowns may be mitigated to reduce operational and financial losses. The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments wherein it has been shown to present a costsavings over regularly-scheduled maintenance processes that replace parts according to a rigid expiration of time and not actual load and wear and tear on the element or machine. To that end, the platform 10 may provide reminders of, or perform some, preventive measures such as adhering to operating manual and mode instructions for machines, proper lubrication, and maintenance of machine parts, minimizing or eliminating overrun of machines beyond their defined capacities, replacement of worn but still functional parts as needed, properly training the personnel formachine use, and the like.

[0680] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by a plurality of physical, electronic, and symbolic formats or signals. The platform 100 may employ signal processing including a plurality of mathematical, statistical, computational, heuristic, and linguistic representations and processing of signals and a plurality of operations needed for extraction of useful information from signal processing operations such as techniques for representation, modeling, analysis, synthesis, sensing, acquisition, and extraction of information from signals. In examples, signal processing may be performed using a plurality of techniques, including but not limited to transformations, spectral estimations, statistical operations, probabilistic and stochastic operations, numerical theory analysis, data mining, and the like. The processing of various types of signals forms the basis of many electrical or computational process. As a result, signal processing applies to almost all disciplines and applications in the industrial environment such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, quality improvements such as noise reduction, image enhancement, and the like. Signal processing for images may include pattern recognition for manufacturing inspections, quality inspection, and automated operational inspection and maintenance. The platform 100 may employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations and may be used in applications such as computer vision, speech and text processing, radar processing, handwriting recognition, CAD systems, and the like. The platform 100 may employ supervised classification and unsupervised classification. The supervised learning classification algorithms may be based to create classifiers for image or pattern recognition, based on training data obtained from different object classes. The unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering. For example, some of the analysis techniques used in unsupervised learning may include K-means clustering, Gaussian mixture models, Hidden Markov models, and the like. The algorithms used in supervised and unsupervised learning methods of pattern recognition enable the use of pattern recognition in various high precision applications. The platform 100 may use pattern recognition in face detection related applications such as security systems, tracking, sports related applications, fingerprint analysis, medical and forensic applications, navigation and guidance systems, vehicle tracking, public infrastructure systems such as transport systems, license plate monitoring, and the like.

[0681] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 using machine learning to enable derivation-based learning outcomes from computers without the need to program them. The platform 100 may, therefore, leam from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include presenting a set of example inputs and desired outputs to the machine learning systems. Unsupervised learning may include the learning algorithm itself structuring its input by methods such as pattern detection and / or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions. In examples, machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), a priori algorithms, and the like. In embodiments, certain machine learning algorithms may be used (such as genetic algorithms defined for solving both constrained and unconstrained optimization problems that may be based on natural selection, the process that drives biological evolution). By way of this example, genetic algorithms may be deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. In an example, the genetic algorithm may be used to address problems of mixed integer programming, where some components restricted to being integer-valued. Genetic algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. By way of this example, the machine learning systems may be used to perform intelligent computing based control and be responsive to tasks in a wide variety of systems (such as interactive websites and portals, brain-machine interfaces, online security and fraud detection systems, medical applications such as diagnosis and therapy assistance systems, classification of DNA sequences, and the like). In examples, machine learning systems may be used in advanced computing applications (such as online advertising, natural language processing, robotics, search engines, software engineering, speech and handwriting recognition, pattern matching, game playing, computational anatomy, bioinformatics systems and the like). In an example, machine learning may also be used in financial and marketing systems (such as for user behavior analytics, online advertising, economic estimations, financial market analysis, and the like).

[0682] Additional details are provided below in connection with the methods, systems, devices, and components depicted in connection with Figures 1 through 6. In embodiments, methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof. A model may be created by a human who has experience with the industrial environment and may be associated with a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like). The machine learning facility may take feedback, such as one or more inputs or measures of success, such that it may train, or improve, its initial model (such as improvements by adjusting weights, rules, parameters, or the like, based on the feedback). For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and / or consumed e.g., such as through combustion, through chemical reactions in battery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like). For example, if a resonance effect between two machines is adversely affecting one of them, the model may account for this and automatically provide an output that results in changing the operation of one of the machines (such as to reduce the resonance, to increase fuel efficiency of one or both machines). By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes.

[0683] Figure 10 illustrates components and interactions of a data collection architecture involving the application of cognitive and machine learning systems to data collection and processing. Referring to Figure 10, a data collection system 102 may be disposed in an environment (such as an industrial environment where one or more complex systems, such as electro-mechanical systems and machines are manufactured, assembled, oroperated). The data collection system 102 may include onboard sensors and may take input, such as through one or more input interfaces or ports 4008, from one or more sensors (such as analog or digital sensors of any type disclosed herein) and from one or more input sources 116 (such as sources that may be available through Wi-Fi, Bluetooth, NFC, or other local network connections or over the Internet). Sensors may be combined and multiplexed (such as with one or more multiplexers 4002). Data may be cached or buffered in a cache / buffer 4022 and made available to external systems, such as a remote host processing system 112 as described elsewhere in this disclosure (which may include an extensive processing architecture 4024, including any of the elements described in connection with other embodiments described throughout this disclosure and in the Figure), though one or more output interfaces and ports 4010 (which may in embodiments be separate from or the same as the input interfaces and ports 4008). The data collection system 102 may be configured to take input from a host processing system 112, such as input from an analytic system 4018, which may operate on data from the data collection system 102 and data from other input sources 116 to provide analytic results, which in turn may be provided as a learning feedback input 4012 to the data collection system, such as to assist in configuration and operation of the data collection system 102.

[0684] Combination of inputs (including selection of what sensors or input sources to turn “on” or “off’) may be performed under the control of machine-based intelligence, such as using a local cognitive input selection system 4004, an optionally remote cognitive input selection system 4114, or a combination of the two. The cognitive input selection systems 4004, 4014 may use intelligence and machine learning capabilities described elsewhere in this disclosure, such as using detected conditions (such as conditions informed by the input sources 116 or sensors), state information (including state information determined by a machine state recognition system 4020 that may determine a state), such as relating to an operational state, an environmental state, a state within a known process or workflow, a state involving a fault or diagnostic condition, or many others. This may include optimization of input selection and configuration based on learning feedback from the learning feedback system 4012, which may include providing training data (such as from the host processing system 112 or from other data collection systems 102 either directly or from the host 112) and may include providing feedback metrics, such as success metrics calculated within the analytic system 4018 of the host processing system 112. For example, if a data stream consisting of a particular combination of sensors and inputs yields positive results in a given set of conditions (such as providing improved pattern recognition, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, or the like), then metrics relating to such results from the analytic system 4018 can be provided via the learning feedback system 4012 to the cognitive input selection systems 4004, 4014 to help configure future data collection to select that combination in those conditions (allowing other input sources to be deselected, such as by powering down the other sensors). In embodiments, selection and deselection of sensor combinations, under control of one or more of the cognitive input selection systems 4004, may occur with automated variation, such as using genetic programming techniques, based on learning feedback 4012, such as from the analytic system 4018, effective combinations for a given state or set of conditions are promoted, and less effective combinations are demoted, resulting in progressive optimization and adaptation of the local data collection system to each unique environment. Thus, an automatically adapting, multisensor data collection system is provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results, energy production or usage, and the like), analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like), and local system metrics (such as bandwidth utilization, storage utilization, power consumption, and the like). In embodiments, the analytic system 4018, the state system 4020 and the cognitive input selection system 4114 of a host may take data from multiple data collection systems 102, such that optimization (including of input selection) may be undertaken through coordinated operation of multiple systems 102. For example, the cognitive input selection system 4114 may understand that if one data collection system 102 is already collecting vibration data for an X-axis, the X-axis vibration sensor for the other data collection system might be turned off, in favor of getting Y-axis data from the other data collector 102. Thus, through coordinated collection by the host cognitive input selection system 4114, the activity of multiple collectors 102, across ahost of different sensors, can provide for a rich data set for the host processing system 112, without wasting energy, bandwidth, storage space, or the like. As noted above, optimization may be based on overall system success metrics, analytic success metrics, and local system metrics, or a combination of the above.

[0685] Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. In embodiments, machine learning may take advantage of a state machine, such as tracking states of multiple analog and / or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on historical data about sequences of state information. For example, where a temperature state of an industrial machine exceeds a certain threshold and is followed by a fault condition, such as breaking down of a set of bearings, that temperature state may be tracked by a pattern recognizer, which may produce an output data structure indicating an anticipated bearing fault state (whenever an input state of a high temperature is recognized). A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, galvanic states, magnetic field states, electrical field states, capacitance states, charge and discharge states, motion, position, and many others. States may comprise combined states, where a data structure includes a series of states, each of which is represented by a place in a byte-like data structure. For example, an industrial machine may be characterized by a genetic structure, such as one that provides pressure, temperature, vibration, and acoustic data, the measurement of which takes one place in the data structure, so that the combined state can be operated on as a byte-like structure, such as a structure for compactly characterizing the current combined state of the machine or environment, or compactly characterizing the anticipated state. This byte-like structure can be used by a state machine for machine learning, such as pattern recognition that operates on the structure to determine patterns that reflect combined effects of multiple conditions. A wide variety of such structure can be tracked and used, such as in machine learning, representing various combinations, of various length, of the different elements that can be sensed in an industrial environment. In embodiments, byte-like structures can be used in a genetic programming technique, such as by substituting different types of data, or data from varying sources, and tracking outcomes over time, so that one or more favorable structures emerges based on the success of those structures when used in real world situations, such as indicating successful predictions of anticipated states, or achievement of success operational outcomes, such as increased efficiency, successful routing of information, achieving increased profits, or the like. That is, by varying what data types and sources are used in byte-like structures that are used for machine optimization over time, a genetic programming-based machine learning facility can “evolve” a set of data structures, consisting of a favorable mix of data types (e.g, pressure, temperature, and vibration), from a favorable mix of data sources (e.g., temperature is derived from sensor X, while vibration comes from sensor Y), for a given purpose. Different desired outcomes may result in different data structures that are best adapted to support effective achievement of those outcomes over time with application of machine learning and promotion of structures with favorable results for the desired outcome in question by genetic programming. The promoted data structures may provide compact, efficient data for various activities as described throughout this disclosure, including being stored in data pools (which may be optimized by storing favorable data structures that provide the best operational results for a given environment), being presented in data marketplaces (such as being presented as the most effective structures for a given purpose), and the like.

[0686] In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, the host processing system 112, such as disposed in the cloud, may include the state system 4020, which may be used to infer or calculate a current state or to determine an anticipated future state relating to the data collection system 102 or some aspect of the environment in which the data collection system 102 is disposed, such as the state of a machine, a component, a workflow, a process, an event (e.g., whether the event has occurred), an object, a person, a condition, a function, or the like. Maintaining state information allows the host processing system 112 to undertake analysis, such as in one or more analytic systems 4018, to determine contextual information, to apply semantic and conditional logic, and perform many other functions as enabled by the processing architecture 4024 described throughout this disclosure.

[0687] In embodiments, a platform is provided having cloud-based policy automation engine for loT, with creation, deployment, and management of loT devices. In embodiments, the platform 100 includes (or is integrated with, or included in) the host processing system 112, such as on a cloud platform, a policy automation engine 4032 for automating creation, deployment, and management of policies to loT devices. Polices, which may include access policies, network usage policies, storage usage policies, bandwidth usage policies, device connection policies, security policies, rule-based policies, role-based polices, and others, may be required to govern the use of loT devices. For example, as loT devices may have many different network and data communications to other devices, policies may be needed to indicate to what devices a given device can connect, what data can be passed on, and what data can be received. As billions of devices with countless potential connections are expected to be deployed in the near future, it becomes impossible for humans to configure policies for loT devices on a connection-by-connection basis. Accordingly, an intelligent policy automation engine 4032 may include cognitive features for creating, configuring, and managing policies. The policy automation engine 4032 may consume information about possible policies, such as from a policy database or library, which may include one or more public sources of available policies. These may be written in one or more conventional policy languages or scripts. The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation, thereby avoiding a remote “takeover” by a hacker. This may be accomplished in turn by automatically finding and applying security policies that bar connection of the control infrastructure of the machine to the Internet, by requiring access authentication, or the like. The policy automation engine 4032 may include cognitive features, such as varying the application of policies, the configuration of policies, and the like (such as features based on state information from the state system 4020). The policy automation engine 4032 may take feedback, as from the learning feedback system 4012, such as based on one or more analytic results from the analytic system 4018, such as based on overall system results (such as the extent of security breaches, policy violations, and the like), local results, and analytic results. By variation and selection based on such feedback, the policy automation engine 4032 can, overtime, learn to automatically create, deploy, configure, and manage policies across very large numbers of devices, such as managing policies for configuration of connections among loT devices.

[0688] Methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial loT devices, including on-device sensor fusion and data storage for an industrial loT device, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. For example, pressure and temperature data may be multiplexed into a data stream that combines pressure and temperature in a time series, such as in a byte-like structure (where time, pressure, and temperature are bytes in a data structure, so that pressure and temperature remain linked in time, without requiring separate processing of the streams by outside systems), or by adding, dividing, multiplying, subtracting, or the like, such that the fused data can be stored on the device. Any of the sensor data types described throughout this disclosure can be fused in this manner and stored in a local data pool, in storage, or on an loT device, such as a data collector, a component of a machine, or the like.

[0689] In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial loT devices. In embodiments, a cognitive system is used for a self-organizing storage system 4028 for the data collection system 102. Sensor data, and in particular analog sensor data, can consume large amounts of storage capacity, in particular where a data collector 102 has multiple sensor inputs onboard or from the local environment. Simply storing all the data indefinitely is not typically a favorable option, and even transmitting all of the data may strain bandwidth limitations, exceed bandwidth permissions (such as exceeding cellular data plan capacity), or the like. Accordingly, storage strategies are needed. These typically include capturing only portions of the data (such as snapshots), storing data for limited time periods, storing portions of the data (such as intermediate or abstracted forms), and the like. With many possible selections among these and other options, determining the correct storage strategy may be highly complex. In embodiments, the self-organizing storage system 4028 may use a cognitive system, based on learning feedback 4012, and use various metrics from the analytic system 4018 or other system of the host cognitive input selection system 4114, such as overall system metrics, analytic metrics, and local performance indicators. The self-organizing storage system 4028 may automatically vary storage parameters, such as storage locations (including local storage on the data collection system 102, storage on nearby data collection systems 102 (such as using peer-to-peer organization) and remote storage, such as network-based storage), storage amounts, storage duration, type of data stored (including individual sensors or input sources 116, as well as various combined or multiplexed data, such as selected under the cognitive input selection systems 4004, 4014), storage type (such as using RAM, Flash, or other short-term memory versus available hard drive space), storage organization (such as in raw form, in hierarchies, and the like), and others. Variation of the parameters may be undertaken with feedback, so that over time the data collection system 102 adapts its storage of data to optimize itself to the conditions of its environment, such as a particular industrial environment, in a way that results in its storing the data that is needed in the right amounts and of the right type for availability to users.

[0690] In embodiments, the local cognitive input selection system 4004 may organize fusion of data for various onboard sensors, external sensors (such as in the local environment) and other input sources 116 to the local collection system 102 into one or more fused data streams, such as using the multiplexer 4002 to create various signals that represent combinations, permutations, mixes, layers, abstractions, data-metadata combinations, and the like of the source analog and / or digital data that is handled by the data collection system 102. The selection of a particular fusion of sensors may be determined locally by the cognitive input selection system 4004, such as based on learning feedback from the learning feedback system 4012, such as various overall system, analytic system and local system results and metrics. In embodiments, the system may learn to fuse particular combinations and permutations of sensors, such as in order to best achieve correct anticipation of state, as indicated by feedback of the analytic system 4018 regarding its ability to predict future states, such as the various states handled by the state system 4020. For example, the input selection system 4004 may indicate selection of a sub-set of sensors among a larger set of available sensors, and the inputs from the selected sensors may be combined, such as by placing input from each of them into a byte of a defined, multi-bit data structure (such as a combination by taking a signal from each at a given sampling rate or time and placing the result into the byte structure, then collecting and processing the bytes over time), by multiplexing in the multiplexer 4002, such as a combination by additive mixing of continuous signals, and the like. Any of a wide range of signal processing and data processing techniques for combination and fusing may be used, including convolutional techniques, coercion techniques, transformation techniques, and the like. The particular fusion in question may be adapted to a given situation by cognitive learning, such as by having the cognitive input selection system 4004 leam, based on feedback 4012 from results (such as feedback conveyed by the analytic system 4018), such that the local data collection system 102 executes context-adaptive sensor fusion.

[0691] In embodiments, the analytic system 4018 may apply to any of a wide range of analytic techniques, including statistical and econometric techniques (such as linear regression analysis, use similarity matrices, heat map based techniques, and the like), reasoning techniques (such as Bayesian reasoning, rule-based reasoning, inductive reasoning, and the like), iterative techniques (such as feedback, recursion, feed-forward and other techniques), signal processing techniques (such as Fourier and other transforms), pattern recognition techniques (such as Kalman and other filtering techniques), search techniques, probabilistic techniques (such as random walks, random forest algorithms, and the like), simulation techniques (such as random walks, random forest algorithms, linear optimization and the like), and others. This may include computation of various statistics or measures. In embodiments, the analytic system 4018 may be disposed, at least in part, on a data collection system 102, such that a local analytic system can calculate one or more measures, such as measures relating to any of the items noted throughout this disclosure. For example, measures of efficiency, power utilization, storage utilization, redundancy, entropy, and other factors may be calculated onboard, so that the data collection 102 can enable various cognitive and learning functions noted throughout this disclosure without dependence on a remote (e.g., cloud-based) analytic system.

[0692] In embodiments, the host processing system 112, a data collection system 102, or both, may include, connect to, or integrate with, a self-organizing networking system 4020, which may comprise a cognitive system for providing machine-based, intelligent or organization of network utilization for transport of data in a data collection system, such as for handling analog and other sensor data, or other source data, such as among one or more local data collection systems 102 and a host system 112. This may include organizing network utilization for source data delivered to data collection systems, for feedback data, such as analytic data provided to or via a learning feedback system 4012, data for supporting a marketplace (such as described in connection with other embodiments), and output data provided via output interfaces and ports 4010 from one or more data collection systems 102.

[0693] Methods and systems are disclosed herein for a self-organizing data marketplace for industrial loT data, including where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. A marketplace may be set up initially to make available data collected from one or more industrial environments, such as presenting data by type, by source, by environment, by machine, by one or more patterns, or the like (such as in a menu or hierarchy). The marketplace may vary the data collected, the organization of the data, the presentation of the data (including pushing the data to external sites, providing links, configuring APIs by which the data may be accessed, and the like), the pricing of the data, or the like, such as under machine learning, which may vary different parameters of any of the foregoing. The machine learning facility may manage all of these parameters by selforganization, such as by varying parameters over time (including by varying elements of the data types presented), the data sourced used to obtain each type of data, the data structures presented (such as byte-like structures, fused or multiplexed structures (such as representing multiple sensor types), and statistical structures (such as representing various mathematical products of sensor information), among others), the pricing for the data, where the data is presented, how the data is presented (such as by APIs, by links, by push messaging, and the like), how the data is stored, how the data is obtained, and the like. As parameters are varied, feedback may be obtained as to measures of success, such as number of views, yield (e.g., price paid) per access, total yield, per unit profit, aggregate profit, and many others, and the selforganizing machine learning facility may promote configurations that improve measures of success and demote configurations that do not, so that, over time, the marketplace is progressively configured to present favorable combinations of data types (e.g., those that provide robust prediction of anticipated states of particular industrial environments of a given type), from favorable sources (e.g., those that are reliable, accurate and low priced), with effective pricing (e.g., pricing that tends to provide high aggregate profit from the marketplace). The marketplace may include spiders, web crawlers, and the like to seek input data sources, such as finding data pools, connected loT devices, and the like that publish potentially relevant data. These may be trained by human users and improved by machine learning in a manner similar to that described elsewhere in this disclosure.

[0694] In embodiments, a platform is provided having a self-organizing data marketplace for industrial loT data. Referring to Figure 11, in embodiments, a platform is provided having a cognitive data marketplace 4102, referred to in some cases as a self-organizing data marketplace, for data collected by one or more data collection systems 102 or for data from other sensors or input sources 116 that are located in various data collection environments, such as industrial environments. In addition to data collection systems 102, this may include data collected, handled or exchanged by loT devices, such as cameras, monitors, embedded sensors, mobile devices, diagnostic devices and systems, instrumentation systems, telematics systems, and the like, such as for monitoring various parameters and features of machines, devices, components, parts, operations, functions, conditions, states, events, workflows and other elements (collectively encompassed by the term “states”) of such environments. Data may also include metadata about any of the foregoing, such as describing data, indicating provenance, indicating elements relating to identity, access, roles, and permissions, providing summaries or abstractions of data, or otherwise augmenting one or more items of data to enable further processing, such as for extraction, transforming, loading, and processing data. Such data (such term including metadata except where context indicates otherwise) may be highly valuable to third parties, either as an individual element (such as the instance where data about the state of an environment can be used as a condition within a process) or in the aggregate (such as the instance where collected data, optionally over many systems and devices in different environments can be used to develop models of behavior, to train learning systems, or the like). As billions of loT devices are deployed, with countless connections, the amount of available data will proliferate. To enable access and utilization of data, the cognitive data marketplace 4102 enables various components, features, services, and processes for enabling users to supply, find, consume, and transact in packages of data, such as batches of data, streams of data (including event streams), data from various data pools 4120, and the like. In embodiments, the cognitive data marketplace 4102 may be included in, connected to, or integrated with, one or more other components of a host processing architecture 4024 of a host processing system 112, such as a cloud-based system, as well as to various sensors, input sources 115, data collection systems 102 and the like. The cognitive data marketplace 4102 may include marketplace interfaces 4108, which may include one or more supplier interfaces by which data suppliers may make data available and one more consumer interfaces by which data may be found and acquired. The consumer interface may include an interface to a data market search system 4118, which may include features that enable a user to indicate what types of data a user wishes to obtain, such as by entering keywords in a natural language search interface that characterize data or metadata. The search interface can use various search and filtering techniques, including keyword matching, collaborative filtering (such as using known preferences or characteristics of the consumer to match to similar consumers and the past outcomes of those other consumers), ranking techniques (such as ranking based on success of past outcomes according to various metrics, such as those described in connection with other embodiments in this disclosure). In embodiments, a supply interface may allow an owner or supplier of data to supply the data in one or more packages to and through the cognitive data marketplace 4102, such as packaging batches of data, streams of data, or the like. The supplier may pre-package data, such as by providing data from a single input source 116, a single sensor, and the like, or by providing combinations, permutations, and the like (such as multiplexed analog data, mixed bytes of data from multiple sources, results of extraction, loading and transformation, results of convolution, and the like), as well as by providing metadata with respect to any of the foregoing. Packaging may include pricing, such as on a per-batch basis, on a streaming basis (such as subscription to an event feed or other feed or stream), on a per item basis, on a revenue share basis, or other basis. For data involving pricing, a data transaction system 4114 may track orders, delivery, and utilization, including fulfillment of orders. The transaction system 4114 may include rich transaction features, including digital rights management, such as by managing cryptographic keys that govern access control to purchased data, that govern usage (such as allowing data to be used for a limited time, in a limited domain, by a limited set of users or roles, or for a limited purpose). The transaction system 4114 may manage payments, such as by processing credit cards, wire transfers, debits, and other forms of consideration.

[0695] In embodiments, a cognitive data packaging system 4012 of the marketplace 4102 may use machine-based intelligence to package data, such as by automatically configuring packages of data in batches, streams, pools, or the like. In embodiments, packaging may be according to one or more rules, models, or parameters, such as by packaging or aggregating data that is likely to supplement or complement an existing model. For example, operating data from a group of similar machines (such as one or more industrial machines noted throughout this disclosure) may be aggregated together, such as based on metadata indicating the type of data or by recognizing features or characteristics in the data stream that indicate the nature of the data. In embodiments, packaging may occur using machine learning and cognitive capabilities, such as by learning what combinations, permutations, mixes, layers, and the like of input sources 116, sensors, information from data pools 4120 and information from data collection systems 102 are likely to satisfy user requirements or result in measures of success. Learning may be based on learning feedback 4012, such as learning based on measures determined in an analytic system 4018, such as system performance measures, data collection measures, analytic measures, and the like. In embodiments, success measures may be correlated to marketplace success measures, such as viewing of packages, engagement with packages, purchase or licensing of packages, payments made for packages, and the like. Such measures may be calculated in an analytic system 4018, including associating particular feedback measures with search terms and other inputs, so that the cognitive packaging system 4110 can find and configure packages that are designed to provide increased value to consumers and increased returns for data suppliers. In embodiments, the cognitive data packaging system 4110 can automatically vary packaging, such as using different combinations, permutations, mixes, and the like, and varying weights applied to given input sources, sensors, data pools and the like, using learning feedback 4012 to promote favorable packages and de-emphasize less favorable packages. This may occur using genetic programming and similar techniques that compare outcomes for different packages. Feedback may include state information from the state system 4020 (such as about various operating states, and the like), as well as about marketplace conditions and states, such as pricing and availability information for other data sources. Thus, an adaptive cognitive data packaging system 4110 is provided that automatically adapts to conditions to provide favorable packages of data for the marketplace 4102.

[0696] In embodiments, a cognitive data pricing system 4112 may be provided to set pricing for data packages. In embodiments, the data pricing system 4112 may use a set of rules, models, or the like, such as setting pricing based on supply conditions, demand conditions, pricing of various available sources, and the like. For example, pricing for a package may be configured to be set based on the sum of the prices of constituent elements (such as input sources, sensor data, or the like), or to be set based on a rule-based discount to the sum of prices for constituent elements, or the like. Rules and conditional logic may be applied, such as rules that factor in cost factors (such as bandwidth and network usage, peak demand factors, scarcity factors, and the like), rules that factor in utilization parameters (such as the purpose, domain, user, role, duration, or the like for a package) and many others. In embodiments, the cognitive data pricing system 4112 may include fully cognitive, intelligent features, such as using genetic programming including automatically varying pricing and tracking feedback on outcomes. Outcomes on which tracking feedback may be based include various financial yield metrics, utilization metrics and the like that may be provided by calculating metrics in an analytic system 4018 on data from the data transaction system 4114.

[0697] Methods and systems are disclosed herein for self-organizing data pools which may include self-organization of data pools based on utilization and / or yield metrics, including utilization and / or yield metrics that are tracked for a plurality of data pools. The data pools may initially comprise unstructured or loosely structured pools of data that contain data from industrial environments, such as sensor data from or about industrial machines or components. For example, a data pool might take streams of data from various machines or components in an environment, such as turbines, compressors, batteries, reactors, engines, motors, vehicles, pumps, rotors, axles, bearings, valves, and many others, with the data streams containing analog and / or digital sensor data (of a wide range of types), data published about operating conditions, diagnostic and fault data, identifying data for machines or components, asset tracking data, and many other types of data. Each stream may have an identifier in the pool, such as indicating its source, and optionally its type. The data pool may be accessed by external systems, such as through one or more interfaces or APIs (e.g., RESTful APIs), or by data integration elements (such as gateways, brokers, bridges, connectors, or the like), and the data pool may use similar capabilities to get access to available data streams. A data pool may be managed by a self-organizing machine learning facility, which may configure the data pool, such as by managing what sources are used for the pool, managing what streams are available, and managing APIs or other connections into and out of the data pool. The self-organization may take feedback such as based on measures of success that may include measures of utilization and yield. The measures of utilization and yield that may include may account for the cost of acquiring and / or storing data, as well as the benefits of the pool, measured either by profit or by other measures that may include user indications of usefulness, and the like. For example, a self-organizing data pool might recognize that chemical and radiation data for an energy production environment are regularly accessed and extracted, while vibration and temperature data have not been used, in which case the data pool might automatically reorganize, such as by ceasing storage of vibration and / or temperature data, or by obtaining better sources of such data. This automated reorganization can also apply to data structures, such as promoting different data types, different data sources, different data structures, and the like, through progressive iteration and feedback.

[0698] In embodiments, a platform is provided having self-organization of data pools based on utilization and / or yield metrics. In embodiments, the data pools 4020 may be self-organizing data pools 4020, such as being organized by cognitive capabilities as described throughout this disclosure. The data pools 4020 may self-organize in response to learning feedback 4012, such as based on feedback of measures and results, including calculated in an analytic system 4018. Organization may include determining what data or packages of data to store in a pool (such as representing particular combinations, permutations, aggregations, and the like), the structure of such data (such as in flat, hierarchical, linked, or other structures), the duration of storage, the nature of storage media (such as hard disks, flash memory, SSDs, network-based storage, or the like), the arrangement of storage bits, and other parameters. The content and nature of storage may be varied, such that a data pool 4020 may learn and adapt, such as based on states of the host system 112, one or more data collection systems 102, storage environment parameters (such as capacity, cost, and performance factors), data collection environment parameters, marketplace parameters, and many others. In embodiments, pools 4020 may learn and adapt, such as by variation of the above and other parameters in response to yield metrics (such as return on investment, optimization of power utilization, optimization of revenue, and the like).

[0699] Methods and systems are disclosed herein for training AI models based on industryspecific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, and where the AI model operates on sensor data from an industrial environment. As noted above, these models may include operating models for industrial environments, machines, workflows, models for anticipating states, models for predicting fault and optimizing maintenance, models for self-organizing storage (on devices, in data pools and / or in the cloud), models for optimizing data transport (such as for optimizing network coding, network-condition-sensitive routing, and the like), models for optimizing data marketplaces, and many others.

[0700] In embodiments, a platform is provided having training AI models based on industryspecific feedback. In embodiments, the various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources 116 (such as relating to optimization of specific machines, devices, components, processes, and the like). Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features (such as for a marketplace 4102 or for other purposes of the host processing system 112) may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving loT devices (such as an industrial environment). This may include optimization of efficiency (such as in electrical, electromechanical, magnetic, physical, thermodynamic, chemical and other processes and systems), optimization of outputs (such as for production of energy, materials, products, services and other outputs), prediction, avoidance and mitigation of faults (such as in the aforementioned systems and processes), optimization of performance measures (such as returns on investment, yields, profits, margins, revenues and the like), reduction of costs (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, and many others), optimization of benefits (such as relating to safety, satisfaction, health), optimization of work flows (such as optimizing time and resource allocation to processes), and others.

[0701] Methods and systems are disclosed herein for a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm. Each member of the swarm may be configured with intelligence, and the ability to coordinate with other members. For example, a member of the swarm may track information about what data other members are handling, so that data collection activities, data storage, data processing, and data publishing can be allocated intelligently across the swarm, taking into account conditions of the environment, capabilities of the members of the swarm, operating parameters, rules (such as from a rules engine that governs the operation of the swarm), and current conditions of the members. For example, among four collectors, one that has relatively low current power levels (such as a low battery), might be temporarily allocated the role of publishing data, because it may receive a dose of power from a reader or interrogation device (such as an RFID reader) when it needs to publish the data. A second collector with good power levels and robust processing capability might be assigned more complex functions, such as processing data, fusing data, organizing the rest of the swarm (including self-organization under machine learning, such that the swarm is optimized over time, including by adjusting operating parameters, rules, and the like based on feedback), and the like. A third collector in the swarm with robust storage capabilities might be assigned the task of collecting and storing a category of data, such as vibration sensor data, that consumes considerable bandwidth. A fourth collector in the swarm, such as one with lower storage capabilities, might be assigned the role of collecting data that can usually be discarded, such as data on current diagnostic conditions, where only data on faults needs to be maintained and passed along. Members of a swarm may connect by peer-to-peer relationships by using a member as a “master” or “hub,” or by having them connect in a series or ring, where each member passes along data (including commands) to the next, and is aware of the nature of the capabilities and commands that are suitable for the preceding and / or next member. The swarm may be used for allocation of storage across it (such as using memory of each memory as an aggregate data store. In these examples, the aggregate data store may support a distributed ledger, which may store transaction data, such as for transactions involving data collected by the swarm, transactions occurring in the industrial environment, or the like. In embodiments, the transaction data may also include data used to manage the swarm, the environment, or a machine or components thereof. The swarm may self-organize, either by machine learning capability disposed on one or more members of the swarm, or based on instructions from an external machine learning facility, which may optimize storage, data collection, data processing, data presentation, data transport, and other functions based on managing parameters that are relevant to each. The machine learning facility may start with an initial configuration and vary parameters of the swarm relevant to any of the foregoing (also including varying the membership of the swarm), such as iterating based on feedback to the machine learning facility regarding measures of success (such as utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, profit measures, and others). Over time, the swarm may be optimized to a favorable configuration to achieve the desired measure of success for an owner, operator, or host of an industrial environment or a machine, component, or process thereof.

[0702] The swarm 4202 may be organized based on a hierarchical organization (such as where a master data collector 102 organizes and directs activities of one or more subservient data collectors 102), a collaborative organization (such as where decision-making for the organization of the swarm 4202 is distributed among the data collectors 102 (such as using various models for decision-making, such as voting systems, points systems, least-cost routing systems, prioritization systems, and the like), and the like.) In embodiments, one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102. Data collection systems 102 may communicate with each other and with the host processing system 112, including sharing an aggregate allocated storage space involving storage on or accessible to one or more of the collectors (which in embodiment may be treated as a unified storage space even if physically distributed, such as using virtualization capabilities). Organization may be automated based on one or more rules, models, conditions, processes, or the like (such as embodied or executed by conditional logic), and organization may be governed by policies, such as handled by the policy engine. Rules may be based on industry, application- and domainspecific objects, classes, events, workflows, processes, and systems, such as by setting up the swarm 4202 to collect selected types of data at designated places and times, such as coordinated with the foregoing. For example, the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and / or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines. In embodiments, self-organization may be cognitive, such as where the swarm varies one or more collection parameters and adapts the selection of parameters, weights applied to the parameters, or the like, over time. In examples, this may be in response to learning and feedback, such as from the learning feedback system 4012 that may be based on various feedback measures that may be determined by applying the analytic system 4018 (which in embodiments may reside on the swarm 4202, the host processing system 112, or a combination thereof) to data handled by the swarm 4202 or to other elements of the various embodiments disclosed herein (including marketplace elements and others). Thus, the swarm 4202 may display adaptive behavior, such as adapting to the current state 4020 or an anticipated state of its environment (accounting for marketplace behavior), behavior of various objects (such as loT devices, machines, components, and systems), processes (including events, states, workflows, and the like), and other factors at a given time. Parameters that may be varied in a process of variation (such as in a neural net, self-organizing map, or the like), selection, promotion, or the like (such as those enabled by genetic programming or other AI-based techniques). Parameters that may be managed, varied, selected and adapted by cognitive, machine learning may include storage parameters (location, type, duration, amount, structure and the like across the swarm 4202), network parameters (such as how the swarm 4202 is organized, such as in mesh, peer-to-peer, ring, serial, hierarchical and other network configurations as well as bandwidth utilization, data routing, network protocol selection, network coding type, and other networking parameters), security parameters (such as settings for various security applications and services), location and positioning parameters (such as routing movement of mobile data collectors 102 to locations, positioning and orienting collectors 102 and the like relative to points of data acquisition, relative to each other, and relative to locations where network availability may be favorable, among others), input selection parameters (such as input selection among sensors, input sources 116 and the like for each collector 102 and for the aggregate collection), data combination parameters (such as those for sensor fusion, input combination, multiplexing, mixing, layering, convolution, and other combinations), power parameters (such as parameters based on power levels and power availability for one or more collectors 102 or other objects, devices, or the like), states (including anticipated states and conditions of the swarm 4202, individual collection systems 102, the host processing system 112 or one or more objects in an environment), events, and many others. Feedback may be based on any of the kinds of feedback described herein, such that over time the swarm may adapt to its current and anticipated situation to achieve a wide range of desired objectives.

[0703] Methods and systems are disclosed herein for an industrial loT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial loT data. A distributed ledger may distribute storage across devices, using a secure protocol, such as those used for cryptocurrencies (such as the Blockchain™ protocol used to support the Bitcoin™ currency). A ledger or similar transaction record, which may comprise a structure where each successive member of a chain stores data for previous transactions, and a competition can be established to determine which of alternative data stored data structures is “best” (such as being most complete), can be stored across data collectors, industrial machines or components, data pools, data marketplaces, cloud computing elements, servers, and / or on the IT infrastructure of an enterprise (such as an owner, operator or host of an industrial environment or of the systems disclosed herein). The ledger or transaction may be optimized by machine learning, such as to provide storage efficiency, security, redundancy, or the like.

[0704] In embodiments, the cognitive data marketplace 4102 may use a secure architecture for tracking and resolving transactions, such as a distributed ledger 4004, wherein transactions in data packages are tracked in a chained, distributed data structure, such as a Blockchain™, allowing forensic analysis and validation where individual devices store a portion of the ledger representing transactions in data packages. The distributed ledger 4004 may be distributed to loT devices, to data pools 4020, to data collection systems 102, and the like, so that transaction information can be verified without reliance on a single, central repository of information. The transaction system 4114 may be configured to store data in the distributed ledger 4004 and to retrieve data from it (and from constituent devices) in order to resolve transactions. Thus, a distributed ledger 4004 for handling transactions in data, such as for packages of loT data, is provided. In embodiments, the self-organizing storage system 4028 may be used for optimizing storage of distributed ledger data, as well as for organizing storage of packages of data, such as loT data, that can be presented in the marketplace 4102.

[0705] Methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing and / or other network conditions. Network sensitivity can include awareness of the price of data transport (such as allowing the system to pull or push data during off-peak periods or within the available parameters of paid data plans), the quality of the network (such as to avoid periods where errors are likely), the quality of environmental conditions (such as delaying transmission until signal quality is good, such as when a collector emerges from a shielded environment, avoiding wasting use of power when seeking a signal when shielded, such as by large metal structures typically of industrial environments), and the like.

[0706] Methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and / or conditions identified in an industrial data collection environment. For example, interfaces can recognize what sensors are available and interfaces and / or processors can be turned on to take input from such sensors, including hardware interfaces that allow the sensors to plug in to the data collector, wireless data interfaces (such as where the collector can ping the sensor, optionally providing some power via an interrogation signal), and software interfaces (such as for handling particular types of data). Thus, a collector that is capable of handling various kinds of data can be configured to adapt to the particular use in a given environment. In embodiments, configuration may be automatic or under machine learning, which may improve configuration by optimizing parameters based on feedback measures over time.

[0707] Methods and systems are disclosed herein for self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data. Self-organizing storage may allocate storage based on application of machine learning, which may improve storage configuration based on feedback measure over time. Storage may be optimized by configuring what data types are used (e.g., byte-like structures, structures representing fused data from multiple sensors, structures representing statistics or measures calculated by applying mathematical functions on data, and the like), by configuring compression, by configuring data storage duration, by configuring write strategies (such as by striping data across multiple storage devices, using protocols where one device stores instructions for other devices in a chain, and the like), and by configuring storage hierarchies, such as by providing pre-calculated intermediate statistics to facilitate more rapid access to frequently accessed data items. Thus, highly intelligent storage systems may be configured and optimized, based on feedback, overtime.

[0708] Methods and systems are disclosed herein for self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment. Network coding, including random linear network coding, can enable highly efficient and reliable transport of large amounts of data over various kinds of networks. Different network coding configurations can be selected, based on machine learning, to optimize network coding and other network transport characteristics based on network conditions, environmental conditions, and other factors, such as the nature of the data being transported, environmental conditions, operating conditions, and the like (including by training a network coding selection model over time based on feedback of measures of success, such as any of the measures described herein).

[0709] In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network. A cognitive system may vary one or more parameters for networking, such as network type selection (e g., selecting among available local, cellular, satellite, Wi-Fi, Bluetooth™, NFC, Zigbee® and other networks), network selection (such as selecting a specific network, such as one that is known to have desired security features), network coding selection (such as selecting a type of network coding for efficient transport[such as random linear network coding, fixed coding, and others]), network timing selection (such as configuring delivery based on network pricing conditions, traffic and the like), network feature selection (such as selecting cognitive features, security features, and the like), network conditions (such as network quality based on current environmental or operation conditions), network feature selection (such as enabling available authentication, permission and similar systems), network protocol selection (such as among Hl IF, IP, TCP / IP, cellular, satellite, serial, packet, streaming, and many other protocols), and others. Given bandwidth constraints, price variations, sensitivity to environmental factors, security concerns, and the like, selecting the optimal network configuration can be highly complex and situation dependent. The self-organizing networking system 4030 may vary combinations and permutations of these parameters while taking input from a learning feedback system 4012 such as using information from the analytic system 4018 about various measures of outcomes. In the many examples, outcomes may include overall system measures, analytic success measures, and local performance indicators. In embodiments, input from a learning feedback system 4012 may include information from various sensors and input sources 116, information from the state system 4020 about states (such as events, environmental conditions, operating conditions, and many others, or other information) or taking other inputs. By variation and selection of alternative configurations of networking parameters in different states, the selforganizing networking system may find configurations that are well-adapted to the environment that is being monitored or controlled by the host system 112, such as the instance where one or more data collection systems 102 are located and that are well-adapted to emerging network conditions. Thus, a self-organizing, network-condition-adaptive data collection system is provided.

[0710] Referring to Figure 32, a data collection system 102 may have one or more output interfaces and / or ports 4010. These may include network ports and connections, application programming interfaces, and the like. Methods and systems are disclosed herein for a haptic or multi-sensory user interface, including a wearable haptic or multi-sensory user interface for an industrial sensor data collector, with vibration, heat, electrical, and / or sound outputs. For example, an interface may, based on a data structure configured to support the interface, be set up to provide a user with input or feedback, such as based on data from sensors in the environment. For example, if a fault condition based on a vibration data (such as resulting from a bearing being worn down, an axle being misaligned, or a resonance condition between machines) is detected, it can be presented in a haptic interface by vibration of an interface, such as shaking a wrist-worn device. Similarly, thermal data indicating overheating could be presented by warming or cooling a wearable device, such as while a worker is working on a machine and cannot necessarily look at a user interface. Similarly, electrical or magnetic data may be presented by a buzzing, and the like, such as to indicate presence of an open electrical connection or wire, etc. That is, a multi-sensory interface can intuitively help a user (such as a user with a wearable device) get a quick indication of what is going on in an environment, with the wearable interface having various modes of interaction that do not require a user to have eyes on a graphical UI, which may be difficult or impossible in many industrial environments where a user needs to keep an eye on the environment.

[0711] In embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and / or sound outputs. In embodiments, a haptic user interface 4302 is provided as an output for a data collection system 102, such as a system for handling and providing information for vibration, heat, electrical, and / or sound outputs, such as to one or more components of the data collection system 102 or to another system, such as a wearable device, mobile phone, or the like. A data collection system 102 may be provided in a form factor suitable for delivering haptic input to a user, such as vibration, warming or cooling, buzzing, or the like, such as input disposed in headgear, an armband, a wristband or watch, a belt, an item of clothing, a uniform, or the like. In such cases, data collection systems 102 may be integrated with gear, uniforms, equipment, or the like worn by users, such as individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may trigger haptic feedback. For example, if a nearby industrial machine is overheating, the haptic interface may alert a user by warming up, or by sending a signal to another device (such as a mobile phone) to warm up. If a system is experiencing unusual vibrations, the haptic interface may vibrate. Thus, through various forms of haptic input, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as those in an industrial environment) without requiring them to read messages or divert their visual attention away from the task at hand. The haptic interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the haptic system 4202. This may include rule-based or modelbased feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitive haptic system may be provided, where selection of inputs or triggers for haptic feedback, selection of outputs, timing, intensity levels, durations, and other parameters (or weights applied to them) may be varied in a process of variation, promotion, and selection (such as using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive haptic interface for a data collection system 102 is provided, which may leam and adapt feedback to satisfy requirements and to optimize the impact on user behavior, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.

[0712] Methods and systems are disclosed herein for a presentation layer for AR / VR industrial glasses, where heat map elements are presented based on patterns and / or parameters in collected data. Methods and systems are disclosed herein for condition-sensitive, selforganized tuning of AR / VR interfaces b...

Claims

1. A method for configuring role-based digital twins, comprising:receiving, by a processing system having one or more processors, an organizational definition of an industrial plant operation, wherein the organizational definition defines a set of roles within the industrial plant operation;generating, by the processing system, an organizational digital twin of the industrial plant operation based on the organizational definition, wherein the organizational digital twin is a digital representation of an organizational structure of the industrial plant operation;determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational definition;determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships;linking an identity of a respective individual to the role;determining, by the processing system, a configuration of a presentation layer of a role-based digital twin corresponding to the role based on the settings of the role that is linked to the identity, wherein the configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role;determining, by the processing system, a set of data sources that provide data corresponding to the set of states, wherein each data source provides one or more respective types of data; andconfiguring one or more data structures that is received from the one or more data sources, wherein the one or more data structures are configured to provide data used to populate one or more of the set of states in the role-based digital twin.

2. The method of claim 1, wherein the organizational definition further identifies a set of physical assets of the industrial plant operation.

3. The method of claim 1, wherein determining the set of relationships includes parsing the organizational definition to identify a reporting structure and one or more business units of the industrial plant operation.

4. The method of claim 3, wherein the set of relationships are inferred from the reporting structure and the business units.

5. The method of claim 1, further comprising linking a set of identities to the set of roles, wherein each identity corresponds to a respective role from the set of roles.

6. The method of claim 1, wherein the role-based digital twin integrates with an industrial plant operation resource planning system that operates on the organizational digital twin that represents the set of roles in the industrial plant operation, such that changes in the industrial plant operation resource planning system are automatically reflected in the organizational digital twin.

7. The method of claim 1, wherein the organizational structure includes hierarchical components.

8. The method of claim 7, wherein the hierarchical components are embodied in a graph data structure.

9. The method of claim 1, wherein the set of settings for the set of roles includes rolebased permission settings.

10. The method of claim 9, wherein the role-based permission settings are based on hierarchical components defined in the organizational definition.

11. The method of claim 1, wherein the set of settings for the set of roles includes rolebased preference settings.

12. The method of claim 11, wherein the role-based preference settings are configured based on a set of role-specific templates.

13. The method of claim 12, wherein the set of templates includes at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data officer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, or a business development template.

14. The method of claim 1, wherein the set of settings for the set of roles includes rolebased taxonomy settings.

15. The method of claim 14, wherein the taxonomy settings identify a taxonomy that is used to characterize data that is presented in the role-based digital twin, such that thedata is presented in a taxonomy that is linked to the role corresponding to the role-based digital twin.

16. The method of claim 15, wherein the set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, or a business development taxonomy.

17. The method of claim 1, wherein at least one role of the set of roles is selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, a human resources manager role, an investor role, an engineering manager role, an accountant role, an auditor role, a resource planning role, a public relations manager role, a project manager role, an operations manager role, a research and development role, an engineer role, including but not limited to mechanical engineer, electrical engineer, semiconductor engineer, chemical engineer, computer science engineer, data science engineer, network engineer, or some other type of engineer, and a business development role.

18. The method of claim 17, wherein the at least one role is selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

19. A system for data collection in an industrial environment, the system comprising:an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;a pattern recognition circuit structured to determine a recognized pattern value in response to at least a portion of the plurality of sensor data values;a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value; wherein the sensor communication circuit isfurther structured to adjust the interpreting of the plurality of sensor data values in response to the updated sensed parameter group, wherein the sensor learning circuit is further structured to update the sensed parameter group by updating a network path configuration corresponding to at least one sensor from the sensed parameter group; anda reporting circuit, wherein the sensed parameter group is associated with a rolebased taxonomy setting, and the role-based taxonomy setting is further associated with at least one reporting rule for reporting the update to the sensed parameter group to at least one role taxonomy including among the role-based taxonomy setting.

20. The system of claim 19, wherein the sensed parameter group comprises a fused pluralityof sensors, and wherein the recognized pattern value further includes a secondary value comprising a value determined in response to the fused plurality of sensors.

21. The system of claim 20, wherein the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value.

22. The system of claim 21, wherein the sensing performance value comprises at least one performance determination including: a signal-to-noise performance for detecting a value of interest in the industrial system; a network utilization of the plurality of sensors in the industrial system; an effective sensing resolution for a value of interest in the industrial system; or a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.

23. The system of claim 21, wherein the sensing performance value comprises a signal-to-noise performance for detecting a value of interest in the industrial system.

24. The system of claim 21, wherein the sensing performance value comprises a network utilization of the plurality of sensors in the industrial system.

25. The system of claim 21, wherein the sensing performance value comprises an effectivesensing resolution for a value of interest in the industrial system.

26. The system of claim 21, wherein the sensing performance value comprises a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.

27. The system of claim 21, wherein the sensing performance value comprises a calculation efficiency for determining the secondary value.

28. The system of claim 27, wherein the calculation efficiency comprises at least one of: processor operations to determine the secondary value, memory utilization for determining the secondary value, a number of sensor inputs from the plurality of sensors for determining the secondary value, and supporting data long-term storage for supporting the secondary value.

29. The system of claim 21, wherein the sensing performance value comprises one of an accuracy or a precision of the secondary value.

30. The system of claim 21, wherein the sensing performance value comprises a redundancy capacity for determining the secondary value.

31. The system of claim 21, wherein the sensing performance value comprises a lead time value for determining the secondary value.

32. The system of claim 31, wherein the secondary value comprises a component overtemperature value.

33. The system of claim 31, wherein the secondary value comprises one of a component maintenance time, a component failure time, or a component service life.

34. The system of claim 31, wherein the secondary value comprises an off nominal operating condition affecting a product quality produced by an operation of the industrial system.

35. The system of claim 19, wherein the plurality of sensors comprises at least one analog sensor.

36. The system of claim 19, wherein at least one of the sensors comprises a remote sensor.

37. The system of claim 20, wherein the secondary value comprises at least one of a virtualsensor output value; a process prediction value; a process state value; a component prediction value; a component state value; or a model output value having the sensor data values from the fused plurality of sensors as an input.

38. The system of claim 20, wherein the fused plurality of sensors further comprises at least one of a vibration sensor and a temperature sensor; a vibration sensor and a pressure sensor; a vibration sensor and an electric field sensor; a vibration sensor and a heat flux sensor; a vibration sensor and a galvanic sensor; or a vibration sensor and a magnetic sensor.

39. The system of claim 19, wherein the sensor learning circuit is further structured to update the sensed parameter group to form the updated sensed parameter group by performing at least one additional operation including updating a sensor selection of the sensed parameter group; updating a sensor sampling rate of at least one sensor fromthe sensed parameter group; updating a sensor resolution of at least one sensor from the sensed parameter group; updating a storage value corresponding to at least one sensor from the sensed parameter group; updating a priority corresponding to at least one sensor from the sensed parameter group; and updating at least one of a sampling rate, sampling order, or sampling phase.

40. The system of claim 39, wherein the pattern recognition circuit is further structured to determine the recognized pattern value by at least one of determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest; determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; or updating the recognized pattern value in response to external feedback.

41. The system of claim 40, wherein the value of interest comprises at least one value including a virtual sensor output value; a process prediction value; a process state value; a component prediction value; a component state value; or a model output value having the sensor data values from a fused plurality of sensors as an input.

42. The system of claim 19, wherein the pattern recognition circuit is further structured to access cloud-based data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system.

43. The system of claim 42, wherein the sensor learning circuit is further structured to access the cloud-based data comprising a second updated sensor parameter group corresponding to the at least one offset industrial system.

44. The system of claim 19, wherein the at least one role taxonomy is at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, asupplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, or a business development taxonomy.

45. A system for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process, the system comprising:a data circuit for analyzing a plurality of sensor inputs; anda network control circuit for sending and receiving information related to the plurality of sensor inputs to an external system, wherein the information sent and received by the network control system is based at least in part on a role-based reporting rule and encoded with role-based information that is used by the external system to report the information sent and received to a specified role taxonomy, wherein the system provides sensor data to one or more other systems for data collection, and wherein the data circuit dynamically reconfigures a route by which the system sends the sensor data based on how many devices are requesting the information.

46. The system of claim 45, wherein the system further comprises a plurality of network communication interfaces.

47. The system of claim 46, wherein the network control circuit bridges another system for data collection from one network to another using the plurality of network communication interfaces.

48. The system of claim 45, wherein the network control circuit implements a network of systems for data collection using an intercommunication protocol selected from a list comprising: multi-hop, mesh, serial, parallel, ring, real-time andhub-and-spoke.

49. The system of claim 45, wherein the system continuously provides a single copy of its information to another system for data collection and directs requesters of its information to the another system for data collection.

50. The system of claim 45, wherein at least one of the one or more other systems for data collection has different operational characteristics than the system.

51. The system of claim 50, wherein the different operational characteristics are selected from a list comprising: power, storage, network connectivity, proximity, reliability, duty cycle.

52. The system of claim 45, wherein, after a configurable time period, the system stores only digests of the information and discards underlying information.

53. The system of claim 45, wherein the data circuit dynamically nominates one of the other systems for data collection capable of providing the sensor data to replace the system.

54. The system of claim 53, wherein the nomination is triggered by a detection of a system failure mode.

55. The system of claim 45, wherein the network control circuit arranges the system into a redundant storage network.

56. The system of claim 45, wherein the system accumulates data received from the other systems for data collection while an upstream network connection is unavailable, and then sends all accumulated data once the upstream network connection is restored.

57. The system of claim 45, wherein the system further comprises an intelligent agent circuit that combines the sensor data between the system and the other systems for data collection.

58. The system of claim 57, wherein the intelligent agent circuit chooses what sensor data to collect or store based on a machine learning algorithm.

59. The system of claim 45, wherein the analyzing further comprises detecting anomalies in the information.

60. The system of claim 45, wherein the system dynamically reconfigures where it sends data and what quantity of data it sends based on an availability of the other systems for data collection.

61. The system of claim 60, wherein the system further comprises a plurality of network communication interfaces.

62. The system of claim 61, wherein the system dynamically reconfigures where it sends data and what quantity of data it sends based on requests received over one or more of the plurality of network communication interfaces.

63. The system of claim 61, further comprising a data filter circuit configured to dynamically adjust what portion of the information is sent based on instructions received over one or more of the plurality of network communication interfaces.

64. The system of claim 60, wherein the system dynamically reconfigures where it sends data and what quantity of data it sends based on an operating parameter of at least one of the system or one of the other systems for data collection.

65. The system of claim 45, wherein the specified role type is at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief dataofficer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, or a business development taxonomy.

66. A method for data collection in an industrial environment, the method comprising: receiving, at a switch, analog data from a plurality of variable groups of analog sensor inputs;monitoring the analog data from the plurality of variable groups of analog sensor inputs;adaptively scheduling data collection of the switch;determining a noise value including at least one of ambient noise, local noise, or vibration noise;using machine learning to forecast a mechanical state of a machine based at least in part on the determined noise value; andreporting the forecasted mechanical state of the machine to an entity associated with a role type stored within a role taxonomy.

67. The method of claim 66, further comprising performing internet protocol (IP) front-end end signal conditioning to improve a signal-to-noise ratio of at least one of the analog sensor inputs.

68. The method of claim 66, further comprising predicting a state of at least one of a component or a process of the industrial environment in response to the determined noise value.

69. The method of claim 66, further comprising utilizing a transfer function to determine a relative phase of a first one of the analog sensor inputs to a second one of the analog sensor inputs.

70. The method of claim 66, wherein the role taxonomy is at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, aservice manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy.

71. A data collection and processing system, comprising:variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment;a switch comprising multiple sensor channels configured to receive long blocks of high-sampling rate data from one or more of the variable groups of sensor inputs; anda data management feature that includes multiple routes communicatively coupling the variable groups of sensor inputs to the multiple sensor channels, wherein the switch is configured to use hierarchical templates to perform one of creating or adjusting one of the multiple routes, and wherein the data management feature includes multiple reporting routes for distributing data from the multiple sensor channels to at least one entity associated with a role type stored within a role taxonomy.

72. The data collection and processing system of claim 71, further comprising a controller configured to store calibration data and a maintenance history of one or more sensors associated with the variable groups of sensor inputs.

73. The data collection and processing system of claim 71, wherein the switch is further configured to provide a continuously monitored alarm in response to one of more inputs from the variable groups of sensor inputs.

74. The data collection and processing system of claim 71, wherein the switch is configured to perform internet protocol (IP) front-end end signal conditioning to improve a signal-to-noise ratio of one or more of the sensor channels.

75. A data collection and processing system, comprising:variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment;a switch comprising multiple sensor channels configured to receive long blocks of high-sampling rate data from one or more of the variable groups of sensor inputs; anda data management feature that includes a controller configured to perform intelligent management of data collection frequency bands obtained by one or more of the variable groups of sensor inputs, and wherein the intelligent management of data collection is based at least in part on a data collection rule relating to a data requirement of at least one entity associated with a role type stored within a role taxonomy.

76. The data collection and processing system of claim 75, wherein the controller is configured to store calibration data and a maintenance history of one or more sensors associated with the variable groups of sensor inputs.

77. The data collection and processing system of claim 75, wherein the switch is further configured to provide a continuously monitored alarm in response to one or more inputs from the variable groups of sensor inputs.

78. The data collection and processing system of claim 75, wherein the switch is configured to perform internet protocol (IP) front-end end signal conditioning to improve a signal-to-noise ratio of one or more of the sensor channels.

79. A data collection and processing system, comprising:variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment;a switch comprising multiple sensor channels configured to receive long blocks of high-sampling rate data from one or more of the variable groups of sensor inputs; anda data management feature that includes a controller comprising a neural net expert system, wherein the neural net expert system is configured to perform intelligent management of data collection frequency bands obtained by at least one of the variable groups of sensor inputs, and wherein the neural net expert system is configured to manage data based at least in part on a rule associated with a role type stored within a role taxonomy.

80. The data collection and processing system of claim 79, wherein the controller is configured to store calibration data and a maintenance history of one or more sensors associated with the variable groups of sensor inputs.

81. The data collection and processing system of claim 79, wherein the switch is further configured to provide a continuously monitored alarm in response to one or more inputs from the variable groups of sensor inputs.

82. The data collection and processing system of claim 79, further comprising at least one additional switch, and wherein each of the switches comprises a corresponding complex programmable logic device (CPLD), each CPLD configured to control a corresponding switch.

83. A data collection and processing system, comprising:variable groups of sensor inputs operationally coupled to an industrial environment;a switch communicatively coupled to the variable groups of sensor inputs; and a controller configured to continuously monitor data from multiple variable groups of sensor inputs, wherein continuously monitoring comprises adaptively scheduling data collection of the switch, and reporting a future state of the industrial environment based on a rule associated with a role type stored within a role taxonomy.

84. The data collection and processing system of claim 83 wherein the controller is further configured to determine a noise value from one or more of an ambient noise, a local noise, or a vibration noise; and wherein the controller is further configured to predict a state of at least one of a component or a process of the industrial environment in response to the determined noise value.

85. The data collection and processing system of claim 83, wherein the controller is further configured to use smart route changes based on at least one of incoming data or alarms, and to provide simultaneous dynamic data from the multiple variable groups of sensor inputs in response to the one of incoming data or alarms.

86. The data collection and processing system of claim 83, wherein the controller is further configured to perform machine pattern recognition based on a fusion of a selected plurality of the variable groups of sensor inputs.

87. The data collection and processing system of claim 83, wherein the controller is further configured to utilize a transfer function to determine a relative phase of a first one of the sensor inputs to a second one of the sensor inputs.

88. The data collection and processing system of claim 83, wherein the controller is further configured to perform machine pattern analysis based on a selected plurality of the variable groups of sensor inputs, and wherein the machine pattern analysis comprises anticipated state information for the industrial environment.

89. The data collection and processing system of claim 83, further comprising a cloudbased policy automation engine comprising at least one of: a sensor selection of the variable groups of sensor inputs; a sensor deployment of variable groups of sensor inputs; a sensor fusion of the variable groups of sensor inputs; or a data storage profile of the variable groups of sensor inputs; wherein the controller is further configured to selectively communicate at least a portion of the data of the multiple variable groups of sensor inputs to a cloud-based storage, and wherein the communication to the cloudbased storage is organized by a quantitative weighting of relevance of the data of the multiple variable groups of sensor to at least one role type stored within a role taxonomy.

90. The data collection and processing system of claim 83, wherein the controller is further configured to implement a self-organizing data marketplace, the self-organizing data marketplace including at least a portion of the data from the variable groups of sensor inputs.

91. The data collection and processing system of claim 90, wherein the controller is further configured to implement a self-organizing data pool based on at least one of: sensor utilization, data utilization, or yield metrics.

92. The data collection and processing system of claim 83, wherein the controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment.

93. A method for data collection in an industrial environment, the method comprising: receiving, at a switch, data from one or more variable groups of sensor inputs; monitoring the data from the one or more variable groups of sensor inputs; adaptively scheduling data collection at the switch;determining one or more noise values including one of an ambient noise, a local noise, or a vibration noise;using machine learning to forecast a future state of the industrial environment based at least in part on the determined one or more noise values; andreporting the forecasted future state of the industrial environment to an entity associated with a role type stored within a role taxonomy.

94. The method of claim 93, further comprising performing internet protocol (IP) front-endend signal conditioning to improve a signal-to-noise ratio of the one or more variable groups of sensor inputs.

95. The method of claim 93, further comprising predicting a state of at least one of a component or a process of the industrial environment in response to one or more of the determined noise values.

96. The method of claim 93, further comprising utilizing a transfer function to determine a relative phase between a first sensor input from one of the variable groups of sensor inputs and a second sensor input from one of the variable groups of sensor inputs.

97. A method, comprising:by a sensor communication circuit, interpreting data from a plurality of input sensors, wherein each of the plurality of input sensors is operationally coupled to a component of an industrial environment;by a controller, operating a self-organizing network on the data from the plurality of input sensors, thereby determining a structure in the data;by the controller, determining a relevance of the determined structure in the data to at least one role type stored within a role taxonomy;by the controller, determining a reduced dimensionality view of the data in response to the determined structure in the data, wherein the reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors, and wherein the reduced dimensionality view further comprises a graphical element representing at least one of: mechanical portions of a machine of the industrial environment, or a sensor from the plurality of input sensors that provided data; andproviding the reduced dimensionality view to a user interface that is associated with at least one entity associated with the at least one role type stored within the role taxonomy.

98. The method of claim 97, further comprising determining that a condition of interest has occurred within the industrial environment, wherein operating the self-organizing network on the data from the plurality of input sensors further comprises applying back calculation to the condition of interest to determine a failed part related to the condition of interest, and wherein the reduced dimensionality view comprises a graphical representation of the failed part related to the condition of interest.

99. The method of claim 98, wherein the graphical representation of the failed part relatedto the condition of interest further comprises a map of highlighted data collection routes that reflect paths of data from at least one of the plurality of input sensors that contributes to calculating the condition of interest.

100. The method of claim 97, further comprising determining that a condition of interest has occurred within the industrial environment, wherein operating the self-organizing network on the data from the plurality of input sensors further comprises applying back calculation to the condition of interest to determine a data collection system configuration template associated with the condition of interest, and wherein the reduced dimensionality view comprises a graphical representation of the data collection system configuration template associated with the condition of interest.

101. The method of claim 100 wherein the graphical representation of a failed part related to the condition of interest further comprises a map of data collection configured by the data collection system configuration template, with highlighted routes in a datacollection system that reflect paths of data from at least one sensor to at least one data collector for data that contributes to calculating the condition of interest.

102. The method of claim 97, wherein the reduced dimensionality view comprises a heat map.

103. The method of claim 97, wherein operating the self-organizing network comprises applying competitive learning to the data from the plurality of input sensors to determine the structure in the data.

104. The method of claim 97, wherein determining the reduced dimensionality view comprises at least one operation including determining an unlabeled data value of the data from the plurality of input sensors; the method further comprising determining that the data from the plurality of input sensors comprises vibration data from an unknown source within the industrial environment; or providing a frequency value corresponding to an unlabeled data value of the data from the plurality of input sensors.

105. The method of claim 97, wherein determining the reduced dimensionality view comprises determining an unlabeled data value of the data from the plurality of input sensors, the method further comprising accepting a user input on the user interface, and labeling the unlabeled data value in response to a user input on the user interface.

106. A system, comprising:a sensor communication circuit structured to interpret a plurality of sensor data values from a plurality of input sensors, wherein each of the plurality of input sensors is operationally coupled to a component of an industrial environment;a controller configured to:operate a self-organizing network on the data from the plurality of input sensors, thereby determining a structure in the data; anddetermine a reduced dimensionality view of the data in response to the determined structure in the data and in response to a relevance of the determined structure in the data to at least one role type stored within a role taxonomy, wherein the reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors, and wherein the reduced dimensionality view further comprises a plurality of graphical elements representing mechanical portions of a machine of the industrial environment; anda user interface configured to display the reduced dimensionality view.

107. The system of claim 106, wherein the plurality of graphical elements further comprises mechanical portions of the machine of the industrial environment that are associated with a condition of interest.

108. The system of claim 107, wherein the reduced dimensionality view further comprises a second plurality of graphical elements representing data collectors in the industrial environment associated with the plurality of input sensors.

109. The system of claim 108, wherein the reduced dimensionality view further comprises a plurality of highlighted graphical elements representing sensors from the plurality of input sensors that provided data outside an acceptable range of data.

110. The system of claim 109, wherein the user interface is further configured to provide a list of conditions of interest to a user, and to select the condition of interest in response to a user selection from the list of conditions.

111. The system of claim 110, wherein the controller is further configured to provide a data collection system configuration template in response to the user selection from the list of conditions, and wherein the data collection system configuration template facilitates configuring a data collection system to collect data for calculating the condition of interest.

112. The system of claim 106, wherein the mechanical portions comprise at least one of a bearing, a shaft, a rotor, or a housing of the machine of the industrial environment.

113. An apparatus, comprising:a user interface configured to provide a list of conditions of interest to at least one entity associated with a role type stored within a role taxonomy, and to select a condition of interest in response to a user selection from the list of conditions; anda controller configured to:operate a self-organizing network on data from a plurality of input sensors, the plurality of input sensors operationally coupled to a component of an industrial environment; anddetermine a reduced dimensionality view of the data in response to a determined structure in the data and further in response to the selected condition of interest, wherein the reduced dimensionality view a plurality of graphical elements representing mechanical portions of a machine of the industrial environment associated with the condition of interest, wherein the reduced dimensionality view further comprises a plurality of highlighted graphical elements representing sensors from the plurality of input sensors that provided data outside an acceptable range of data, andwherein the user interface is further configured to display the reduced dimensionality view.

114. The apparatus of claim 113, wherein the controller is further configured to provide a data collection system configuration template in response to the user selection from the list of conditions, and wherein the data collection system configuration template facilitates configuring a data collection system to collect data for calculating the condition of interest.

115. A system for data collection in an industrial environment, comprising:a plurality of wearable haptic stimulators that produce stimuli;a plurality of sensors operationally coupled to a machine of the industrial environment;a processor logically disposed between the plurality of sensors and the wearable haptic stimulators, the processor configured to:receive data from the plurality of sensors representative of a sensed condition of the machine;determine a relevance of the sensed condition of the machine to at least one role type stored within a role taxonomy;determine at least one haptic stimulation that corresponds to the received data; and send at least one signal in response to the at least one haptic stimulation, wherein at least one of the plurality of wearable haptic stimulators is responsive to the at least one signal, wherein the at least one haptic stimulation represents an effect on the machine in the industrial environment resulting from the sensed condition, and wherein an effect on the machine in the industrial environment is presented as an effect on at least one of the plurality of wearable haptic stimulators and reported to at least one entity associated with the at least one role type stored within the role taxonomy.

116. The system of claim 115, wherein the effect from at least one haptic stimulation at least one of a list of stimuli including tactile, vibration, heat, sound, force, odor, or motion.

117. The system of claim 115, wherein a vibrating effect on the machine in the industrial environment is presented as vibrating at least one of the plurality of wearable haptic stimulators.

118. The system of claim 115, wherein a heating effect on the machine in the industrial environment is presented as an increase in temperature of at least one of the plurality of wearable haptic stimulators.

119. The system of claim 115, wherein an electrical effect on the machine in the industrial environment is presented as a change in sound produced by at least one of the plurality of wearable haptic stimulators.

120. The system of claim 115, wherein at least one of the plurality of wearable haptic stimulators including at least one of a glove, a ring, a wrist band, a wristwatch, an arm band, head gear, a belt, a necklace, a shirt, footwear, pants, overalls, coveralls, or safety goggles.

121. The system of claim 115, wherein the at least one signal comprises an alert of a condition of interest in the industrial environment.

122. The system of claim 121, wherein the at least one haptic stimulation produced in response to the alert of a condition of interest is repeated by at least one of the plurality of wearable haptic stimulators until an acceptable response is detected.

123. A monitoring system for collecting data related to an industrial environment, the monitoring system comprising:a data collector communicatively coupled to a plurality of input channels, wherein the plurality of input channels comprises data relating to an aspect of an industrial production process and data relating to at least one role type stored within a role taxonomy;a data storage structured to store a plurality of detection values that corresponds to the plurality of input channels;a data analysis circuit structured to interpret at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state;an optimization circuit structured to analyze a subset of the plurality of detection values and the state value using at least one of a neural net or an expert system to determine a signal effectiveness of at least one of the plurality of input channels relative to the state value, and to provide an adjustment recommendation based, at least in part, on the signal effectiveness; andan analysis response circuit structured to adjust the industrial production process in response to the adjustment recommendation.

124. The monitoring system of claim 123, wherein the optimization circuit is further structured to provide the adjustment recommendation as one of an equipment change for a component of the industrial production process, or an equipment operating parameter change for the component of the industrial production process.

125. The monitoring system of claim 123, wherein the optimization circuit is further structured to provide the adjustment recommendation as a process parameter change for the industrial production process.

126. The monitoring system of claim 125, wherein the process parameter change comprises a command to rebalance process loads between components of the industrial production process.

127. The monitoring system of claim 126, wherein the optimization circuit is further structured to provide the process parameter change to achieve at least one of: extending a life of one of the components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the components of the industrial production process.

128. The monitoring system of claim 126, wherein the optimization circuit is further structured to provide the process parameter change to facilitate maintenance of one of the components of the industrial production process.

129. The monitoring system of claim 128, wherein the optimization circuit is further structured to facilitate the maintenance of one of the components by performing at least one operation including extending a maintenance interval of one of the components; synchronizing a first maintenance interval of a first one of the components with a second maintenance interval of a second one of the components; and differentiating the first maintenance interval of the first one of the components from the second maintenance interval of the second one of the components.

130. The monitoring system of claim 128 wherein the optimization circuit is further structured to facilitate the maintenance of one of the components by aligning a maintenance interval of one of the components with an external reference time.

131. The monitoring system of claim 130, wherein the external reference time comprises at least one time including a planned shutdown time for the industrial production process, a time that is past an expected completion time of the industrial production process, or a scheduled maintenance time for one of the components.

132. The monitoring system of claim 123, wherein the optimization circuit is further structured to determine a sensitivity of at least one of the plurality of input channels relative to the state value.

133. The monitoring system of claim 123, wherein the optimization circuit is further structured to determine a predictive accuracy of at least one of the plurality of input channels relative to the state value.

134. A computer-implemented method for collecting data related to an industrial environment, the computer-implemented method comprising:collecting data, using a data collector communicatively coupled to a plurality of input channels, wherein the plurality of input channels comprises data relating to an aspect of an industrial production process;storing a plurality of detection values that corresponds to the plurality of input channels; interpreting at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state;analyzing a subset of the plurality of detection values and the state value, using at least one of a neural net or an expert system, and providing an adjustment recommendation for the industrial production process, the adjustment recommendation, at least in part, in response to a sensitivity of at least one of the plurality of input channels relative to the state value;adjusting the industrial production process in response to the adjustment recommendation;determining a relevance of the adjustment recommendation to at least one role type stored within a role taxonomy; andreporting the adjustment to the industrial production process to at least one entity associated with the role type stored within the role taxonomy.

135. The computer-implemented method of claim 134, further comprising providing the adjustment recommendation in response to a signal effectiveness of at least one of the plurality of input channels relative to the state value.

136. The computer-implemented method of claim 134, further comprising providing the adjustment recommendation in response to a predictive confidence of at least one of the plurality of input channels relative to the state value.

137. The computer-implemented method of claim 134, further comprising providing the adjustment recommendation in response to a predictive accuracy of at least one of the plurality of input channels relative to the state value.

138. The computer-implemented method of claim 134, wherein adjusting the industrial production process comprises rebalancing process loads between components of the industrial production process to achieve at least one of: extending a life of one of a plurality of components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the plurality of components of the industrial production process.

139. The computer-implemented method of claim 134, wherein adjusting the industrial production process comprises facilitating maintenance on a component of the industrial production process to achieve at least one of extending a maintenance interval of the component; synchronizing a first maintenance interval of the component with a second maintenance interval of a second component of the industrial production process; and differentiating the first maintenance interval of the component from the second maintenance interval of the second component of the industrial production process.

140. The computer-implemented method of claim 134, wherein adjusting the industrial production process comprises facilitating maintenance on a component of the industrial production process to align a maintenance interval of a component of the industrial production process with an external reference time.

141. The computer-implemented method of claim 140, wherein the external reference time comprises at least one time including a planned shutdown time for the industrial production process, a time that is past an expected completion time of the industrial production process, or a scheduled maintenance time for a second component of the industrial production process.

142. An apparatus for collecting data related to an industrial environment, the apparatus comprising:a data collector component, communicatively coupled to a plurality of input channels, wherein the plurality of input channels comprises data from and data about an element of an industrial production process, the element comprising at least one of: a machine, a component, a system, a sub-system, an ambient condition, a state, a workflow, or a process;a data storage component configured to store a plurality of detection values that corresponds to the plurality of input channels;a data analysis component configured to interpret at least a subset of the plurality of detection values to determine a state value, wherein the state value comprises at least one of: a sensor state, a process state, or a component state;an optimization component configured to analyze a subset of the plurality of detection values, and the state value using at least one of a neural net or an expert system, and to determine a predictive accuracy of at least one of the plurality of input channels relative to the state value, and to provide an adjustment recommendation based, at least in part, on the predictive accuracy;an analysis response component configured to adjust the industrial production process in response to the adjustment recommendation;a relevance calculation component to determine a relevance of the adjustment recommendation to at least one role type stored within a role taxonomy; anda reporting component to report the adjustment to the industrial production process to at least one entity associated with the role type stored within the role taxonomy.

143. The apparatus of claim 142, wherein adjusting the industrial production process comprises rebalancing process loads between components to achieve at least one of: extending a life of one of a plurality of components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the plurality of components of the industrial production process.

144. The apparatus of claim 142, wherein the optimization component is further configuredto provide the adjustment recommendation as a process parameter change for the industrial production process.

145. The apparatus of claim 144, wherein the process parameter change comprises a command to rebalance process loads between components of the industrial production process.

146. The apparatus of claim 145, wherein the optimization component is further configuredto provide the process parameter change to achieve at least one of: extending a life of one of the components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the components of the industrial production process.

147. The apparatus of claim 146, wherein the optimization component is further configuredto facilitate the maintenance of one of the components by performing at least one operation including extending a maintenance interval of one of the components; synchronizing a first maintenance interval of a first one of the components with a second maintenance interval of a second one of the components; differentiating the first maintenance interval of the first one of the components from the second maintenance interval of the second one of the components; and aligning a maintenance interval of one of the components with an external reference time.

148. The apparatus of claim 147, wherein the external reference time comprises at least onetime including a planned shutdown time for the industrial production process, a timethat is past an expected completion time of the industrial production process, or a scheduled maintenance time for one of the components.

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