Methods and systems for industrial internet of things

Abstract

An embodiment of the present invention discloses a method and system for an industrial Internet of Things. The system includes a platform of a computing environment connected to a local data collection system, wherein the local data collection system obtains a first sensor signal and a second sensor signal from a first machine in an industrial environment; a first sensor in the local data collection system for connecting to the first machine and a second sensor in the local data collection system, and a crosspoint switch having multiple inputs and multiple outputs, wherein the first output and the second output of the multiple outputs are switched between the following two situations: a situation where the first output is used to switch between transmitting the first sensor signal and transmitting the second sensor signal, and a situation where the first sensor signal is transmitted from the first output and the second sensor signal is transmitted from the second output at the same time, each of the multiple inputs is used to be individually assigned to any one of the multiple outputs, and the unassigned outputs are used to be turned off by generating a high impedance state.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 333,589, filed May 9, 2016, and entitled “Powerful Industrial IoT Arrays”; U.S. Provisional Patent Application No. 62 / 350,672, filed June 15, 2016, and entitled “High Sampling Rate Digital Recording of Measurement Waveform Data as Part of Automated Sequential Listing of Streaming Long Duration and Gapless Waveform Data for Storage for More Flexible Post-Processing”; U.S. Provisional Patent Application No. 62 / 412,843, filed October 26, 2016, and entitled “Methods and Systems for Industrial Internet of Things”; and U.S. Provisional Patent Application No. 62 / 427,141, filed November 28, 2016, and entitled “Methods and Systems for Industrial Internet of Things”. All of the above-mentioned patent applications are hereby incorporated by reference in their entirety as if fully set forth herein. Technical Field

[0003] The present invention relates to methods and systems for data collection in industrial environments, and methods and systems for performing monitoring, remote control, autonomous actions and other activities in industrial environments using the collected data. Background Art

[0004] Heavy industrial environments, such as those used for large-scale manufacturing (e.g., airplanes, ships, trucks, cars, and large industrial machines), energy production environments (e.g., oil and gas plants, renewable energy environments, etc.), energy extraction environments (e.g., mining, drilling, etc.), and construction environments (e.g., construction of large buildings), involve highly complex machines, equipment, and systems, as well as highly complex workflows, where operators must be able to account for numerous parameters, metrics, etc., in order to optimize the design, development, deployment, and operation of different technologies to improve overall effectiveness. In the past, people used dedicated data collectors to collect data in heavy industrial environments, often recording multiple batches of specific sensor data on media such as tapes or hard drives for later analysis. In the past, multiple batches of data were returned to headquarters for analysis, such as signal processing or other analysis of data collected by multiple sensors. Thereafter, the results of the analysis can be used as a basis for diagnosing problems in the environment and / or proposing operational improvements. In the past, this work often took weeks or months and was aimed at limited data sets.

[0005] The advent of the Internet of Things (IoT) has enabled the continuous connection of a wider range of devices and the continuous interconnection between these devices. Most of these devices are consumer devices, such as lights, thermostats, etc. This is more difficult to achieve in more complex industrial environments because the range of available data is often limited and the processing of data from multiple sensors is very complex, making it difficult to form "smart" solutions that are effective for the industrial sector. Improvements are needed in methods and systems for data collection in industrial environments, as well as methods and systems for using the collected data to provide improved monitoring, control, intelligent problem diagnosis, and intelligent operation optimization in a variety of heavy industrial environments. Summary of the invention

[0006] The present invention provides methods and systems for data collection in industrial environments, and improved methods and systems for using the collected data to provide improved monitoring, control, intelligent problem diagnosis, and intelligent operation optimization in a variety of heavy industrial environments. These methods and systems include methods, systems, components, devices, workflows, services, processes, etc. applied in different configurations and locations, such as: (a) at the edge of the Internet of Things, such as the local environment of a heavy industrial machine; (b) a data transmission network that moves data between the local environment of a heavy industrial machine and other environments, such as other machines or remote controllers, such as enterprises that own or operate machines or facilities where machines operate; and (c) where facilities are deployed to control machines or their environments, such as cloud computing environments and on-premises computing environments of enterprises that own or control heavy industrial environments or machines, devices, or systems deployed therein. These include a series of methods and systems for providing improved data collection, as well as methods and systems for deploying enhanced intelligence at the edge of controllers in industrial environments, in networks, and in the cloud or on-premises.

[0007] Disclosed herein are methods and systems for continuous ultrasonic monitoring, including continuous ultrasonic monitoring of rotating elements and bearings of energy production facilities.

[0008] Disclosed herein are methods and systems for cloud-based machine pattern recognition based on remote simulated industrial sensor fusion.

[0009] Disclosed herein are methods and systems for cloud-based machine pattern analysis of multiple simulated industrial sensor state information to provide expected state information of an industrial system.

[0010] Disclosed herein are methods and systems for device-built-in sensors and data storage for industrial IoT devices, including device-built-in sensors and data storage for industrial IoT devices, wherein data from multiple sensors are multiplexed in a device for storing fused data streams.

[0011] Disclosed herein are methods and systems for a self-organizing data marketplace for industrial IoT data, including a self-organizing data marketplace for industrial IoT data, wherein available data elements are organized in a marketplace for consumption by consumers based on training a self-organizing facility using a training set and feedback from marketplace success metrics.

[0012] Disclosed herein are methods and systems for self-organizing data pools, which may include self-organizing data pools based on utilization and / or revenue metrics, including utilization and / or revenue metrics tracked for multiple data pools.

[0013] The present invention discloses a method and system for training an AI (Artificial Intelligence) model based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, benefit, or impact, and wherein the AI ​​model operates on sensor data from an industrial environment.

[0014] Disclosed herein are methods and systems for self-organizing groups of industrial data collectors, including self-organizing groups of industrial data collectors that organize among the industrial data collectors based on the capabilities and conditions of group members to optimize data collection.

[0015] Methods and systems for an Industrial Internet of Things distributed ledger are disclosed herein, including a distributed ledger that supports tracking transactions performed on Industrial IoT data in an automated data marketplace.

[0016] Disclosed herein are methods and systems for self-organizing collectors, including self-organizing, multi-sensor data collectors that can optimize data collection, power, and / or revenue based on conditions in their environment.

[0017] Disclosed herein are methods and systems for network-aware collectors, including network condition-aware, self-organizing, multi-sensor data collectors that can optimize based on bandwidth, quality of service, pricing, and / or other network conditions.

[0018] Disclosed herein are methods and systems for remotely organizing a universal data collector that can power on and off sensor interfaces based on needs and / or conditions identified in an industrial data collection environment.

[0019] Disclosed herein are methods and systems for self-organizing storage of a multi-sensor data collector, including self-organizing storage of a multi-sensor data collector for industrial sensor data.

[0020] Disclosed herein are methods and systems for self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network for transmitting data from multiple sensors in an industrial data collection environment.

[0021] Disclosed herein are methods and systems for tactile or multi-sensory user interfaces, including wearable tactile or multi-sensory user interfaces for industrial sensor data collectors with vibration, thermal, electrical, and / or acoustic output.

[0022] Disclosed herein are methods and systems for an AR / VR industrial eyewear presentation layer, wherein heat map elements are presented based on patterns and / or parameters in collected data.

[0023] Disclosed herein are methods and systems for condition-sensitive, self-organizing adjustments to AR / VR interfaces based on feedback metrics and / or training in industrial environments.

[0024] In an embodiment, a system for data collection, processing and utilization of signals from at least a first element in a first machine in an industrial environment includes a platform having a computing environment connected to a local data collection system, the local data collection system having a first sensor signal and a second sensor signal obtained from at least a first machine in the industrial environment. The system includes a first sensor located in the local data collection system and a second sensor located in the local data collection system, the local data collection system being configured to be connected to the first machine. The system also includes a crosspoint switch located in the local data collection system, the crosspoint switch having a plurality of inputs and a plurality of outputs, including a first input connected to the first sensor and a second input connected to the second sensor. The plurality of outputs include a first output and a second output, which are configured to switch between the following two situations: a situation where the first output is configured to switch between transmitting the first sensor signal and transmitting the second sensor signal, and a situation where there is a situation where the first sensor signal is transmitted from the first output and the second sensor signal is transmitted from the second output simultaneously. Each of the plurality of inputs is configured to be individually assigned to any one of the plurality of outputs. The unassigned output is configured to be turned off, thereby generating a high impedance state.

[0025] In an embodiment, the first sensor signal and the second sensor signal are continuous vibration data associated with an industrial environment. In an embodiment, the second sensor in the local data collection system is configured to be connected to a first machine. In an embodiment, the second sensor in the local data collection system is configured to be connected to a second machine in an industrial environment. In an embodiment, the computing environment of the platform is configured to compare the relative phases of the first and second sensor signals. In an embodiment, the first sensor is a single-axis sensor and the second sensor is a three-axis sensor. In an embodiment, at least one of the multiple inputs of the crosspoint switch includes internal protocol front-end signal conditioning for improving signal-to-noise ratio. In an embodiment, the crosspoint switch includes a third input configured with a continuously monitored alarm having a predetermined trigger condition when the third input is not assigned to any of the multiple outputs.

[0026] In an embodiment, a local data collection system includes a plurality of multiplexing units and a plurality of data collection units, the data collection units receiving a plurality of data streams from a plurality of machines in an industrial environment. In an embodiment, the local data collection system includes distributed complex programmable hardware device (CPLD) chips, each chip being dedicated to a data bus for logically controlling the plurality of multiplexing units and the plurality of data collection units receiving the plurality of data streams from the plurality of machines in the industrial environment. In an embodiment, the local data collection system is configured to provide high current input capability using solid state relays. In an embodiment, the local data collection system is configured to power off at least one of an analog sensor channel and a component board.

[0027] In an embodiment, the local data collection system includes an external voltage reference for the A / D zero reference that is independent of the voltage of the first sensor and the second sensor. In an embodiment, the local data collection system includes a phase-locked loop bandpass tracking filter that obtains slow RPM and phase information. In an embodiment, the local data collection system is configured to use an onboard timer to digitally derive the phase relative to at least one trigger channel and at least one input. In an embodiment, the local data collection system includes a peak detector that is configured to perform peak detection using automatic scaling using a separate analog-to-digital converter. In an embodiment, the local data collection system is configured to route from at least one of the original and buffered (one of) trigger channels to at least one of the multiple inputs. In an embodiment, the local data collection system includes at least one oversampling analog-to-digital converter that is configured to increase the input oversampling rate to reduce the sample rate output and minimize anti-aliasing filtering requirements. In an embodiment, each distributed CPLD chip dedicated to a data bus for logically controlling a plurality of multiplexing units and a plurality of data acquisition units includes a high-frequency crystal clock reference configured to be down-clocked by at least one distributed CPLD chip of at least one oversampling analog-to-digital converter to achieve a lower sampling rate without the need for digital resampling.

[0028] In an embodiment, the local data collection system is configured to acquire long data blocks at a single relatively high sampling rate, rather than multiple data sets extracted at different sampling rates. In an embodiment, the single relatively high sampling rate corresponds to a maximum frequency of about 40 kHz. In an embodiment, the long data block has a duration of more than 1 minute. In an embodiment, the local data collection system includes a plurality of data acquisition units, each data acquisition unit having an onboard card group, the onboard card group being configured to store calibration information and maintenance history of the data acquisition unit in which the onboard card group is located. In an embodiment, the local data collection system is configured to plan a data acquisition path based on a hierarchical template.

[0029] In an embodiment, the local data collection system is configured to manage data collection bands. In an embodiment, the data collection bands define a specific frequency band and at least one of a set of spectral peaks, a true peak level, a crest factor derived from a time waveform, and a complete waveform derived from a vibration envelope. In an embodiment, the local data collection system includes a neural network expert system that utilizes intelligent management of the data collection bands. In an embodiment, the local data collection system is configured to create data collection paths based on hierarchical templates, each hierarchical template including a machine-related data collection band associated with the data collection path. In an embodiment, at least one of the hierarchical templates is associated with a plurality of interconnected elements of a first machine. In an embodiment, at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and the second machine. In an embodiment, at least one of the hierarchical templates is associated with at least the first machine that is located adjacent to the second machine.

[0030] In an embodiment, the local data collection system includes a graphical user interface system configured to manage the data collection band. In an embodiment, the graphical user interface system includes an expert system diagnostic tool. In an embodiment, the platform includes cloud-based machine pattern analysis of state information from multiple sensors to provide expected state information of an industrial environment. In an embodiment, the platform is configured to provide self-organization of the data pool based on at least one of utilization metrics and revenue metrics. . In an embodiment, the platform includes a self-organizing group of industrial data collectors In an embodiment, the local data collection system includes a wearable tactile user interface for the industrial sensor data collector, the user interface having at least one of vibration, thermal, electrical, and sound output.

[0031] In an embodiment, the plurality of inputs of the crosspoint switch include a third input connected to the second sensor and a fourth input connected to the second sensor. The first sensor signal comes from a single-axis sensor located at a constant position associated with the first machine. In an embodiment, the second sensor is a three-axis sensor. In an embodiment, the local data collection system is configured to simultaneously record gapless digital waveform data from at least the first input, the second input, the third input, and the fourth input. In an embodiment, the platform is configured to determine a change in relative phase based on the gapless digital waveform data recorded simultaneously. In an embodiment, the second sensor is configured to be movable to a plurality of positions associated with the first machine while acquiring the gapless digital waveform data recorded simultaneously. In an embodiment, the plurality of outputs of the crosspoint switch include a third output and a fourth output. The second, third, and fourth outputs are distributed together to a series of three-axis sensors each located at a different position associated with the machine. In an embodiment, the platform is configured to determine a working deformation mode based on a change in relative phase and the gapless digital waveform data recorded simultaneously.

[0032] In an embodiment, the constant position refers to a position associated with a rotating shaft of the first machine. In an embodiment, the three-axis sensors in the series of three-axis sensors are respectively located at different positions on the first machine, but are respectively associated with different bearings in the machine. In an embodiment, the three-axis sensors in a series of three-axis sensors are respectively located at similar positions associated with similar bearings, but are respectively associated with different machines. In an embodiment, the local data collection system is configured to acquire simultaneously recorded gapless digital waveform data from the first machine while both the first machine and the second machine are running. In an embodiment, the local data collection system is configured to characterize the contributions from the first machine and the second machine in the gapless digital waveform data simultaneously recorded for the first machine. In an embodiment, the simultaneously recorded gapless digital waveform data has a duration of more than 1 minute.

[0033] In an embodiment, a method of monitoring a machine having at least one axis supported by a set of bearings includes monitoring a first data channel assigned to a single-axis sensor located at a constant position associated with the machine. The method includes monitoring second, third, and fourth data channels assigned to the axes of the three-axis sensor, respectively. The method includes simultaneously recording gapless digital waveform data from all data channels while the machine is operating, and determining a change in relative phase based on the digital waveform data.

[0034] In an embodiment, a triaxial sensor is placed at a plurality of positions associated with a machine while acquiring a digital waveform. In an embodiment, the second, third, and fourth channels are assigned together to a series of triaxial sensors respectively located at different positions associated with the machine. In an embodiment, data is received from all sensors simultaneously. In an embodiment, the method includes determining a working deformation mode based on relative phase change information and waveform data. In an embodiment, a constant position refers to a position associated with an axis of a machine. In an embodiment, triaxial sensors in a series of triaxial sensors are respectively located at different positions and are respectively associated with different bearings in a machine. In an embodiment, a constant position refers to a position associated with an axis of a machine. Triaxial sensors in a series of triaxial sensors are respectively located at different positions and are respectively associated with different bearings supporting an axis in a machine.

[0035] In an embodiment, the method includes monitoring a first data channel assigned to a single-axis sensor located at a constant position on a second machine. The method includes monitoring second, third, and fourth data channels respectively assigned to axes of a three-axis sensor located at a position associated with the second machine. The method also includes simultaneously recording gapless digital waveform data for all data channels from the second machine while both machines are running. In an embodiment, the method includes characterizing the contribution from each machine in the gapless digital waveform data simultaneously from the second machine.

[0036] In an embodiment, a method for data collection, processing, and utilization of signals by a platform monitoring at least a first element of a first machine in an industrial environment includes automatically acquiring at least a first sensor signal and a second sensor signal using a computing environment by monitoring a local data collection system of at least the first machine. The method includes connecting a first input of a crosspoint switch of the local data collection system to a first sensor and connecting a second input of the crosspoint switch to a second sensor in the local data collection system. The method includes switching between the following two situations: a situation where a first output of the crosspoint switch alternates between transmitting at least a first sensor signal and a second sensor signal, and a situation where there is a simultaneous transmission of the first sensor signal from the first output of the crosspoint switch and the transmission of the second sensor signal from the second output of the crosspoint switch. The method also includes shutting down an unassigned output of the crosspoint switch to a high impedance state.

[0037] In an embodiment, the first sensor signal and the second sensor signal are continuous vibration data from an industrial environment. In an embodiment, the second sensor in the local data collection system is connected to the first machine. In an embodiment, the second sensor in the local data collection system is connected to a second machine in the industrial environment. In an embodiment, the method includes automatically comparing the relative phases of the first and second sensor signals using a computing environment. In an embodiment, the first sensor is a single axis sensor and the second sensor is a three axis sensor. In an embodiment, at least a first input of the crosspoint switch includes internal protocol front end signal conditioning for improving signal to noise ratio.

[0038] In an embodiment, the method includes continuously monitoring at least one third input of the crosspoint switch with an alarm, the alarm having a predetermined trigger condition when the third input is not assigned to any output on the crosspoint switch. In an embodiment, the local data collection system includes a plurality of multiplexing units and a plurality of data acquisition units, the data acquisition units receiving a plurality of data streams from a plurality of machines in an industrial environment. In an embodiment, the local data collection system includes a distributed complex programmable hardware device (CPLD) chip, each chip being dedicated to a data bus for logically controlling a plurality of multiplexing units and a plurality of data acquisition units receiving a plurality of data streams from a plurality of machines in an industrial environment. In an embodiment, the local data collection system uses solid-state relays to provide high current input capability.

[0039] In an embodiment, the method includes powering off at least one of an analog sensor channel of a local data collection system and a component board. In an embodiment, the local data collection system includes an external voltage reference for an A / D zero reference that is independent of the voltage of the first sensor and the second sensor. In an embodiment, the local data collection system includes a phase-locked loop bandpass tracking filter that acquires slow RPMs and phase information. In an embodiment, the method includes digitally deriving phase relative to at least one of a plurality of inputs on at least one trigger channel and a crosspoint switch using an onboard timer.

[0040] In an embodiment, the method includes using a separate analog-to-digital converter to perform peak detection by automatic scaling with a peak detector. In an embodiment, the method includes routing at least one trigger channel of the original and buffered (one of them) to at least one of the multiple inputs on the crosspoint switch. In an embodiment, the method includes increasing the input oversampling rate using at least one oversampling analog-to-digital converter to reduce the sampling rate output and minimize the anti-aliasing filtering requirements. In an embodiment, each distributed CPLD chip dedicated to the data bus for logical control of multiple multiplexing units and multiple data acquisition units includes a high frequency crystal clock reference, which is down-converted by at least one of the distributed CPLD chips of at least one oversampling analog-to-digital converter to achieve a lower sampling rate without digital resampling. In an embodiment, the method includes acquiring long data blocks at a single relatively high sampling rate rather than multiple data sets extracted at different sampling rates. In an embodiment, the single relatively high sampling rate corresponds to a maximum frequency of about 40 kilohertz. In an embodiment, the long data block has a duration of more than 1 minute. In an embodiment, the local data collection system includes a plurality of data collection units, each of which has an onboard card group that stores calibration information and maintenance history of the data collection unit in which the onboard card group is located.

[0041] In an embodiment, the method includes planning a data collection path based on a hierarchical template associated with at least a first element of a first machine in an industrial environment. In an embodiment, a local data collection system manages data collection bands that define a specific frequency band and at least one of a set of spectral peaks, a true peak level, a crest factor derived from a time waveform, and a complete waveform derived from a vibration envelope. In an embodiment, the local data collection system includes a neural network expert system that utilizes intelligent management of the data collection bands. In an embodiment, the local data collection system creates a data collection path based on hierarchical templates, each hierarchical template including a data collection band associated with a machine associated with the data collection path. In an embodiment, at least one of the hierarchical templates is associated with a plurality of interconnected elements of the first machine. In an embodiment, at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and the second machine. In an embodiment, at least one of the hierarchical templates is associated with at least the first machine that is located adjacent to the second machine.

[0042] In an embodiment, the method includes a graphical user interface system that controls a local data collection system to manage the data collection band. The graphical user interface system includes an expert system diagnostic tool. In an embodiment, the computing environment of the platform includes a cloud-based machine pattern analysis of state information from multiple sensors to provide expected state information of the industrial environment. In an embodiment, the computing environment of the platform provides data pool self-organization based on at least one of utilization metrics and revenue metrics. In an embodiment, the computing environment of the platform includes a self-organizing group of industrial data collectors. In an embodiment, each of the multiple inputs of the crosspoint switch can be individually assigned to any of the multiple outputs of the crosspoint switch. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figures 1 to 5 are schematic diagrams respectively showing multiple parts of an overall view of an industrial IoT data collection, monitoring and control system according to the present invention;

[0044] Figure 6 is a schematic diagram of a platform including a local data collection system arranged in an industrial environment according to the present invention, the platform for collecting data from or relating to environmental elements such as machines, components, systems, subsystems, environmental conditions, states, workflows, processes and other elements;

[0045] Figure 7 is a schematic diagram showing elements of an industrial data collection system for collecting analog sensor data in an industrial environment according to the present invention;

[0046] Figure 8 is a schematic diagram of a rotating or vibrating machine having a data acquisition module configured to collect waveform data according to the present invention;

[0047] Fig. 9 is a schematic diagram of an exemplary three-axis sensor mounted to a motor bearing of an exemplary rotating machine according to the present invention;

[0048] Fig.10 and Fig.11 is a schematic diagram of an exemplary three-axis sensor and a single-axis sensor mounted to an exemplary rotating machine according to the present invention;

[0049] Fig.12 is a schematic diagram of a plurality of surveyed machines having a sensor set according to the present invention;

[0050] Fig.13 is a schematic diagram of a hybrid method of relational metadata and binary storage according to the present invention;

[0051] Fig.14 is a schematic diagram of components and interactions of a data collection architecture involving application of cognitive and machine learning systems to data collection and processing according to the present invention;

[0052] Fig.15 is a schematic diagram of components and interactions of a data collection architecture involving application of a platform having a cognitive data marketplace according to the present invention;

[0053] Fig.16 is a schematic diagram of components and interactions of a data collection architecture involving a self-organizing group of application data collectors according to the present invention;

[0054] Fig.17 is a schematic diagram of components and interactions of a data collection architecture involving application of a tactile user interface according to the present invention. DETAILED DESCRIPTION

[0055] Detailed embodiments of the present invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely exemplary of the present invention, which can be implemented in a variety of forms. Therefore, the specific structural and functional details disclosed herein should not be interpreted as limiting, but merely as a basis for the claims and as a representative basis for guiding those skilled in the art to utilize the present invention in virtually any suitable specific structure.

[0056] The terms "a" and "an", as used herein, are defined as one or more. The term "another", as used herein, is defined as at least a second or more. The terms "including" and / or "having", as used herein, are defined as comprising (ie, open ended meanings).

[0057] Although only a few embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that many changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention as described in the claims below. All foreign and domestic patent applications and patents and all other publications cited herein are incorporated herein in their entirety to the extent permitted by law.

[0058] Figures 1 to 5 Portions of an overall view of an Industrial IoT data collection, monitoring and control system 10 are shown. Figure 2 Show Figure 1-5 The upper left portion of the schematic diagram of the industrial IoT system 10 is shown. Figure 2 Includes a mobile ad hoc network (MANET) 20 that can form secure temporary network connections 22 (sometimes connected, sometimes isolated) with a cloud 30 or other remote networking system so that network functions can be achieved through the MANET 20 within the environment without the need for an external network, but at other times, information can be sent and received at a central location. This enables industrial environments to take advantage of networking and control technologies while also providing security, such as protection against cyber attacks. The MANET 20 can use cognitive radio technology 40, including technologies that constitute the equivalent of the IP protocol, such as routers 42, MAC 44, and physical layer technology 46. The figure also shows network-sensitive or network-aware data transmission through the network to and from data collection devices or heavy industrial machines.

[0059] Figure 3 Show Figure 1-5 The upper right portion of a schematic diagram of an industrial IoT system 10 is shown. This includes 50 smart data collection technologies deployed locally at the edge of the IoT deployment where the heavy industrial machinery is located. This includes a variety of sensors 52, IoT devices 54, data storage capabilities 56 (including smart, self-organizing memory), sensor fusion (including self-organizing sensor fusion), etc. Figure 3 Interfaces for data collection are shown, including a multi-sensory interface, a tablet computer, a smart phone 58, etc. Figure 3 Also shown is a data pool 60 which may collect data published by machines or sensors detecting machine conditions, such as data for later consumption by local or remote intelligence. A distributed ledger system 62 may distribute storage to local storage of various environmental elements or more broadly throughout the system.

[0060] Figure 1 Show Figure 1-5The middle part of the schematic diagram of the industrial IoT system shown in FIG. This includes the use of network coding (including self-organizing network coding and / or automated configuration), which can configure the network coding model based on feedback measurements, network conditions, etc., for efficient transmission of large amounts of data between the data collection system and the cloud across the network. Various intelligence, analysis, remote control, remote operation, remote optimization, etc. functions can be deployed in the cloud or on the premises of the enterprise owner or operator, including Figure 1 This includes a variety of storage configurations, which may include distributed ledger storage for supporting system transaction data or other elements.

[0061] Figure 1 , Figure 4 and Figure 5 Show Figure 1-5 The lower right corner of the schematic diagram of the industrial IoT system shown. This includes a programmatic data market 70, which can be a self-organizing market, such as for providing data collected in an industrial environment, such as data collectors, data pools, distributed ledgers, and the like disclosed herein and Figure 1-5 Other components shown in . Figure 1 , Figure 4 and Figure 5 Also shown are on-device sensors 80, such as for storing device data from multiple simulated sensors 82 on the device, which can be analyzed locally or in the cloud by machine learning 84, including training the machine based on an initial model created by people and enhanced by providing feedback (such as based on successful measurements) when operating the methods and systems disclosed herein. Figures 1 to 5 Additional details of the various components and subcomponents of the .

[0062] In an embodiment, a method and system for data collection, processing, and utilization system (referred to herein as platform 100) in an industrial environment is provided. Figure 6 , the platform 100 may include a local data collection system 102, which may be disposed in an environment 104, such as an industrial environment, for collecting data from or related to elements of the environment, such as machines, components, systems, subsystems, environmental conditions, states, workflows, processes, and other elements. The platform 100 may be connected to or include Figure 1- Multiple portions of the industrial IoT data collection, monitoring and control system 10 shown in Figure 5. The platform 100 may include a network data transmission system 108, such as for transmitting data to and from the local data collection system 102 via a network 110, such as for transmitting data to a host processing system 112, for example, a system provided in a cloud computing environment or at an enterprise site or composed of distributed components that interact with each other to process data collected by the local data collection system 102. The host processing system 112, in some cases simply referred to as the host system 112, may include a variety of systems, components, methods, processes, facilities, etc. for implementing automated or automated-assisted data processing, such as for monitoring one or more environments 104 or the network 110 or for remotely controlling one or more elements in the local environment 104 or in the network 110. The platform 100 may include one or more local autonomous systems 114, such as for implementing autonomous behavior, such as reflecting artificial or machine-based intelligence or such as for implementing automatic operations based on applying a set of rules or models to input data from the local data collection system 102 or from one or more input sources 116, which may include information feedback and input from various sources, including information feedback and input from the local environment 104, the network 110, the host system 112, or one or more external systems, databases, etc. The platform 100 may include one or more intelligent systems 118, which may be disposed in, integrated with, or as input to one or more components of the platform 100. Details of these and other components of the platform 100 are provided herein.

[0063] Intelligent systems may include cognitive systems 120, such as achieving a degree of cognitive behavior due to coordinated grid, peer, ring, serial, and other architectures of processing elements, where one or more node elements coordinate with each other to provide unified and coordinated behavior to assist in processing, communication, data collection, etc. Figure 2The MANET 20 shown may also use cognitive radio technology, including technologies that constitute the equivalent of the IP protocol, such as routers 42, MAC 44, and physical layer technology 46. In one example, the cognitive system technology stack may include multiple examples disclosed in U.S. Patent No. 8,060,017 issued on November 15, 2011 to Schlicht et al., and incorporated herein by reference as if fully set forth herein. The intelligent system may include a machine learning system 122, such as for learning one or more data sets, such as information collected using the local data collection system 102 or other information from the input source 116, so as to be used to identify states, objects, events, patterns, conditions, etc., which are then processed by the host system 112 as input to multiple parts of the platform 100 components or industrial IoT data collection, monitoring and control system 10. Learning may be manually supervised or fully automated learning, such as using one or more input sources 116 to provide data sets and information about the items to be learned. Machine learning can use one or more models, rules, semantic understanding, workflows, or other structured or semi-structured understandings of the world to automatically optimize the control of a system or process based on feedback or feedforward to a working model of the system or process. Such machine learning techniques for semantic and scene understanding, workflows, or other structured or semi-structured understandings are disclosed in U.S. Patent No. 8,200,775, issued to Moore on June 12, 2012. Machine learning can be used to improve the aforementioned techniques, for example, by adjusting one or more weights, structures, rules, etc. (such as changing functions within a model) based on feedback (such as the success of the relevant model in a given situation) or based on iteration (such as in a recursive process). If a full understanding of the underlying structure or behavior of the system is not yet known, insufficient data is available, or other solutions are preferred for a variety of reasons, machine learning can also be implemented in the absence of an underlying model; that is, the input source can be weighted, structured, etc. in the machine learning facility without considering any prior understanding of the structure, and the results (such as the results of the success measurement based on the achievement of multiple desired goals) can be continuously provided to the machine learning system so that it can learn how to achieve the calibrated goals. For example, the system can learn to identify failures, identify patterns, develop models or functions, formulate rules, optimize performance, minimize failure rates, optimize revenue, optimize resource utilization, optimize processes (e.g., traffic flow), or optimize multiple other parameters that may be correlated with successful outcomes, such as outcomes in various environments. Machine learning can use genetic programming techniques, for example, to upgrade or downgrade one or more input sources, structures, data types, objects, weights, nodes, links, or other factors based on feedback so that successful elements appear over a series of generations.For example, alternative available sensor inputs to the data collection system 102 may be arranged in alternative configurations and permutations such that the system may use genetic programming techniques for a series of data collection events to determine a permutation that provides a successful outcome based on a variety of conditions (e.g., conditions of platform 100 components, conditions of network 110, conditions of data collection system 102, conditions of environment 104), etc. In an embodiment, local machine learning may turn one or more sensors in a multi-sensor data collector 102 in a permutation on or off over time while tracking successful outcomes, such as contribution to successful prediction of failures, contribution to performance metrics (e.g., efficiency, effectiveness, return on investment, profitability, etc.), contribution to optimization of one or more parameters, identification of patterns (e.g., related to threats, failure modes, success patterns, etc.), etc. For example, the system may learn which groups of sensors should be turned on or off under given conditions to achieve the highest value utilization of the data collector 102. In an embodiment, similar techniques may be used to handle data transmission optimization in the platform 100, such as in the network 110, such as by using genetic programming or other machine learning techniques to learn to configure network elements, such as configuring network transmission paths, configuring network coding types and architectures, configuring network security elements, etc.

[0064] In an embodiment, the local data collection system 102 may include a high-performance multi-sensor data collector with a number of novel features for collecting and processing analog and other sensor data. In an embodiment, the local data collection system 102 may be deployed to Figure 3 The local data collection system 102 can also be deployed to monitor other machines, such as Fig. 9 and Fig.10 The machine 2300 shown in FIG. Fig.12 The machines 2400, 2600, 2800, 2950, ​​3000 and Fig.13 3202, 3204 shown in the figure. The data collection system 102 can have an onboard intelligent system (such as learning to optimize the configuration and operation of the data collector, such as configuring the arrangement and combination of sensors based on scenarios and conditions). In one example, the data collection system 102 includes a crosspoint switch 130. The automated intelligent configuration of the local data collection system 102 can be based on multiple types of information, such as from multiple input sources, such as information based on the following items: available power, power requirements of sensors, values ​​of collected data (such as based on feedback information from other elements of the platform 100), relative values ​​of information (such as based on the availability of other sources of the same or similar information), power availability (such as for powering sensors), network conditions, environmental conditions, operating status, operating scenarios, operating events, and others.

[0065] Figure 7 1 shows elements and subcomponents of a data collection and analysis system 1100 for sensor data (eg, simulated sensor data) collected in an industrial environment. Figure 7 As shown, embodiments of the methods and systems disclosed herein may include hardware with several different modules starting with a multiplexer (Mux) 1104. In an embodiment, the Mux 1104 is composed of a main board and an option board 1108. The main board is where the sensors connect to the system. These connections are located at the top for easy installation. Then, there are many settings on the underside of this board and on the Mux option board, which connects to the main board through two connectors at both ends of the board. In an embodiment, the Mux option board has a male connector that engages with the female connector on the main Mux board. This allows them to be stacked on top of each other, taking up less space.

[0066] In an embodiment, the main Mux board is then connected to the mother analog board (e.g., through 4 simultaneous channels) and the daughter analog board 1110 (e.g., through 4 additional channels, a total of 8 channels) via cables where some signal conditioning (such as hardware integration) occurs. These signals are then moved from the analog board 1110 to the anti-aliasing board, which removes some potential aliasing. The rest of the aliasing is done on the oversampling board 1112, which is connected via a cable. The oversampling board 1112 provides more aliasing protection and other conditioning and digitization operations to the signal. Next, the data moves to the jennic board 1114 for more digitization operations and transfer to the computer via USB or Ethernet. In an embodiment, the jennic board 1114 can be replaced with a pic board 1118 to achieve more advanced and efficient data collection and communication. Once the data is moved to the computer software 1102, the computer software 1102 can operate to process the data to display trends, spectra, waveforms, statistics and analysis.

[0067] In an embodiment, the system is designed to receive all types of data from a few volts to 4-20mA signals. In an embodiment, open data storage and communication formats can be used. Some proprietary parts of the system will be included in the analysis and reporting. In an embodiment, smart band analysis is a way to split the data into easily analyzed parts that can be combined with other smart bands to produce new simpler but complete analysis. In an embodiment, this unique information is extracted and graphics are used to illustrate these conditions, because picture explanations are more helpful to users. In an embodiment, complex programs and user interfaces are simplified to enable any user to manipulate the data like an expert.

[0068] In an embodiment, the system essentially works in one big loop. It starts with a generic user interface in software. Most, but not all, online systems require the OEM to create or develop the GUI system 1124. In an embodiment, fast path creation can utilize hierarchical templates. In an embodiment, a graphical user interface (GUI) is created so that any ordinary user can fill in the information themselves using a simple template. After creating the template, the user can copy and paste any required information. In addition, the user can also develop their own templates for future use and institutionalize knowledge. When the user enters all user information and connects all of the user's sensors, the user can start the system to acquire data. In some applications, rotating machines may generate charges that can damage electrical equipment. In an embodiment, to eliminate the impact of this charge on the equipment, unique electrostatic protection for trigger and vibration inputs is set before the Mux and DAQ hardware to dissipate the charge when the signal is passed from the sensor to the hardware. In an embodiment, the Mux and analog boards can also use solid-state relays and design topologies to provide front-end circuits and wider traces to achieve high current input capabilities, which enables the system to handle high current inputs when necessary.

[0069] In this example, the important part before the Mux is the front signal conditioning on the Mux, which is used to improve the signal to noise ratio that provides the front end signal conditioning. Most multiplexers are added later, and OEMs often do not pay attention to or even consider the quality of the signal coming out of the multiplexer. As a result, the signal quality can drop by as much as 30dB or more. Every system is only as strong as its weakest link, so whether the signal to noise ratio of a 24-bit DAQ is 110dB, the signal quality is lost by the Mux. If the signal to noise ratio in the Mux drops to 80dB, it may not be much better than a 16-bit system from 20 years ago.

[0070] In embodiments, in addition to providing a better signal, multiplexers can also play a key role in enhancing the system. A truly continuous system can monitor every sensor at all times, but is often expensive. Multiplexer systems typically monitor only a certain number of channels at a time and switch one by one among a larger set of sensors. As a result, sensors that are not being collected are not monitored; if the level rises, the user may never know. In embodiments, a multiplexer continuous monitoring alarm function can provide continuous monitoring alarms for the multiplexer by providing circuits on the multiplexer that can measure input levels against known alarms even when the data acquisition ("DAQ") is not monitoring the input. Essentially, this enables the system to monitor continuously, but it cannot capture data about the problem in a timely manner like a truly continuous system. In embodiments, this alarm function is combined with adaptive scheduling technology for continuous monitoring, and the continuous monitoring system software adapts and adjusts the data collection order based on statistics, analysis, data alarms, and dynamic analysis. The system will be able to quickly collect dynamic spectrum data about the alarming sensor immediately after the alarm is issued.

[0071] Another limitation of multiplexers is that they typically have a limited number of channels. In an embodiment, a distributed complex programmable logic device (CPLD) chip is used along with a dedicated bus for logic control of multiple Mux and data acquisition sections, so that the CPLD can control multiple Mux and DAQs, thereby making the number of channels that the system can handle unlimited. In an embodiment, multiplexers and DAQs can be stacked together to provide additional input and output channels connected to the system.

[0072] In addition to the limited number of channels, multiplexers can also typically only collect data from sensors in the same group. This is very limiting for detailed analysis, as being able to simultaneously examine data from sensors on the same machine is of great value. In an embodiment, this problem is addressed by collecting variable groupings of vibration input channel data using analog crosspoint switches, which are commonly used in the telephone industry and provide a matrix circuit that enables the system to access any group of 8 channels from the total number of input sensors.

[0073] In an embodiment, the system provides a phase locked loop band pass tracking filter method for acquiring slow RPMs and phase for balancing, thereby remotely balancing slow machinery in a paper mill, etc. and providing additional analysis based on its data.

[0074] In an embodiment, multiple multiplexers are controlled using a distributed CPLD chip with a dedicated bus to provide logic control of multiple Mux and data acquisition sections, this capability can be enhanced by utilizing a hierarchical multiplexer that allows multiple DAQs to collect data from multiple multiplexers. In an embodiment, this allows for faster data collection and more channels to collect data simultaneously for more complex analysis. In an embodiment, the Mux can be slightly adapted to be portable and use the data acquisition resident feature, which can make the SV3X DAQ a protected system.

[0075] In an embodiment, once the signals leave the multiplexers and hierarchical Mux, they are moved to analog boards with other enhancements. In an embodiment, the ability to power down analog channels when not in use and other energy saving measures including the ability to power down component boards enable the system to power down channels on the main and sub-analog boards to save energy. In an embodiment, this can bring the same energy saving benefits to protection systems, especially when operating on batteries or solar power. In an embodiment, to maximize the signal-to-noise ratio and provide the best data, a peak detector routed to a separate A / D and used for automatic conversion will provide the system with the highest peak in each set of data so it can quickly convert the data to that peak. In an embodiment, improved integration using both analog and digital methods forms an innovative hybrid integration, which will also improve or maintain the highest possible signal-to-noise ratio.

[0076] In an embodiment, a segment of the analog board allows the trigger channel (raw or cached) to be routed to other analog channels. This allows the user to route the trigger to any channel for analysis and troubleshooting. In an embodiment, once the signals leave the analog board, they will enter the oversampling board, where the precise voltage reference for the A / D zero reference provides more accurate DC sensor data. The high speed of oversampling also supports oversampling the higher inputs of the oversampled A / D for the lower sample rate output to minimize the anti-aliasing filter requirements, thereby oversampling the data at the higher inputs, which will minimize the anti-aliasing requirements. In an embodiment, the CPLD can be used as a clock divider for the oversampled A / D to achieve a lower sampling rate without digital resampling, so that the oversampled A / D can achieve a lower sampling rate without digital resampling of the data.

[0077] In an embodiment, the data is then moved from the oversampling board to the Jennic board where the phase is digitally derived relative to the input and trigger channels using onboard timers to digitally derive the phase from the input signal and trigger. In an embodiment, the jennic board can also store calibration data and system maintenance repair history data in the onboard card set. In an embodiment, the jennic board will be able to achieve the acquisition of long blocks of data at a high sampling rate, rather than multiple data sets extracted at different sampling rates, allowing the data to be streamed and long blocks of data to be acquired for advanced analysis in the future.

[0078] In an embodiment, after the data passes through the jennic board, it will be transferred to the computer. Once installed on the computer, the software has a variety of enhanced functions to improve the system's analytical capabilities. In an embodiment, hierarchical templates are used to create fast routes and simple templates are used to provide fast route creation capabilities for all devices, which also accelerates the deployment of the software. In an embodiment, the software will be used to increase the intelligence of the system. It will start with the expert system GUI graphical method of defining the smart band and diagnosis of the expert system, which will provide a graphical expert system with a simplified user interface, so that anyone can develop complex analysis. In an embodiment, this user interface will be centered around the smart band, which is a simplified way for ordinary users to achieve complex but flexible analysis. In an embodiment, the smart band will be paired with a self-learning neural network to achieve even more advanced analysis methods. In an embodiment, this system will also use a hierarchical structure of additional analytical insights of the machine. A key part of predictive maintenance is the ability to learn from known information during maintenance or inspection. In an embodiment, the graphical method of back calculation can improve the smart band and correlation based on known faults or problems.

[0079] In an embodiment, a bearing analysis method is provided in addition to the detailed analysis performed by the smart belt. In recent years, there has been a strong drive to save energy, which has led to an influx of variable frequency drives. In an embodiment, torsional vibration detection and analysis using transient signal analysis provides advanced torsional vibration analysis to diagnose machinery related to torsional forces (such as machinery with rotating components) in a more comprehensive manner. In an embodiment, the system is able to deploy multiple intelligent functions on its own to achieve better data analysis and more comprehensive analysis. In an embodiment, this intelligence will start with intelligent routing, where the software's intelligent routing is able to simultaneously adjust the sensor data it collects to obtain additional correlation intelligence. In an embodiment, the intelligent operational data storage ("ODS") allows the system to select the collection of deformation modal analysis to further examine the condition of the machinery. In an embodiment, in addition to changing the routing, the adaptive scheduling technology for continuous monitoring allows the system to change the scheduling data collected for full spectrum analysis on multiple (e.g., 8) related channels. In an embodiment, the intelligence will provide data to achieve extended statistical capabilities for continuous monitoring and environmental local vibration for analysis, which combines ambient temperature and local temperature and vibration level changes for identifying mechanical problems.

[0080] Embodiments of the methods and systems disclosed herein may include a self-contained DAQ box. In embodiments, a data acquisition device may be controlled by a PC to implement the intended data acquisition commands. In embodiments, the system may be a self-contained system and capable of data acquisition, processing, analysis, and monitoring independent of external PC control. Embodiments of the methods and systems disclosed herein may include a secure digital ("SD") card memory. In embodiments, SD cards are utilized to provide significant additional storage capabilities, such as cameras, smart phones, and the like. This is critical for monitoring applications that can permanently store critical data. In addition, if a power failure occurs, the latest data may be stored even if it is not offloaded to another system. Embodiments of the methods and systems disclosed herein may include a data acquisition ("DAQ") system. The current trend is to enable DAQ systems to communicate with the outside world as much as possible, typically in the form of a network including a wireless network. However, in the past, a dedicated bus was typically used to control a DAQ system having a microprocessor or microcontroller / microprocessor paired with a PC, and now there is a greater need for networking, and thus out of the environment in which this new design prototype was produced. In an embodiment, multiple microprocessors / microcontrollers or dedicated processors can be used to achieve multiple different aspects of DAQ functionality, with one or more processor units focusing primarily on communication with the outside world. This eliminates the need to constantly interrupt some major processes, including control of signal conditioning circuits, using A / D to trigger raw data acquisition, directing A / D outputs directly to suitable onboard memory and processing the data. In an embodiment, a dedicated microcontroller / microprocessor is designated for all communications with the outside world. These include USB, Ethernet, and wireless, which have the ability to provide one or more IP addresses for hosting pages. All communications with the outside world are completed using simple text-based menus. A series of commonly used commands (actually more than 100) are provided, such as InitializeCard (initialization of the card), AcquireData (data acquisition), StopAcquisition (stop acquisition), RetrieveCalibrationInfo (retrieve calibration information), etc. In an embodiment, intensive signal processing activities, including resampling, weighting, filtering, and spectral processing, can be performed by dedicated processors, including one or more of field programmable gate arrays ("FPGAs"), digital signal processors ("DSPs"), microprocessors, and microcontrollers. In an embodiment, this subsystem can communicate with the communication processing segment via a dedicated hardware bus. Dual-port memory, signal logic, etc. will help achieve this goal. This embodiment can not only significantly improve efficiency, but also significantly improve processing capabilities, including streaming of data and other high-end analysis techniques.

[0081] Embodiments of the methods and systems disclosed herein may include sensor overload identification. A monitoring system is required to identify when a sensor is overloaded. A monitoring system can identify when its system is overloaded, but in some embodiments, the system can look at the voltage of a sensor to determine if the overload is coming from that sensor, which helps the user get another sensor that is more suitable for the situation or try to recollect data. There are often situations involving high frequency inputs that will saturate a standard 100mv / g sensor (which is the most commonly used in the industry), and the ability to sense the overload is needed to improve the data quality for better analysis.

[0082] Embodiments of the methods and systems disclosed herein may include RFID on accelerometers and RFID on inclinometers or other sensors, whereby the sensors can tell the system / software what machine / bearing and orientation it is connected to, and can automatically set it up in the software to store data without the user having to tell it. In embodiments, the user can place the system on any one or more machines, and it will automatically set itself up and be ready for data collection in seconds.

[0083] Various embodiments of the methods and systems disclosed herein may include ultrasonic online monitoring by placing ultrasonic sensors inside transformers, MCCs, circuit breakers, etc., where the system will monitor through the sound spectrum, continuously looking for patterns that identify arcs, coronas, and other electrical problems that indicate faults or problems. In embodiments, an analysis engine is used in ultrasonic online monitoring and identification of other faults by combining this data with other parameters such as vibration, temperature, pressure, heat flux, magnetic field, electric field, current, voltage, capacitance, inductance, and combinations thereof (such as single ratio).

[0084] Embodiments of the methods and systems disclosed herein may include the use of analog crosspoint switches for collecting variable groups of vibration input channels. For vibration analysis, it is very beneficial to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or multiple machines) in different orientations. By taking readings simultaneously, for example, the relative phases of these inputs can be compared to diagnose multiple mechanical faults. In addition, other types of cross-channel analysis such as cross-correlation, transfer functions, and operating deformation modes ("ODS") can also be performed. Current systems using conventional fixed-group multiplexers can only compare a limited number of channels assigned to a specific group at installation time (based on the # channels in each group). The only way to provide some flexibility is to overlap channels in the system or incorporate a lot of redundancy, both of which can significantly increase overhead (in some cases, cost increases exponentially with flexibility). The simplest Mux design selects one of multiple inputs and routes it to a single output line. The grouped design consists of a set of simple building blocks, each of which processes a fixed group of inputs and routes to its corresponding output. Typically, these inputs do not overlap, so the input of one Mux group cannot be routed to another group. Unlike conventional Mux chips, which typically switch a fixed group or groups of fixed channel selections to a single output, such as in groups of 2, 4, 8, etc., a crosspoint 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 in fact too noisy for analog applications such as vibration analysis; however, recent advances in the technology now make this possible. Another advantage of a crosspoint Mux is the ability to disable the outputs by placing them in a high impedance state. This is ideal for output busses, so that multiple Mux cards can be stacked and their output busses can be connected together without the need for a bus switch.

[0085] Embodiments of the methods and systems disclosed herein may include front-end signal conditioning on the Mux to improve the signal-to-noise ratio. Multiple embodiments may perform signal conditioning (such as range / gain control, integration, filtering, etc.) on vibration and other signal inputs before the Mux switches in order to achieve the highest signal-to-noise ratio.

[0086] Embodiments of the methods and systems disclosed herein may include a data acquisition resident feature. In embodiments, continuous monitoring of the Mux bypass provides a mechanism to continuously monitor channels not currently being sampled by the Mux system for significant alarms using a filtered peak hold circuit or multiple trigger conditions of similar functionality, and then pass these alarms to the monitoring system in a cost-effective manner using hardware interrupts or other means.

[0087] Multiple embodiments of the methods and systems disclosed herein may include the use of distributed CPLD chips with dedicated buses to perform logical control of multiple MUX and data acquisition sections. A large number of switching operations are required to implement interfaces with multiple types of predictive maintenance and vibration transducers. This includes AC / DC coupling, 4-20 interface, IEPE (transducer power), channel power reduction (for converting operational amplifier power), single-ended or differential grounding options, etc. In addition, control of digital ports is required for range and gain control, hardware integrated switching, AA filtering and triggering. This logic can be performed by a series of CPLD chips strategically positioned for the controlled tasks. A single large CPLD requires long circuit paths and these circuit paths have considerable density at the large chip. In an embodiment, distributed CPLDs not only solve these problems, but also provide considerable flexibility. A bus is created in which each CPLD with a fixed assignment has its own unique device address. For multiple boards, such as multiple Mux boards, jumpers are provided for setting multiple addresses. In another example, three bits allow up to 8 boards that can be cross-configured. In an embodiment, the bus protocol is defined so that each CPLD on the bus can be addressed individually or as a group.

[0088] Embodiments of the methods and systems disclosed herein may include high current input capability using solid state relays and design topologies. Typically, vibration data collectors are not designed to handle large input voltages because they are costly and generally not needed. As technology improves and monitoring costs drop significantly, these data collectors need to acquire many different types of PM data. In an embodiment, one approach is to use mature OptoMOS technology rather than the more conventional reed relay approach, which allows for switching of front high voltage signals. Many of the historical issues regarding nonlinear zero crossings or other nonlinear solid state behavior have been eliminated with respect to the passage of weak buffered analog signals. Additionally, in an embodiment, the PCB is topologically arranged to place all single channel input circuits as close to the input connector as possible.

[0089] Embodiments of the methods and systems disclosed herein provide the ability to power down unused analog channels, power down component boards, and implement other energy conservation measures. In embodiments, powering down analog signal processing op amps for unselected channels and powering down component boards and other hardware via low-level firmware of the DAQ system makes high-level application control relatively easy relative to energy conservation capabilities. Explicit control of the hardware is possible, but not required by default.

[0090] Embodiments of the methods and systems disclosed herein may include unique electrostatic protection for trigger and vibration inputs. In many critical industrial environments where large electrostatic forces may be generated, such as slow speed balancing using large conveyors, appropriate transducer and trigger input protection is required. In embodiments, a low-cost but highly effective method is described for such protection without the need for external supplemental equipment.

[0091] Embodiments of the methods and systems disclosed herein may include an accurate voltage reference for the A / D zero reference. Some A / D chips provide their own internal zero voltage reference to be used as an intermediate scale value for external signal conditioning circuits to ensure that the A / D and external op amp use the same reference. While this seems reasonable in theory, there are practical complexities. In many cases, these references are themselves based on the supply voltage using a resistor divider. For many current systems, especially those powered from a PC via USB or similar buses, this provides an unreliable reference because the supply voltage tends to change significantly with changes in load. This is especially true for oversampling A / D chips that require a lot of signal processing. Although the offset may drift with the load, problems arise if the reading needs to be calibrated digitally. The voltage offset represented by the count from the A / D is usually modified digitally to compensate for the DC offset. However, for this case, if the appropriate calibration offsets are determined for one set of load conditions, they will not be applicable to other conditions. The absolute DC offset represented by the count will no longer apply. Then, it is necessary to calibrate for all load conditions which becomes complicated, unreliable and ultimately difficult to manage. In an embodiment, an external voltage reference is used, which is independent only from the supply voltage, to serve as a zero offset.

[0092] Embodiments of the methods and systems disclosed herein may include a phase-locked loop bandpass tracking filter method for acquiring slow PRMs and phase for balancing purposes. For balancing purposes, it is sometimes necessary to balance at very slow speeds. Typical tracking filters can be constructed based on phase-locked loop or PLL designs. However, stability and speed range are the most important considerations. In an embodiment, multiple digitally controlled switches are used to select appropriate RC and damping constants. After measuring the frequency of the access tachometer (tach) signal, switching can be fully automatic. Embodiments of the methods and systems disclosed herein may include using onboard timers to digitally derive the phase relative to the input and trigger channels. In an embodiment, the digital phase derivation uses a digital timer to determine the exact delay from the trigger event to the precise start of data acquisition. This delay or offset is then further refined using interpolation to obtain a more precise offset, which is then applied to the phase determined by the analysis of the acquired data so that the phase is "essentially" absolute phase and has a precise mechanical meaning useful for primary balancing, alignment analysis, etc.

[0093] Embodiments of the methods and systems disclosed herein may include a peak detector for automatic scaling that is routed to a separate A / D. Many microprocessors currently in use have the functionality of a built-in A / D converter. For the purpose of vibration analysis, they are often insufficient in terms of the number of bits, channels, or sampling frequency relative to not significantly reducing the microprocessor speed. Despite these limitations, it is beneficial to use them for automatic scaling purposes. In an embodiment, a separate A / D with reduced functionality and cheaper can be used. For each channel input, the signal is fed directly into the microprocessor or low-cost A / D after buffering the signal (usually with a suitable coupling: AC or DC) but before signal conditioning it. Unlike the adjustment signal for range, gain, and filter switch position, the switch is not changed. This allows simultaneous sampling of automatic scaling data while signal conditioning the input data, feeding it to a more powerful external A / D, and directing it to onboard memory using a direct memory access (DMA) method in which the memory can be accessed without the CPU. This can significantly simplify the automatic scaling process without switching switch positions and allowing setup time, greatly slowing down the automatic scaling process. Furthermore, the data are collected simultaneously, which ensures an optimal signal-to-noise ratio. The reduced number of bits and other characteristics are usually more than sufficient for automatic scaling purposes.

[0094] Embodiments of the methods and systems disclosed herein may include routing a trigger channel (raw or cached) to other analog channels. Many systems may have trigger channels to determine the relative phase between multiple input data sets or to obtain important data without repeating undesired inputs. In embodiments, a digitally controlled relay is used to switch the raw or cached trigger signal to one of these input channels. Many times, it is very useful to check the quality of the trigger pulse because it is often corrupted for a variety of reasons, including insufficient placement of the trigger sensor, wiring problems, incorrect setup problems (such as dirty reflective tape when using a light sensor), etc. The ability to view the raw or cached signal provides an excellent diagnostic or debugging tool. By using the recorded data signal for a variety of signal processing techniques, such as variable speed filtering algorithms, it can also provide some improved phase analysis capabilities.

[0095] Embodiments of the methods and systems disclosed herein may include oversampling the higher input of the oversampled A / D for the lower sample rate output to minimize the anti-aliasing filter requirements. In an embodiment, a higher input oversampling rate is used for the oversampled A / D to obtain lower sample rate output data, thereby minimizing the AA filtering requirements. A lower oversampling rate can be used for a higher sample rate. For example, a third-order AA filter for a minimum sample rate requirement of 256 Hz (Fmax of 100 Hz) is sufficient for an Fmax range of 200 to 500 Hz. Another higher cutoff AA filter can be used for an Fmax range starting from 1 kHz and higher (where the auxiliary filter starts at the highest sample rate of 256x128 kHz). Embodiments of the methods and systems disclosed herein may include using a CPLD as a clock divider for the oversampled A / D to achieve a lower sample rate without digital resampling. In an embodiment, a CPLD can be used as a programmable clock divider to downsample a high frequency crystal reference to a lower frequency. The accuracy of the downsampled lower frequency is even more accurate than the original source relative to its longer time period. This also minimizes or eliminates the need for digital resampling processing.

[0096] Embodiments of the methods and systems disclosed herein may include signal processing firmware / hardware. In an embodiment, a high sampling rate is used to acquire long data blocks rather than extracting multiple data sets at different sampling rates. Typically, in modern path collection processes for vibration analysis, it is customary to collect data at a fixed sampling rate at a specified data length. Depending on the specific mechanical analysis requirements in place, the sampling rate and data length may vary from one path point to another. For example, a motor may require a relatively slow sampling rate and a higher resolution to distinguish operating speed harmonics from line frequency harmonics. However, the realistic trade-off is that more collection time is required to achieve this higher resolution. In contrast, some high-speed compressors or gear sets require much higher sampling rates to measure the amplitude of relatively high frequency data, although precise resolution may not necessarily be required. However, ideally, it would be better to collect data with very long sample lengths at very high sampling rates. When digital acquisition devices first became popular in the early 1980s, A / D sampling, digital storage, and computing power were not close to the levels they are today, so a compromise was made between the time required for data collection and the desired resolution and accuracy. Because of this limitation, some analysts in the field have even refused to abandon their analog tape recording systems, which have not been greatly affected by these digitization deficiencies. Even several hybrid systems have been used, which digitize the playback of recorded analog data at a variety of sampling rates and lengths as required, although these systems are undoubtedly less automated. As mentioned earlier, the more common approach is to balance data collection time with analysis capabilities and digitally acquire blocks of data at a variety of sampling rates and sampling lengths and store these blocks digitally separately. In embodiments, data with long data lengths can be collected and stored at the highest practical sampling rate, such as 1024kHz, corresponding to 40kHz Fmax. This long block of data can be acquired in the same time as the lower sampling rate of the shorter length used by the a priori method, so there is no effective delay added to the sampling at the measurement point, which is always a concern during the path collection process. In embodiments, the analog tape recording of the data is digitized with an accuracy that ensures that it can actually be regarded as continuous or "analog" accuracy suitable for many purposes, including for use in embodiments of the present invention, unless the context indicates otherwise.

[0097] Embodiments of the method and system disclosed herein may include storing calibration data and maintenance history on an onboard card set. Many data acquisition devices that rely on interfacing with a PC to work store their calibration coefficients on a PC. This is especially true for complex data acquisition devices that have many signal paths and therefore calibration tables that may be very large. In an embodiment, the calibration coefficients are stored in a flash memory that will permanently remember the data or any other important information about the matter for all practical purposes. This information may include nameplate information, such as serial numbers, firmware or software version numbers, maintenance history, and calibration tables of individual components. In an embodiment, no matter which computer the box is ultimately connected to, the DAQ box remains calibrated and continues to save all such important information. A PC or external device may poll this information at any time for implantation or information exchange purposes.

[0098] Embodiments of the methods and systems disclosed herein may include fast path creation using hierarchical templates. In the field of vibration monitoring and general parameter monitoring, data monitoring points need to be established in a database or functionally equivalent location. These points are associated with a variety of attributes, including the following categories: transducer attributes, data collection settings, mechanical parameters, and operating parameters. Transducer attributes will include probe type, probe mounting type, and probe mounting direction or axis direction. Data collection attributes associated with the measurement will include sampling rate, data length, IEPE (probe power) and coupling requirements, hardware integration requirements, 4-20 or voltage interface, range and gain settings (if applicable), filter requirements, etc. Mechanical parameter requirements relative to a specific point will include the following items: operating speed, bearing type, bearing parameter data corresponding to rolling bearings, these bearing parameter data include median diameter, number of balls, inner ring diameter and outer ring diameter; for tilting pad bearings, the parameter data will include the number of bearing pads, etc. For measurement points on equipment such as gearboxes, the required parameters will include, for example, the number of teeth on each gear; for induction motors, the number of rotor bars and posts; for compressors, the number of blades and / or vanes; for fans, the number of blades; for belt / pulley systems, the number of belts and the associated belt pass frequency calculated from the size of the pulleys and the pulley center distance; for measurements near couplers, the type of coupler and the number of teeth in the gear coupler may be essential, etc. Operating parameter data will include workload, which can be expressed in megawatts, flow (gas or liquid), percentage, horsepower, feet per minute, etc. Operating temperature (both ambient and operating), operating pressure, humidity, etc. may be relevant. As can be seen, the setup information required for a single measurement point can be very large. It is also critical to perform any reasonable analysis of this data. Machinery, equipment, and bearing specific information are essential to identify fault frequencies and predict specific faults of many types. Transducer properties as well as data collection parameters are essential to correctly interpret the data and provide constraints for the appropriate type of analysis technique. The traditional way of entering this data is to enter it manually, usually at the lowest hierarchical level, such as the bearing level for mechanical parameters or the transducer level for data collection setup information, and the operation is quite cumbersome. However, it cannot be overemphasized that the importance of the hierarchical relationships required to organize the data is used for analysis and interpretation, as well as the storage and movement of data. Here, we mainly emphasize the storage and movement of data. In essence, the setup information discussed above is extremely redundant at the lowest level. However, because it has a strong hierarchical nature, it can be efficiently stored in this form. In an embodiment, the hierarchical nature can be utilized when copying data in template form.For example, a hierarchical storage structure suitable for many purposes is defined from the universality to the specificity of a company, plant or site, unit or process, machine, equipment, shaft element, bearing and transducer. It is much easier to copy data associated with a specific machine, equipment, shaft element or bearing than to copy the data only at the lowest transducer level. In an embodiment, the system not only stores data in this hierarchical manner, but also uses hierarchical templates to robustly support the rapid replication of data. The similarity of components at a specific hierarchical level contributes to the efficient storage of data in a hierarchical format. For example, many machines have common components, such as motors, gearboxes, compressors, belts, fans, etc. More specifically, many motors can be easily classified as induction, DC, fixed speed or variable speed motors. Many gearboxes can be divided into common groupings, such as input / output, input pinion / intermediate gear / output pinion, 4 cylinders (poster), etc. In a factory or company, there are many similar types of equipment purchased and standardized for cost and maintenance reasons. This leads to a large number of similar types of equipment being repeated, and therefore provides a great opportunity to utilize a hierarchical template approach.

[0099] Embodiments of the methods and systems disclosed herein may include smart bands. Smart bands are applicable to any processed signal features derived from any dynamic input or input group in order to analyze data and achieve correct diagnosis. In addition, smart bands may even include miniature or relatively simple diagnostics for achieving more robust and complex smart bands. In the past, in the field of mechanical vibration analysis, alarm bands have been used to define relevant spectral bands for analyzing and / or trending significant vibration modes. Alarm bands are usually composed of a spectrum (amplitude plotted relative to frequency) zone defining a low frequency limit and a high frequency limit. The amplitudes between these limits are summed in the same way as for calculating the total amplitude. Smart bands are more flexible because they involve not only a specific frequency band, but also a group of spectral peaks, such as harmonics of a single peak, true peak level or crest factor obtained from a time waveform, overall waveform obtained from a vibration envelope spectrum, or other dedicated signal analysis techniques or logical combinations (AND, OR, XOR, etc.) of these signal attributes. In addition, countless classifications of other parameter data (including system load, motor voltage and phase information, bearing temperature, flow rate, etc.) can also be used as the basis for forming additional smart bands. In an embodiment, smart belt failure symptoms can be used as building blocks of an expert system whose engine uses these inputs to obtain diagnostic information. Then, some of these mini-diagnostics can in turn be used as smart belt failure symptoms (smart belts can even include diagnostics) for a wider range of diagnostics.

[0100] Embodiments of the methods and systems disclosed herein may include a neural network expert system using smart bands. Typical vibration analysis engines are rule-based, i.e., they use expert rule list engines that trigger specific diagnostics when conditions are met. In contrast, neural methods utilize weighted triggering of multiple input stimuli into smaller analysis engines or neurons, which in turn feed simplified weighted outputs to other neurons. The outputs of these neurons can also be classified as smart bands that in turn feed other neurons. This will produce a more hierarchical expert diagnostic approach compared to the one-shot approach of rule-based systems. In an embodiment, the expert system utilizes this neural approach using smart bands; however, the system does not exclude the reclassification of rule-based diagnostics as smart bands as other stimuli for the expert system to utilize. From this perspective, although it is essentially a neural approach at the highest level, it can be summarized as a hybrid approach.

[0101] Embodiments of the methods and systems disclosed herein may include using a database hierarchy in the analysis. Smart belt fault phenomena and diagnostics may be assigned to multiple hierarchical database levels. For example, a smart belt 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 to perform a smart belt diagnostic called "Horizontal Plane Phase Flip" on the coupling and generate a smart belt diagnostic of "Vertical Coupling Misalignment" at the machine level.

[0102] Embodiments of the methods and systems disclosed herein may include an expert system GUI. In an embodiment, the system uses a graphical approach to define smart bands and diagnostics for the expert system. The input of fault phenomena, rules, or broader smart bands for creating specific machine diagnostics can be tedious and time-consuming. One means to make the process more convenient and efficient is to provide a graphical approach using wiring. The proposed graphical interface consists of 4 main components: a fault phenomenon component library, a diagnostic library, a tool library, and a graphical wiring area ("GWA"); the fault phenomenon component library includes a variety of spectra, waveforms, envelopes, and any type of signal processing features or feature groupings (such as spectrum peaks, spectrum harmonics, waveform true peaks, waveform crest factors, spectrum alarm bands, etc.). Each component can be assigned other attributes. For example, the spectrum peak component can be assigned the frequency or order (multiple orders) of the operating speed. Some components can be predefined or user-defined, such as 1×, 2×, 3× operating speed, 1×, 2×, 3× gear mesh, 1×, 2×, 3× blade passing, the number of motor rotor bars × operating speed, etc.

[0103] In an embodiment, a diagnostic library contains various predefined and user-defined diagnostics, such as misalignment, imbalance, looseness, bearing failure, etc. Similar to components, diagnostics can also be used as parts for building more complex diagnostics. In an embodiment, the tool library contains logical operations such as AND, OR, XOR, or other ways of combining the various parts listed above, such as maximum, minimum, interpolation, average, other statistical operations, etc. Various components, tools and diagnostics will be represented by icons, which are simply connected together graphically in the desired manner. Embodiments of the methods and systems disclosed herein may include expert system GUI graphical methods for defining expert system smart bands and diagnostics. The input of fault symptoms, rules or broader smart bands for creating specific machine diagnostics may be tedious and time-consuming. A means to make this process more convenient and efficient is to provide a graphical method using wiring. The proposed graphical interface consists of four main components: a fault symptom parts bin, a diagnostics bin, a tools bin, and a graphical wiring area ("GWA"). In an embodiment, the fault phenomenon component library contains various spectra, waveforms, envelopes, and any type of signal processing features or feature groups, such as spectrum peaks, spectrum harmonics, waveform true peaks, waveform crest factors, spectrum alarm bands, etc. Each part can be assigned other attributes, for example, the spectrum peak part can be assigned the frequency or order (multiple orders) of the operating speed. Some components can be predefined or user-defined, such as 1×, 2×, 3× operating speed, 1×, 2×, 3× gear meshing, 1×, 2×, 3× blade passing, the number of motor rotor bars×operating speed, etc. In an embodiment, the diagnostic library contains various predefined and user-defined diagnoses, such as misalignment, imbalance, looseness, bearing failure, etc. Similar to components, diagnosis can also be used as a part for building more complex diagnosis. The tool library includes logical operations such as AND, OR, XOR, or other ways of combining the various parts listed above, such as maximum, minimum, interpolation, average, other statistical operations, etc. GWA can generally include components in the component library or diagnoses in the diagnostic library, which are connected together using tools to create a diagnosis. The various components, tools, and diagnostics are represented by icons that are simply connected together graphically in the required manner.

[0104] Embodiments of the methods and systems disclosed herein may include graphical methods for back-calculating definitions. In an embodiment, the expert system also provides learning opportunities to the system. If a unique set of stimuli or smart bands are known to correspond to a specific fault or diagnosis, then a set of coefficients can be back-calculated, and when these coefficients are applied to a future set of similar stimuli, the same diagnosis will be obtained. In an embodiment, if there are multiple sets of data, then a best fit method can be used. Unlike the smart band GUI, this embodiment will generate a wiring diagram by itself. In an embodiment, the user can customize the back propagation method settings and use a database browser to match a specific data set and the desired diagnosis. In an embodiment, the smart band GUI can be used to create or customize the desired diagnosis. In an embodiment, thereafter, the user can press the GENERATE button, and the dynamic wiring of the fault phenomenon to the diagnosis may appear on the screen when the algorithm that achieves the best fit is completed. In an embodiment, various statistics will be displayed after the above operations are completed, detailing the extent to which the mapping process has progressed. In some cases, mapping may not be achieved, for example if the input data is all zero or erroneous data (wrong allocation), etc. Embodiments of the methods and systems disclosed herein may include a bearing analysis method. In an embodiment, the bearing analysis method may combine computer-aided design ("CAD"), predictive deconvolution, minimum variance distortionless response ("MVDR"), and spectral harmonic summation.

[0105] Embodiments of the methods and systems disclosed herein may include torsional vibration detection and analysis using transient signal analysis, which has become a significant trend with the recent popularity of variable speed machinery. Due primarily to the reduction in the cost of motor speed control systems and the increase in the cost and awareness of energy usage, the potential huge energy savings of utilizing load control have become more economically justified. Unfortunately, one design aspect of this problem that is often overlooked is vibration. If the machine is designed to run at only one speed, it is correspondingly much simpler to design the physical structure to avoid structural and torsional mechanical resonances, each of which can significantly reduce the mechanical health of the machine. This will include structural features such as the type of materials used, material weight, reinforcement requirements and placement, bearing type, bearing location, base bracket constraints, etc. Even if the machine runs at one speed, designing a structure to minimize vibration can still be a difficult task and may require computer modeling, finite element analysis, and field testing. By adding variable speeds, in many cases, it is not possible to design for all required speeds. The problem then becomes a minimization problem, such as through speed avoidance. This is why many modern motor controllers are typically programmed to skip or quickly pass through specific speed ranges or speed bands. Embodiments include determining speed ranges in vibration monitoring systems. Non-torsional structural resonances are usually fairly easy to detect using conventional vibration analysis techniques. However, this is not the case for torsions. One particular area of ​​current concern is the increased incidence of torsional resonance problems, which is apparently due to the increased torsional stress of speed changes and the operation of equipment at torsional resonant speeds. Unlike non-torsional structural resonances, which usually exhibit a greatly increased housing or external vibration effect, torsional resonances usually do not have such effects. In the case of shaft torsional resonances, the torsional motion caused by the resonance can only be discerned by looking for speed and / or phase changes. The current standard method for analyzing torsional vibrations involves the use of specialized instruments. The methods and systems disclosed herein allow torsional vibrations to be analyzed without the use of such specialized instruments. This method can consist of shutting down the machine and using strain gauges and / or speed encoder plates and / or other special fixtures such as gears. Friction wheels are another alternative, but they usually require manual implementation and require professional analysts. In general, these techniques can be too expensive and / or cumbersome. Continuous vibration monitoring systems are becoming more and more popular due to reduced costs and increased convenience (e.g., remote access). In an embodiment, torsional speed and / or phase changes can be discerned using only the vibration signal.In an embodiment, transient analysis techniques may be used to distinguish torsional induced vibrations from speed changes due to process control.In embodiments, factors used for discrimination may focus on one or more of the following: the rate of speed change caused by variable speed motor control is relatively slow, continuous, and stable; torsional speed changes tend to be brief, pulsed, and not continuous; torsional speed changes tend to be volatile, likely to decay exponentially, while process speed changes do not; and small speed changes associated with torsional relative to the rotational speed of the shaft indicate that monitoring phase behavior will exhibit fast or brief speed bursts, in contrast to the slow phase changes historically associated with increasing or decreasing machine speed (represented by Bode or Nyquist plots).

[0106] Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When integrating signals digitally using software, the amplitude of the frequency data at the low end of the spectrum is essentially multiplied by a function that quickly becomes infinite as it approaches zero and produces 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. A simple remedy for this is a traditional hardware integrator that is capable of performing at a much greater signal-to-noise ratio than the signal that has been digitized. In addition, the amplification factor can be limited to a reasonable level, thereby essentially prohibiting multiplication with very large numbers. However, at higher frequencies where the frequency becomes larger, it is possible to multiply the initial amplitude that is far above the noise floor by a very small number (1 / f) that reduces the amplitude to a level far below the noise floor. Hardware integrators have a fixed noise floor, although the lower floor limit does not decrease with the current lower amplitude high-frequency data. In contrast, the same digital multiplication of the digitized high-frequency signal still reduces the noise floor proportionally. In an embodiment, hardware integration may be used below the unity gain point (at a value typically determined based on gain being unity and / or a desired signal-to-noise ratio), and software integration may be used above the unity gain value to produce the desired result. In an embodiment, the integration is performed in the frequency domain. In an embodiment, the resulting mixed data may then be converted back into a waveform that should be far superior to the hardware integrated or software integrated data in terms of signal-to-noise ratio. In an embodiment, the advantages of hardware integration are used in conjunction with the advantages of digital software integration to achieve the maximum signal-to-noise ratio. In an embodiment, a first order progressive hardware integrator high pass filter with curve fitting allows some relatively low frequency data to pass while reducing or eliminating noise, thereby allowing very useful analytical data that would otherwise be eliminated by an improper filter to be recovered.

[0107] Embodiments of the methods and systems disclosed herein may include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is usually performed using a front-end Mux, the purpose of which is to select a few channels among many data channels to feed the hardware signal processing, A / D and processing components of the DAQ system. This is mainly due to practical cost considerations. The compromise is not to monitor all points continuously (but to monitor in a smaller range through alternative hardware methods). In an embodiment, multiple scheduling levels are provided. In an embodiment, at the lowest level of most continuous, all measurement points will be cycled in a round-robin manner. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 measurement points, each point will be serviced once every 15 minutes. However, if a point should be alarmed according to a standard selected by the user, its priority can be increased so that it can be serviced more frequently. Since there may be multiple levels of severity for each alarm, there may be multiple levels of priority in monitoring. In an embodiment, more severe alarms will be monitored more frequently. In an embodiment, a variety of additional advanced signal processing techniques can be applied at less frequent intervals. Embodiments may take advantage of the enhanced processing power of the PC, and the PC is able to temporarily pause the round-robin collection (with multiple collection layers) process and stream the required amount of data for its selected point. Embodiments may incorporate various advanced processing techniques such as envelope processing, wavelet analysis, and many other signal processing techniques. In embodiments, after collecting this data, the DAQ card group will continue its route at the point where it was interrupted. In embodiments, the various PC scheduled data collections will follow their own schedules, which will be less frequent than the DAQ card routes. They can be set by hour, day, number of route cycles (e.g., once every 10 cycles), and also add scheduling based on their alarm severity priority or measurement type (e.g., motors can be monitored differently than fans).

[0108] Embodiments of the methods and systems disclosed herein may include a data collection resident feature. In embodiments, a data collection box used for route collection, real-time analysis, and generally as a collection tool may be separated from its PC (tablet or other device) and powered by an external power source or a suitable battery. In embodiments, the data collector still retains continuous monitoring capabilities, and its onboard firmware may implement dedicated monitoring functions for a long time or may be remotely controlled for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical capabilities for continuous monitoring.

[0109] Embodiments of the methods and systems disclosed herein may include environmental sensing, local sensing, and vibration for analysis. In embodiments, ambient temperature and pressure, sensor temperature and pressure may be combined with long-term / medium-term vibration analysis to predict any condition or feature range. Variations may add infrared sensing, infrared thermography, ultrasound, and many other sensor types and input types, combined with vibration or combined with each other. Embodiments of the methods and systems disclosed herein may include intelligent routing. In embodiments, the software of the continuous monitoring system will adapt / adjust the data collection sequence based on statistics, analysis, data alerts, and dynamic analysis. Typically, the routing is set based on the channels to which the sensors are connected. In embodiments, the Mux is able to combine any input Mux channel to (e.g., eight) output channels through crosspoint switches. In embodiments, as the channel enters an alarm state or the system identifies a critical deviation, the Mux will suspend the normal routing set in the software to collect specific synchronized data from channels that share critical statistical changes for more advanced analysis. Embodiments include performing intelligent ODS or intelligent transfer functions.

[0110] Embodiments of the methods and systems disclosed herein may include smart ODS and transfer functions. In embodiments, due to the multiplexers and crosspoint switches of the system, ODS, transfer functions or other special tests can be performed on all vibration sensors connected to the machine / structure, showing how the various points of the machine are connected to each other. In embodiments, data of 40-50kHz and longer lengths (e.g., at least one minute) can be streamed, which can reveal information different from what a normal ODS or transfer function would show. In embodiments, the system will be able to determine smart line features based on the data / statistics / analysis to be used, which deviate from the standard route and perform ODS on a machine, structure, or multiple machines and structures that may show correlation due to the conditions / data that guide them. In embodiments, for transfer functions, an impact hammer can be used on one channel to compare with other vibration sensors on the machine. In embodiments, the system can perform transfer functions using condition changes such as load, speed, temperature or other changes of the machine or system. In embodiments, different transfer functions can be compared with each other over time. In an embodiment, different transfer functions can be strung together like a movie that can show how a mechanical failure changes, for example, a bearing can show how it goes through four stages of bearing failure, etc. Embodiments of the methods and systems disclosed herein may include a layered Mux. In an embodiment, the layered Mux can allow more (16, 24 or more) channels to be modularly output to multiples of 8 channel card groups, which allows multiple data channels to be collected simultaneously for more complex analysis and faster data collection. Disclosed herein are methods and systems for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of energy production facilities.

[0111] See also Figure 8 , the present invention generally includes collecting or streaming waveform data 2010 digitally from a machine 2020, the operating speed of which can vary from a relatively slow rotational or oscillating speed to a relatively fast speed in different scenarios. The waveform data 2010 on at least one machine can include data from a single-axis sensor 2030 mounted at a constant reference position 2040 and from a three-axis sensor 2050 mounted at a varying position (or at multiple positions) including a position 2052. In an embodiment, the waveform data 2010 can be vibration data obtained simultaneously from each sensor 2030, 2050 in a gap-free format over a duration of several minutes, with a maximum resolvable frequency sufficient to capture periodic and transient impact events. By way of example, the waveform data 2010 can include vibration data that can be used to generate operating deformation modes. If necessary, the data can also be used to diagnose vibrations, and machine maintenance plans can be prescribed based on these vibrations.

[0112] In an embodiment, the machine 2020 may further include a housing 2100, which may include a drive motor 2110 capable of driving a shaft 2120. The shaft 2120 may be supported for rotation or oscillation by a set of bearings 2130 (e.g., including a first bearing 2140 and a second bearing 2150). A data collection module 2160 may be connected to the machine 2020 (or reside on the machine 2020). In one example, the data collection module 2160 may be located and accessed through a cloud network facility 2170, may collect waveform data 2010 from the machine 2020, and transmit the waveform data 2010 to a remote location. The working end 2180 of the machine 2020 drive shaft 2120 may drive a windmill, a fan, a pump, a drill, a gear system, a drive system, or other working element, as the technology described herein can be applied to a wide range of machines, equipment, tools, etc. that include rotating or oscillating elements. In other cases, a generator may replace the motor 2110, and the working end of the drive shaft 2120 may direct the rotational energy to the generator to produce electricity, rather than consuming electricity.

[0113] In an embodiment, the waveform data 2010 can be obtained using a predetermined route format based on the layout of the machine 2020. The waveform data 2010 can include data from a single-axis sensor 2030 and a three-axis sensor 2050. The single-axis sensor 2030 can be used as a reference probe with one data channel and fixed at a constant position 2040 on the surveyed machine. The three-axis sensor 2050 can be used as a three-axis probe with three data channels (e.g., three orthogonal axes) and can be moved from one test point to the next test point according to a predetermined diagnostic route format. In one example, the sensors 2030, 2050 can be manually installed to the machine 2020 and can be connected to a separate portable computer in certain service instances. The reference probe can remain in one position, while the user can move the three-axis vibration probe along a predetermined route on the machine (e.g., bearing to bearing). In this example, the user is guided to position the sensor at a predetermined position to complete the survey of the machine (or part thereof).

[0114] See also Fig. 9 , shows a portion of an exemplary machine 2200 according to the present invention having a three-axis sensor 2210 mounted to a location 2220 associated with a motor bearing of the machine 2200 having an output shaft 2230 and an output member 2240. See Fig. 9 and Fig.10, shows an exemplary machine 2300 according to the present invention having a triaxial sensor 2310 and a uniaxial vibration sensor 2320, which is used as a reference sensor connected to a constant position on the machine 2300 during a vibration survey. The triaxial sensor 2310 and the uniaxial vibration sensor 2320 can be connected to a data collection system 2330.

[0115] In other examples, the sensors and data acquisition modules and devices may be integrated into or reside on the rotating machine. With these examples, the machine may include multiple single-axis sensors and multiple three-axis sensors at predetermined locations. The sensors may initially be 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 may select and use the single-axis sensor and acquire data only from the single-axis sensor during the collection of waveform data 2010 while moving to each three-axis sensor. The data collection module 2160 may reside on the machine 2020 and / or be connected via a cloud network facility 2170.

[0116] See also Figure 8 , various embodiments include collecting waveform data 2010 by digitally recording locally or streaming over a cloud network facility 2170. The waveform data 2010 can be collected continuously to ensure no gaps, and in some aspects, can be similar to analog recorded waveform data. The waveform data 2010 from all channels can be collected in one to two minutes depending on the rotational or oscillating speed of the monitored machine. In embodiments, the data sampling rate can be a relatively high sampling rate relative to the operating frequency of the machine 2020.

[0117] In an embodiment, a second reference sensor may be used and data for a fifth data channel may be collected. Thus, a single axis sensor may be the first channel and three axis vibration may occupy the second, third and fourth data channels. The second reference sensor, like the first sensor, may be a single axis sensor, such as an accelerometer. In an embodiment, the second reference sensor, like the first reference sensor, may remain at the same location on the machine for the entire vibration survey. The location of the first reference sensor (i.e., the single axis sensor) may be different from the location of the second reference sensor (i.e., the other single axis sensor). In some instances, a second reference sensor may be used when the machine has two axes that run at different speeds, where the two reference sensors are located on two different axes. According to this example, other single axis reference sensors may be used at other constant locations associated with the rotating machine.

[0118] In an embodiment, the waveform data may be transmitted electronically in a gapless format at a relatively high rate over a relatively long period of time. In one example, the period of time is 60 seconds to 120 seconds. In another example, the sampling rate is 100 kHz and the maximum resolvable frequency (Fmax) is 40 kHz. According to the present invention, it should be appreciated that the waveform data may be presented so as to more closely resemble some of the data that may be obtained from previous examples of analog recording of waveform data.

[0119] In an example, sampling, frequency band selection and filtering techniques can allow one or more portions of a long data stream (i.e., one to two minutes in duration) to be undersampled or oversampled to achieve a varying effective sampling rate. To this end, interpolation and decimation can be used to further achieve a varying effective sampling rate. For example, oversampling can be applied to a frequency band close to the rotational or oscillating operating speed of the sampled machine or to its harmonics, because the vibration effects at these frequencies may often be more obvious within the operating range of the machine. In an embodiment, the digital sampled data set can be decimated to produce a lower sampling rate. According to the present invention, it should be understood that decimation herein can be opposite to interpolation. In an embodiment, decimating the data set can include first applying a low pass filter to the digital sampled data set and then undersampling the data set.

[0120] In one example, a 100 Hz sample waveform may be undersampled at every tenth of the digital waveform to produce an effective sample rate of 10 Hz, but the remaining nine tenths of the waveform may be effectively discarded and not included in the modeling of the sample waveform. Furthermore, this type of unmodified undersampling may produce spurious frequencies due to the undersampling rate associated with the 100 Hz sample waveform (i.e., 10 Hz).

[0121] Most hardware used for analog-to-digital conversion uses a sample-and-hold circuit that charges a capacitor for a given time in order to determine the average value of a waveform relative to a particular time variation. According to the present invention, it should be understood that the waveform value relative to a particular time variation is not a linear value, but rather more similar to a cardinal sine ("sinc") function; therefore, it can be shown that more emphasis may be placed on waveform data at the center of the sampling interval, and the cardinal sine signal decays exponentially from the center of the sampling interval.

[0122] With the above example, a 100 Hz sample waveform can be hardware sampled at 10 Hz, so each sample point is averaged over 100 milliseconds (e.g., each point of a 100 Hz sampled signal can be averaged over 10 milliseconds). In contrast to effectively discarding nine-tenths of the data points of the sampled waveform as discussed above, the present invention can include weighted adjacent data. Adjacent data can refer to previously discarded sample points and one remaining point retained. In one example, a low-pass filter can be used to average adjacent sample data in a linear manner, i.e., determine the sum of every ten points and then divide the sum by ten. In another example, a sinc function can be used to weight adjacent data. The process of using a sinc function to weight the initial waveform can be called an impulse function, or in the time domain, it can be called a convolution.

[0123] The present invention is applicable not only to digitizing waveform signals based on detected voltages, but also to digitizing waveform signals based on current waveforms, vibration waveforms, and image processing signals including rasterization of video signals. In one example, window resizing on a computer screen may be decimated, although this may need to be done in at least two directions. In these further examples, it will be appreciated that self-undersampling may be demonstrated to be insufficient. To this end, self-oversampling or upsampling may be demonstrated to be insufficient in a similar manner so that interpolation may be used as with decimation, rather than simply self-undersampling.

[0124] According to the present invention, it should be understood that interpolation herein may refer to first applying a low-pass filter to digital sampled waveform data and then up-sampling the waveform data. According to the present invention, it should be understood that examples in reality may usually require the use of non-integer factors for extraction or interpolation or both. For this reason, the present invention includes sequential interpolation and extraction to achieve non-integer factor ratios for interpolation and extraction. In one example, sequential interpolation and extraction can be defined as applying a low-pass filter to a sample waveform, then interpolating the waveform after low-pass filtering, and then extracting the waveform after interpolation. In an embodiment, vibration data can be looped to purposefully simulate the loop of a conventional tape recorder, combined with effective splicing to use digital filtering techniques for longer analysis. According to the present invention, it should be understood that the above technology does not exclude the use of a user's GUI processing and display of waveforms, spectra and other types of analysis when collected. According to the present invention, it should be understood that the new system can allow the function to be performed in parallel with a high-performance collection of raw waveform data.

[0125] As for the time to collect issue, it should be appreciated that the time savings from using the compromise approach of improving data resolution by collecting at different sampling rates and data lengths is not actually as good as expected. For this reason, latency issues may arise each time the data acquisition hardware is stopped and started, especially when hardware autoscaling is performed. The same may be true for data retrieval of line information (i.e., test locations), which is typically in a database format and is performed at a very slow rate. Storing the raw data to disk (whether solid state or otherwise) out of the blue may also be quite slow.

[0126] In contrast, many embodiments include digital streaming waveform data 2010 as disclosed herein, while also enjoying the benefit of loading route parameter information while only having to set up data acquisition hardware once. Since the waveform data 2010 is only streamed to a file, there is no need to open and close the file or use a storage medium to switch between loading and writing operations. It can be shown that the collection and storage of waveform data 2010 described herein can generate relatively more meaningful data in a significantly shorter time than traditional batch data acquisition methods. In view of the above, an example includes a motor, and in order to achieve a sufficiently high resolution, a data length of 4K points (i.e., 4096) can be used to collect the relevant waveform data of the motor, so as to (especially) distinguish the electrical sideband frequency. For fans or blowers, a reduced resolution of 1K (i.e., 1024) can be used. In some cases, 1K can be used as the minimum waveform data length requirement. The sampling rate can be 1280Hz, and is equivalent to an Fmax of 500Hz. According to the present invention, it should be understood that the industry standard oversampling factor of 256 can meet the two times (2×) oversampling required by the Nyquist Criterion, and has some additional margin to accommodate anti-aliasing filter rolloff. The time to collect these waveform data is 1024 points at 1280 Hz, or 800 milliseconds.

[0127] To improve accuracy, the waveform data may be averaged. Eight averages and may be used with a fifty percent overlap (for example). This will extend the time from 800 milliseconds to 36 seconds, equivalent to 800msec × 8 averages × 0.5 (overlap rate) + 0.5 × 800msec (no overlap of the head and tail). After collecting the waveform data at Fmax = 500Hz, a higher sampling rate may be used. In one example, ten times (10×) the previous sampling rate may be used, and Fmax = 10kHz. With this example, eight averages may be used with a fifty percent (50%) overlap to collect waveform data at a higher rate (which may be equivalent to a collection time of 360msec or 0.36 seconds). According to the present invention, it should be understood that it may be necessary to read the hardware collection parameters for the higher sampling rate from the route list, and allow the hardware to automatically scale or reset other necessary hardware collection parameters, or both. To this end, a delay of several seconds may be added to accommodate changes in the sampling rate. In other cases, introducing delays can accommodate automatic scaling of hardware and changes in hardware collection parameters that may be required when using the lower sampling rates disclosed herein. In addition to accommodating changes in sampling rates, additional time is required to read route point information from the database (i.e., the monitored location and the next location to be monitored), display the route information, and process the waveform data. In addition, the display of waveform data and / or associated spectrum may also consume a lot of time. Based on the above, 15 to 20 seconds may have passed while waveform data is obtained at each measurement point.

[0128] In other examples, additional sampling rates can be added, but this may make the total amount of time for the vibration survey longer because the time is added up to the conversion time from one sampling rate to another and the time to obtain additional data at different sampling rates. In one example, a lower sampling rate is used, such as a sampling rate of 128Hz, where Fmax=50Hz. With this example, in addition to the other conditions mentioned above, at this sampling rate, the vibration survey will require an additional 36 seconds for the first set of averaged data, and therefore the total time spent at each measurement point will increase more significantly. Other examples include similar digital streaming of gapless waveform data disclosed herein for wind turbines and other machines that may have relatively low-speed rotating or oscillating systems. In many examples, the collected waveform data may include long data samples at relatively high sampling rates. In one example, the sampling rate may be 100kHz and the sampling duration may be two minutes on all recorded channels. In many examples, one channel may be used for a single-axis reference sensor, and the other three data channels may be used for a three-channel sensor for three axes. According to the present invention, it will be appreciated that longer data lengths may be displayed to facilitate detection of very low frequency phenomena. In addition, longer data lengths may be displayed to accommodate inherent speed variations in wind turbine operation. Longer data lengths may also be displayed to provide the opportunity to use numerous averages as discussed herein, thereby achieving very high spectral resolution and enabling tape looping of certain spectral analyses. Today, many advanced analysis techniques are available for use because these techniques may use available, longer, uninterrupted lengths of waveform data according to the present invention.

[0129] According to the present invention, it should also be appreciated that collecting waveform data from multiple channels simultaneously can help to perform transfer functions between multiple channels. In addition, collecting waveform data from multiple channels simultaneously can help to establish phase relationships on the machine, so that more complex correlations can be utilized by relying on the fact that waveforms are collected from each channel simultaneously. In other examples, more channels can be used in data collection to reduce the time it takes to complete an entire vibration survey by allowing waveform data to be collected from multiple sensors simultaneously (otherwise it would have to be collected in a continuous manner moving from sensor to sensor in the vibration survey).

[0130] The present invention includes using at least one single-axis reference probe on one channel to allow for the acquisition of relative phase comparisons between channels. The reference probe may be an accelerometer or other type of transducer that does not move during a vibration survey of a machine and is therefore fixed to a constant position. Multiple reference probes may be deployed at appropriate locations (i.e., constant positions) that are fixed in place throughout the vibration data acquisition of the vibration survey. In some instances, up to seven reference probes may be deployed, depending on the capacity of the data collection module 2160, etc. Using transfer functions or similar techniques, the relative phases of all channels may be compared to each other at all selected frequencies. By keeping one or more reference probes in their constant positions while moving or monitoring other three-axis vibration sensors, it can be shown that the entire machine can be mapped in terms of amplitude and relative phase. This can be proven to be true even if there are more measurement points than data collection channels. With this information, working deformation modes can be generated to display the dynamic motion of the machine in 3D, which can provide a very valuable diagnostic tool. In an embodiment, one or more reference probes may provide relative phase instead of absolute phase. According to the present invention, it should be understood that relative phase may not be as valuable as absolute phase for some purposes, but it can still be shown that relative phase information is very useful.

[0131] In an embodiment, the sampling rate used during the vibration survey can be digitally synchronized to a predetermined operating frequency, which may be related to a relevant parameter of the machine (such as the rotation or oscillation speed). In this way, more information can be extracted using synchronous averaging techniques. According to the present invention, it should be understood that this can be achieved without using key phasors or reference pulses from rotating shafts that are generally unavailable for route collection data. In this way, non-synchronous signals can be removed from complex signals without using key phasors to deploy synchronous averaging. This can be shown to be very effective when analyzing a specific pinion in a gearbox or any component that is generally applied to a complex mechanical mechanism. In many cases, key phasors or reference pulses are rarely available for route collection data, but the method disclosed herein can overcome this lack. In an embodiment, there may be multiple shafts running at different speeds in the machine being analyzed. In some cases, each shaft may have a single-axis reference probe. In other cases, only a single-axis reference probe at a constant position on one shaft can be used to associate the phase of one shaft with another shaft. In an embodiment, variable speed equipment may be easier to analyze using relatively long data durations relative to single-speed equipment. Vibration surveys can be performed at several machine speeds in the same continuous vibration data set using the same techniques disclosed herein. These techniques can also allow the study of changes in the relationship between vibration and the rate of change of speed, which was not possible before.

[0132] In an embodiment, since raw waveform data can be captured in a gapless digital format disclosed herein, numerous analysis techniques emerge. The gapless digital format can facilitate multiple approaches to analyzing waveform data in a variety of ways after identifying a specific problem. Vibration data collected according to the techniques disclosed herein can provide analysis of transient, semi-periodic, and very low frequency phenomena. Waveform data collected according to the present invention can contain a relatively long stream of raw gapless waveform data, which can be conveniently played back as needed and can be subjected to a variety of different complex analysis techniques. A large number of such techniques can provide various forms of filtering to extract low amplitude modulation from transient impulse data, which can be contained in a relatively long stream of raw gapless waveform data. According to the present invention, it should be understood that in past data collection practices, these types of phenomena were usually discarded during the averaging process of the spectrum processing algorithm because the previous data acquisition module was only intended for periodic signals; or because most of the content in the original raw signal was usually discarded when it was known that it would not be used, so these phenomena were discarded to archive the size reduction method.

[0133] In an embodiment, there is a method for 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 a constant position 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: simultaneously recording gapless digital waveform data from all 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 a three-axis sensor located at multiple positions associated with the machine when the digital waveform is obtained. In an embodiment, the second, third, and fourth channels are assigned together to a series of three-axis sensors located at different positions associated with the machine. In an embodiment, data is received from all sensors on all of its channels simultaneously.

[0134] The method also includes determining the working deformation mode based on the change of relative phase information and waveform data. In an embodiment, the constant position of the reference sensor refers to the position associated with the shaft of the machine. In an embodiment, the three-axis sensors in a series of three-axis sensors are respectively located at different positions and are respectively associated with different bearings in the machine. In an embodiment, the constant position refers to the position associated with the shaft of the machine, and wherein the three-axis sensors in a series of three-axis sensors are respectively located at different positions and are respectively associated with different bearings supporting the shaft in the machine. Various embodiments include methods for sequentially monitoring vibration or similar process parameters and signals from multiple channels (which can be referred to as a collection) of rotating or oscillating machines or similar processing machinery. In various instances, a collection can include one to eight channels. In other instances, a collection can represent a logical measurement grouping on the monitored device, whether these measurement locations are temporary for measurement, provided by the original equipment manufacturer, later modified, or one or more combinations of the above.

[0135] In one example, the set can monitor bearing vibration in a single direction. In another example, the set can monitor three different directions (e.g., orthogonal directions) using a triaxial sensor. In other examples, the set can monitor four or more channels, wherein the first channel can monitor a single-axis vibration sensor, and the second, third, and fourth channels can monitor three directions of the triaxial sensor, respectively. In other examples, the set can be fixed to a group of adjacent bearings on the same device or related shaft. Various embodiments provide a variety of methods, including strategies for collecting waveform data from various sets deployed in vibration research, etc. in a relatively more efficient manner. These methods also include simultaneously monitoring a reference channel, which is assigned to a constant reference position associated with the set of monitoring machines. Collaboration with the reference channel can be shown to support a more complete correlation between the collected waveform and the set. 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 position on a rotating shaft, etc. As disclosed herein, the above method can further include simultaneously recording gapless digital waveform data from all channels in each set at a relatively high sampling rate, so as to include all frequencies that are considered necessary for proper analysis of the monitored machinery when it is running. The data in the collection can be streamed to the storage medium for subsequent processing in a gapless manner. The storage medium can be connected to cloud network facilities, local data links, Bluetooth TM connection, cellular data connection, etc.

[0136] In an embodiment, the method disclosed herein includes a strategy for collecting data from various sets, including digital signal processing techniques that can be applied to the data from the set to highlight or better separate specific frequencies or waveform phenomena. This may be in contrast to the current method of collecting multiple sets of data at different sampling rates or with different hardware filtering configurations including integration, which (referred to as a priori hardware configurations) provide relatively low post-processing flexibility due to their constraints. In addition, it can also be shown that these hardware configurations increase the time of vibration surveys due to delays associated with configuring hardware for each independent test. In an embodiment, the method including a strategy for collecting data from various sets includes a data marking technique for classifying a portion of the streaming data as homogeneous data and belonging to a specific set. In one example, a category can be defined as a running speed. In this way, numerous sets can be generated from a set that is collected only as a set by a conventional system. Many embodiments include post-processing analysis techniques for comparing the relative phases of all relevant frequencies between each channel of the collected set and between all channels of all monitored sets when appropriate.

[0137] refer to Fig.12 , many embodiments include a first machine 2400 having a rotating or oscillating component 2410 or both, each component being supported by a set of bearings 2420 including a bearing set 2422, a bearing set 2424, a bearing set 2426, and more bearing sets (as needed). The first machine 2400 can be monitored by a first sensor set 2450. The first set 2450 can be configured to receive signals from sensors initially installed (or added later) on the first machine 2400. The sensors on the machine 2400 can include single-axis sensors 2460, such as single-axis sensors 2462, single-axis sensors 2464, and more single-axis sensors (as needed). In many examples, the single-axis sensors 2460 can be positioned in the machine 2400 to allow sensing of the position of one rotating or oscillating component 2410 of the machine 2400.

[0138] The machine 2400 may also have a three-axis (e.g., orthogonal axis) sensor 2480, such as three-axis sensor 2482, three-axis sensor 2484, and more three-axis sensors (as needed). In many examples, the three-axis sensor 2480 may be positioned in the machine 2400 to allow sensing of the position of each bearing group in the plurality of bearing groups 2420, which are associated with the rotating or oscillating components of the machine 2400. The machine 2400 may also have a temperature sensor 2500, such as temperature sensor 2502, temperature sensor 2504, and more temperature sensors (as needed). The machine 2400 may also have a tachometer sensor 2510 or more tachometer sensors (as needed) that respectively detail the RPM of one of its rotating components. With the above example, the first sensor set 2450 may survey the above sensors associated with the first machine 2400. To this end, the first set 2450 may be configured to receive data from eight channels. In other examples, the first sensor set 2450 may be configured to include more than eight channels or less than eight channels as needed. In this example, the eight channels include two channels that can monitor single-axis reference sensor signals and three channels that can monitor triaxial sensor signals. The remaining three channels can monitor two temperature signals and one signal from a tachometer. In one example, according to the present invention, the first set 2450 can monitor single-axis sensor 2462, single-axis sensor 2464, triaxial sensor 2482, temperature sensor 2502, temperature sensor 2504, and tachometer sensor 2510. During a vibration survey of machine 2400, the first set 2450 can monitor triaxial sensor 2482 and then triaxial sensor 2484.

[0139] According to the present invention, after monitoring the triaxial sensor 2484, the first set 2450 may monitor other triaxial sensors on the machine 2400 as needed, which are part of a predetermined route list associated with a vibration survey of the machine 2400. During the vibration survey, the first set 2450 may continue to monitor the single-axis sensor 2462, the single-axis sensor 2464, the two temperature sensors 2502, 2504, and the tachometer sensor 2510, while the first set 2450 may continue to monitor the plurality of triaxial sensors 2480 as per the predetermined route plan for the vibration survey.

[0140] refer to Fig.12, many embodiments include a second machine 2600 having a rotating or oscillating component 2610 or both, each component being supported by a set of bearings 2620 including a bearing set 2622, a bearing set 2624, a bearing set 2626, and more bearing sets (as needed). The second machine 2600 can be monitored by a second sensor set 2650. The second set 2650 can be configured to receive signals from sensors initially installed (or added later) on the second machine 2600. The sensors on the machine 2600 can include single-axis sensors 2660, such as single-axis sensors 2662, single-axis sensors 2664, and more single-axis sensors (as needed). In many examples, the single-axis sensors 2660 can be positioned in the machine 2600 to allow sensing of the position of one rotating or oscillating component 2600 of the machine 2610.

[0141] The machine 2600 may also have a three-axis (e.g., orthogonal axis) sensor 2680, such as three-axis sensor 2682, three-axis sensor 2684, three-axis sensor 2686, three-axis sensor 2688, and more three-axis sensors (as needed). In many examples, the three-axis sensor 2680 may be positioned at a location of the machine 2600 that allows sensing of each of the plurality of sets of bearings 2620 associated with a rotating or oscillating component of the machine 2600. The machine 2600 may also have a temperature sensor 2700, such as temperature sensor 2702, temperature sensor 2704, and more temperature sensors (as needed). The machine 2600 may also have a tachometer sensor 2710 or more tachometer sensors (as needed) that each detail the revolutions per minute of one of its rotating components.

[0142] With the above example, the second sensor set 2650 can survey the above sensors associated with the second machine 2600. To this end, the second set 2650 can be configured to receive data from eight channels. In other examples, the second sensor set 2650 can be configured to have more than eight channels or less than eight channels as needed. In this example, the eight channels include one channel that can monitor a single-axis reference sensor signal and six channels that can monitor two three-axis sensor signals. The remaining channels can monitor temperature signals. In one example, the second set 2650 can monitor a single-axis sensor 2662, a three-axis sensor 2682, a three-axis sensor 2684, and a temperature sensor 2702. During the vibration survey of the machine 2600 according to the present invention, the second set 2650 can first monitor the three-axis sensor 2682 and the three-axis sensor 2684 simultaneously, and then monitor the axis sensor 2686 and the three-axis sensor 2688 simultaneously.

[0143] According to the present invention, after monitoring the triaxial sensor 2680, the second set 2650 may monitor other triaxial sensors on the machine 2600 (in pairs simultaneously) as needed, which are part of a predetermined route list associated with a vibration survey of the machine 2600. During the vibration survey, the second set 2650 may continue to monitor the single-axis sensor 2662 and the temperature sensor 2702 at their constant positions, and the second set 2650 may continuously monitor multiple triaxial sensors according to a predetermined route plan for the vibration survey.

[0144] Continue to refer Fig.12 Many embodiments include a third machine 2800 having a rotating or oscillating component 2810 or both, each component supported by a set of bearings 2820 including a bearing set 2822, a bearing set 2824, a bearing set 2826, and more bearing sets as needed. The third machine 2800 can be monitored by a third sensor set 2850. The third set 2850 can be configured with a single-axis sensor 2860 and two three-axis (e.g., orthogonal axis) sensors 2880, 2882. In many examples, the single-axis sensor 2860 can be fastened to the machine 2800 by a user to allow sensing of the position of one rotating or oscillating component of the machine 2800. The three-axis sensors 2880, 2882 can also be positioned on the machine 2800 by a user to allow sensing of the position of each bearing in a set of bearings, the bearings being respectively associated with the rotating or oscillating components of the machine 2800. The third set 2850 can also include a temperature sensor 2900. Unlike the first set 2450 and the second set 2650, the third set 2850 and its sensors can be moved to other machines.

[0145] Many embodiments also include a fourth machine 2950 having a rotating or oscillating component 2960 or both, each component supported by a set of bearings 2970 including a bearing set 2972, a bearing set 2974, a bearing set 2976, and more bearing sets as needed. When the user moves the third sensor set 2850 to the fourth machine 2950, ​​the fourth machine 2950 can also be monitored by the third sensor set 2850. Many embodiments also include a fifth machine 3000 having a rotating or oscillating component 3010 or both. The fifth machine 3000 may not be explicitly monitored by any sensor or any sensor set during operation, but it may generate vibration or other pulse energy of sufficient magnitude to be recorded in data associated with any of the machines 2400, 2600, 2800, 2950 in a vibration survey.

[0146] Many embodiments include monitoring a first sensor set 2450 on a first machine 2400 via a predetermined route as disclosed herein. Many embodiments also include monitoring a second sensor set 2650 on a second machine 2600 via a predetermined route. The location of machine 2400 in proximity to machine 2600 may be included in the contextual metadata of both vibration surveys. A third set 2850 may be moved between machine 2800, machine 2950, ​​and other suitable machines. Machine 3000 is not configured with onboard sensors, but may be monitored by the third sensor set 2850 when desired. Machine 3000 and its operating characteristics may be recorded in metadata associated with vibration surveys of other machines to indicate contributions due to its proximity.

[0147] Many embodiments include hybrid database adaptation for coordinating relational metadata and streaming raw data formats. According to the present invention, it should be appreciated that unlike older systems that utilize traditional database structures to associate nameplates and operating parameters (sometimes considered metadata) with discrete and relatively simple individual data measurements, more modern systems can collect relatively larger amounts of raw streaming data at higher sampling rates and higher resolutions. At the same time, according to the present invention, it should also be appreciated that the metadata network used to link and obtain or be associated with or both such raw data is expanding at an ever-increasing rate.

[0148] In one example, a single overall vibration level may be collected as part of a route or prescribed list of measurement points. The collected data may then be associated with database measurement location information for a point located on a bearing housing surface on a particular machine that is adjacent to the coupler in a vertical direction. Mechanical analysis parameters related to appropriate analysis may be associated with points located on the surface. Examples of mechanical analysis parameters related to appropriate analysis may include the operating speed of an axis passing through a measurement point on the surface. Other examples of mechanical analysis parameters related to appropriate analysis may include one or a combination of the following: the operating speed of all component axes of a piece of equipment and / or machine; the type of bearing being analyzed, such as plain bearings or roller bearings; the number of gear teeth on the gears (if a gearbox is present); the number of poles in the motor; the differential and line frequencies of the motor; the size of the roller bearing elements; the number of fan blades, etc. Examples of mechanical analysis parameters related to appropriate analysis may further include machine operating conditions, such as the load on the machine and whether the load is expressed in percentage, wattage, airflow, head pressure, horsepower, etc. Other examples of mechanical analysis parameters include information about neighboring machines that may affect the data obtained during the vibration study.

[0149] According to the present invention, it should be appreciated that a large number of equipment and machine types can support many different categories, each of which can be analyzed in significantly different ways. For example, it can be shown that some machines, such as screw compressors and hammer mills, are noisier in operation and can be expected to vibrate more significantly than other machines. It can be shown that machines that are known to vibrate more significantly require changes in vibration levels that may be considered acceptable for quieter machines.

[0150] The present invention further includes hierarchical relationships found in the collected vibration data that can be used to support appropriate analysis of the data. An example of hierarchical data includes: the interconnection of machine components (such as bearings) measured in the vibration survey, the relationship between the bearings (including the way the bearing is connected to a specific shaft, a specific pinion is installed in a specific gearbox on a specific shaft), and the relationship between the shaft, the pinion and the gearbox. The hierarchical data can further include at which specific point within the mechanical gear train the monitored bearing is located relative to other components in the machine. The hierarchical data can also detail whether the measured bearing in the machine is connected to another machine, the vibration of which may affect what is measured in the machine that is the subject of the vibration study.

[0151] Analysis of vibration data from bearings or other components that are related to each other in the hierarchical data can use table lookups, correlation searches between frequency patterns derived from the raw data, and specific frequencies from the machine metadata. In some embodiments, the above data can be stored in a relational database and can be retrieved from the database. In an embodiment, the National Instrument's Technical Data Management Solution (TDMS) file format can be used. The TDMS file format can be optimized for streaming various types of measurement data (i.e., binary digital samples of waveforms) and being able to manipulate hierarchical metadata.

[0152] Many embodiments include a hybrid relational metadata binary storage method (HRM-BSA). The HRM-BSA may include a relational database engine based on structured query language (SQL). The relational database engine based on structured query language may also include a raw data engine that can be optimized for throughput and storage density of uniform and relatively unstructured data. According to the present invention, it should be appreciated that benefits can be demonstrated in terms of collaboration between hierarchical metadata and a SQL relational database engine. In one instance, tagging techniques and indicator benchmarks can be used to correlate a raw database engine with a SQL relational database engine. Three examples of correlation between a raw database engine and a SQL relational database engine link include: indicators from a SQL database to raw data; indicators from auxiliary metadata tables or similar raw data groupings to a SQL database; independent storage tables outside the SQL database or raw data technology field.

[0153] refer to Fig.13 , the present invention may include indicators for Group 1 and Group 2, which may include: associated file names; path information; table names; database key fields used with existing SQL database technology, which can be used to associate specific database segments or locations; asset attributes of specific measurement raw data streams; records with associated timestamps / date stamps; or associated metadata, such as operating parameters, panel conditions, etc. With this example, the device 3200 may include a first machine 3202, a second machine 3204, and many other machines in the device 3200. The first machine 3202 may include a gearbox 3210, a motor 3212, and other components. The second machine 3204 may include a motor 3220 and other components. A number of waveforms 3230, including waveforms 3240, waveforms 3242, waveforms 3244, and other waveforms (as needed) may be collected from the machines 3202, 3204 in the device 3200. The waveforms 3230 may be associated with a local tag link table 3300 and a link raw data table 3400. The machines 3202, 3204 and their elements may be associated with a link table with a relational database 3500. Link Table The original data table 3400 and the link table with the relational database 3500 may be associated with a link table with an optional independent storage table 3600.

[0154] The present invention may include markers that can be applied to time stamps or sample lengths within the raw waveform data. Markers generally fall into two categories: preset markers or dynamic markers. Preset markers may be associated with preset or existing operating conditions, such as load, head pressure, airflow in cubic feet per minute, ambient temperature, RPM, etc. These preset markers may be fed directly into the data acquisition system. In some cases, preset markers may be collected on a data channel in parallel with the waveform data, such as waveforms for vibration, current, voltage, etc. Alternatively, the value of the preset marker may be manually entered.

[0155] For dynamic tagging such as trending data, it may be critical to compare similar data, for example, comparing vibration amplitude and pattern to a set of repeatable operating parameters. One embodiment of the present invention includes a parallel channel input, which is a key phasor trigger pulse from the operating shaft, which can provide RPM information at the moment of collection. In this example of dynamic tagging, the portion of the collected waveform data can be labeled with the appropriate speed or speed range.

[0156] The present invention may also include dynamic tags that may be associated with data that may be obtained from post-processing and analysis of sample waveforms. In other embodiments, dynamic tags may also be associated with parameters derived after collection (including RPM) and other metrics derived from operation (e.g., alarm conditions such as maximum RPM). In some instances, many modern devices that are candidates for vibration surveys with portable data collection systems described herein do not include tachometer information. This may be because even though the measurement of RPM may be the most important for vibration surveys and analysis, adding a tachometer is not always practical or cost-effective. It should be understood that for fixed-speed machinery, especially when the approximate speed of the machine can be determined in advance, it is not so important to obtain an accurate RPM measurement; however, variable speed drives are becoming more and more common. According to the present invention, it should also be understood that various signal processing techniques can allow RPM to be derived from the raw data without the need for a dedicated tachometer signal.

[0157] In many embodiments, the RPM information can be used to tag segments of raw waveform data in the collection history thereof. Other embodiments include techniques for collecting instrument data following a prescribed route in a vibration study. Dynamic tagging can enable analysis and trending software to use multiple segments of the collection time interval indicated by the tag (e.g., two minutes) as multiple historical collection sets, rather than just one set as in previous systems where the route collection system would have stored data for only one RPM setting. As previously described, this in turn can be extended to any other operating parameters, such as load settings, ambient temperature, etc. However, dynamic tags that can be placed in a type of index file pointing to the raw data stream can categorize portions of the stream in a homogeneous entity, making it easier to compare with previously collected portions of the raw data stream.

[0158] Many embodiments include a hybrid relational metadata binary storage method that can fully utilize existing technologies for relational data streams and raw data streams. In an embodiment, the hybrid relational metadata binary storage method can match data with various tagged links. Tagged links can allow fast searches of relational metadata, and raw data can be analyzed more efficiently using conventional SQL techniques and existing technologies. This can mean that many functions, links, compatibilities, and extensions that conventional database technologies cannot provide can be utilized.

[0159] Tag links can also allow the use of conventional binary storage and data compression techniques to quickly and efficiently store raw data. This can indicate that many functions, links, compatibilities and extensions provided by conventional raw data technologies can be utilized, such as TMDS (National Instruments), UFF (Universal File Format, such as UFF58), etc. Tag links can further allow the use of tag technology links, where richer data sets from the collection can be accumulated at the same collection time as more conventional systems. Richer data sets from the collection can store data snapshots associated with predetermined collection criteria, and the proposed system can utilize tag technology to derive multiple snapshots from the collected data stream. This can indicate that a relatively more comprehensive analysis of the collected data can be achieved. One of the benefits of doing so can be included in vibration points that are more trending at specific frequencies or orders such as operating speed relative to RPM, load, operating temperature, flow rate, etc., which can be collected in a time similar to the time it takes to collect data using a conventional system.

[0160] In an embodiment, the platform 100 may include a local data collection system 102 deployed in an environment 104 to monitor signals from machines, machine elements, and the environment in which the machines are located, including heavy machines deployed at a local work site or a distributed work site under common control. The heavy machines may include earthmoving equipment, heavy-duty highway industrial vehicles, heavy-duty off-road industrial vehicles, industrial machines deployed in various environments, such as turbines, turbomachinery, generators, pumps, pulley systems, manifold and valve systems, etc. In an embodiment, the heavy industrial machinery may also include earthmoving equipment, compacting equipment, hauling equipment, lifting equipment, transportation equipment, aggregate production equipment, equipment for concrete construction, and piling equipment. In an example, earthmoving equipment may include excavators, backhoes, loaders, bulldozers, skid loaders, trenchers, automatic graders, automatic scrapers, crawler loaders, and wheeled loading forklifts. In an example, construction vehicles may include dump trucks, tank trucks, dump trucks, and trailers. In an example, material handling equipment can include cranes, transporters, forklifts and cranes. In an example, construction equipment can include tunnel loading and unloading equipment, rollers, concrete mixers, hot mix equipment, road builders (ramming machines), stone crushers, pavers, slurry sealers, sprayers and heavy pumps. Other examples of heavy industrial equipment can include different systems for implementing traction, structure, transmission, control and information, etc. Heavy industrial equipment can include many different transmissions and combinations thereof to provide mobile power and provide power for accessories and onboard functions. In each such example, platform 100 can deploy local data collection system 102 in environment 104, and machines, motors, pumps, etc. are operated in environment 104 and are directly connected and integrated into each machine, motor, pump, etc.

[0161] In an embodiment, the platform 100 may include a local data collection system 102 deployed in an environment 104 to monitor data from machines in operation and machines under construction (eg, turbines and generators, such as Siemens TM SGT6-5000F TM Gas turbine, SST-900 TM Steam turbine, SGen6-1000A TM Generator and SGen6-100A TMIn embodiments, a local data collection system 102 may be deployed to monitor a steam turbine as it rotates in an electric current caused by hot water vapor that may be directed through the turbine, but which may also be generated from a different source, such as a gas furnace, a nuclear core, a molten salt ring, etc. In these systems, the local data collection system 102 may monitor the turbine and water or other fluid in a closed loop cycle in which the water condenses and is then heated until it evaporates again. The local data collection system 102 may monitor the steam turbine and the fuel source deployed to heat the water into steam separately. In embodiments, the operating temperature of the steam turbine may be between 500°C and 650°C. In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressures so that they may convert the corresponding steam pressures into rotational motion in an optimal manner.

[0162] The local data collection system 102 can also be deployed in a gas turbine arrangement, thereby monitoring not only the operating turbine, but also the hot combustion gases fed into the turbine, which may exceed 1500°C. Because these gases are much hotter than those in a steam turbine, the blades can be cooled using air that can be blown from smaller openings to form a protective film or boundary layer between the exhaust gas and the blades. This temperature profile can be monitored by the local data collection system 102. Unlike a typical steam turbine, a gas turbine engine contains a compressor, a combustor, and a turbine, which are all journaled to rotate with a rotating shaft. The construction and operation of each of these components can be monitored by the local data collection system 102.

[0163] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from a hydro turbine used as a rotary engine that can capture energy from flowing water and use it to generate electricity. The type of hydro turbine or hydroelectric power generation equipment selected for a project can be based on the static water height (commonly referred to as head) and flow (or volume of water) at the site. In this example, a generator can be placed at the top of the shaft connected to the hydro turbine. When the turbine captures the naturally flowing water in its blades and rotates, the turbine sends rotational power to the generator to generate electrical energy. In this way, the platform 100 can monitor signals from generators, turbines, local water supply systems, flow controls (such as dam windows and sluices). In addition, the platform 100 can monitor the local conditions of the power grid, including loads, predicted demand, frequency response, etc., and include such information in the monitoring and control deployed by the platform 100 in these hydroelectric environments.

[0164] In an embodiment, the platform 100 may include a local data collection system 102 deployed in an environment 104 to monitor signals from energy production environments such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid energy regeneration equipment, etc. Many of these devices may use various forms of energy acquisition devices powered by heat from nuclear, gas, sunlight, and molten salt heat sources, such as wind turbines, hydroelectric turbines, and steam turbines. In an embodiment, the elements in such systems may include transmission lines, heat exchangers, desulfurization scrubbers, pumps, coolers, recuperators, chillers, etc. In an embodiment, certain embodiments of turbomachinery, turbines, scroll compressors, etc. may be configured as array controls to monitor large facilities that generate electricity for consumption, provide refrigeration, generate steam for local manufacturing and heating, etc., and the array control platform may be provided by a supplier of industrial equipment (such as Honeywell and its Experion TM PKS platform). In an embodiment, the platform 100 can specifically communicate with and integrate local manufacturer specific controls, and can allow devices from one manufacturer to communicate with other devices. In addition, the platform 100 allows local data collection systems 102 to collect information on systems from many different manufacturers. In an embodiment, the platform 100 can include a local data collection system 102 deployed in an environment 104 to monitor data from offshore industrial equipment, marine diesel engines, shipbuilding units, oil and gas plants, refineries, petrochemical plants, ballast water treatment solutions, marine pumps and turbines, etc.

[0165] In an embodiment, the platform 100 may include a 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 example, a sensor may be a device that can be used to detect or respond to a certain type of input (such as an electrical signal, a thermal signal, or an optical signal) in a physical environment. In an embodiment, the local data collection system 102 may include, but is not limited to, a plurality of sensors: for example, a temperature sensor, a pressure sensor, a torque sensor, a flow sensor, a thermal 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, etc. The torque sensor may include a magnetic torsion angle sensor. In one example, the torsion sensor and the speed sensor in the local data collection system 102 may be similar to the sensors discussed in U.S. Patent No. 8,352,149 to Meachem, which was published on January 8, 2013 and is hereby incorporated by reference herein as if fully set forth herein. In an embodiment, 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, etc.

[0166] In an embodiment, the platform 100 may include a 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, improper materials, improper material properties, accuracy of proper size, accuracy of proper shape, proper weight, accuracy of balance. Other fault sensors include sensors used for inventory control and inspection to confirm that parts have been packaged as planned, parts have planned tolerances, indicate the presence of packaging damage or crushing, and sensors that may indicate impact or damage in transit. Other fault sensors may include detection of insufficient lubrication, excessive lubrication, the need to clean sensor detection windows, maintenance needs caused by low lubrication, maintenance needs caused by blockages or reduced flow in lubrication areas, etc.

[0167] In an embodiment, the platform 100 may include a local data collection system 102 deployed in an environment 104, which includes aircraft operations and manufacturing, including monitoring signals of sensors used for special applications, such as sensors used in the aircraft's attitude and heading reference system (AHRS), such as gyroscopes, accelerometers, and magnetometers. In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals of image sensors such as semiconductor charge coupled devices (CCDs) in complementary metal oxide semiconductor (CMOS) or N-type metal oxide semiconductor (NMOS, active MOS) technology, active pixel sensors, etc. In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as infrared (IR) sensors, ultraviolet (UV) sensors, touch sensors, proximity sensors, etc. In an embodiment, the platform 100 may include a local data collection system 102 deployed in an 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 diagnosis, health monitoring, etc.

[0168] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as micro-electromechanical system (MEMS) sensors, such as ST Microelectronic's TM The LSM303AH smart MEMS sensor includes a 3D digital linear acceleration sensor and a 3D digital magnetic sensor in an ultra-low power, high-performance system-in-package.

[0169] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from other large machines such as turbines, windmills, industrial vehicles, robots, etc. These large machines include multiple components and elements that provide multiple subsystems on each machine. To this end, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from individual elements such as axles, bearings, belts, barrels, gears, shafts, gearboxes, cams, brackets, camshafts, clutches, brakes, rollers, generators, feeders, flywheels, pads, pumps, claws, machine arms, seals, sockets, sleeves, valves, wheels, actuators, motors, servomotors, etc. Many machines and their elements may include servomotors. The local data collection system 102 may monitor the motors, rotary encoders, and potentiometers of the servomechanisms to provide three-dimensional details of the position, placement, and progress of the industrial process.

[0170] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals of gear drives, powertrains, transfer cases, multi-speed shafts, transmissions, direct drives, chain drives, belt drives, shaft drives, magnetic drives, and similar meshing mechanical drives. In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals of industrial machine fault conditions, which may include overheating, noise, gear grinding, gear locking, excessive vibration, oscillation, insufficient inflation, excessive inflation, etc. Operational failures, maintenance indications, and maintenance or operational problems from interactions with other machines may occur during operation, installation, and maintenance. Failures may occur not only in the mechanisms of industrial machines, but also in the infrastructure supporting the machines, such as their wiring and local mounting platforms. In an embodiment, large industrial machines may face different types of fault conditions, such as overheating, noise, gear grinding, excessive vibration of machine components, fan vibration problems, and problems with rotating components of the machine.

[0171] In an embodiment, the platform 100 may include a local data collection system 102 deployed in an environment 104 to monitor signals from industrial machinery, including failures caused by premature bearing failures that may occur due to contamination or loss of bearing lubricant. In another example, mechanical defects such as bearing misalignment may occur. Many factors may cause failures, such as metal fatigue, so the local data collection system 102 can monitor cyclic and local stresses. With this example, the platform 100 can monitor improper operation of machine components, lack of component maintenance and repair, corrosion of important machine components such as couplings or gearboxes, misalignment of machine components, etc. Although it is impossible to completely prevent the occurrence of failures, many industrial failures can be mitigated to reduce operational losses and economic losses. The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments, which may show that this is more cost-effective than a scheduled maintenance process that replaces components based on a fixed time limit rather than the actual load and wear on the component or machine. To this end, the platform 10 can provide reminders for preventive measures or perform some preventive measures, such as complying with the machine's operating manual and mode instructions, properly lubricating and maintaining machine components, minimizing or eliminating machine overruns that exceed the machine's limited capacity, replacing worn but still usable parts as needed, and providing training to relevant personnel on the use of the machine.

[0172] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by multiple physical, electronic and symbolic formats or signals. The platform 100 may use signal processing, including multiple mathematical, statistical, computational, heuristic and linguistic representations and processing of signals and multiple operations required to extract useful information from signal processing operations (such as techniques for representing, modeling, analyzing, synthesizing, sensing, collecting and extracting information from signals). In an example, signal processing can be performed using a variety of techniques, including but not limited to conversion, spectrum estimation, statistical operations, probability operations and random operations, numerical theory analysis, data mining, etc. The processing of various types of signals forms the basis of many electrical or computing processes. Therefore, signal processing is applicable to almost all disciplines and applications in industrial environments, such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, noise reduction and other quality improvements, image enhancement, etc. Signal processing for images can include pattern recognition for manufacturing inspection, quality inspection and automated operation inspection and maintenance. The platform 100 can use many pattern recognition techniques, including techniques that can classify input data into multiple categories based on key features to achieve the purpose of identifying patterns or regularities in the data. The platform 100 can also implement a pattern recognition process with machine learning operation, and can be used in applications such as computer vision, speech and text processing, radar processing, handwriting recognition, CAD systems, etc. The platform 100 can use supervised classification and unsupervised classification. A classifier for image or pattern recognition can be created based on supervised learning classification algorithms based on training data obtained from different object categories. Unsupervised learning classification algorithms can operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering. For example, some analysis techniques for unsupervised learning can include K-means clustering, Gaussian mixture models, hidden Markov models, etc. The algorithms used in supervised and unsupervised learning pattern recognition methods enable pattern recognition to be used in a variety of high-precision applications. The platform 100 can 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 transportation systems, license plate monitoring, etc.

[0173] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104, which uses machine learning to achieve inference-based learning results from a computer without programming. Therefore, the platform 100 can learn and make decisions from a set of data by making data-driven predictions and adapting according to the data set. In an embodiment, machine learning may involve performing multiple machine learning tasks by a machine learning system, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include providing a set of instance inputs and required inputs to the machine learning system. Unsupervised learning may include the learning algorithm itself structuring its inputs through methods such as pattern detection and / or feature learning. Reinforcement learning may include the machine learning system executing in a dynamic environment and then providing feedback on correct and incorrect decisions. In an example, machine learning may include multiple other tasks based on the output of the machine learning system. In an example, tasks can also be classified as machine learning problems, such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, etc. In an example, machine learning may include a variety of mathematical and statistical techniques. In an example, many types of machine learning algorithms can include decision tree-based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian networks, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forests, K-means, gradient boosting and adaptive boosting, K-nearest neighbors (KNN), prior algorithms, etc. In an embodiment, certain machine learning algorithms (such as genetic algorithms defined to solve constrained and unconstrained optimization problems, which may be based on natural selection, i.e., the process of promoting biological evolution) can be used. With this example, genetic algorithms can be deployed to solve a variety of optimization problems that are not very suitable for standard optimization algorithms, including problems where the objective function is discontinuous, non-differentiable, random or highly nonlinear. In an example, genetic algorithms can be used to solve mixed integer programming problems, in which some components are restricted to integer values. Genetic algorithms and machine learning techniques and systems can be used in computational intelligence systems, computer vision, natural language processing (NLP), recommendation systems, reinforcement learning, building graphical models, etc. By this example, machine learning systems can be used to perform intelligent computing-based control and respond to tasks in a variety of systems, such as interactive websites and portals, brain-computer interfaces, online security and fraud detection systems, medical applications such as diagnostic and treatment assistance systems, DNA sequence classification, etc. In practice, machine learning systems can be used in advanced computing applications such as online advertising, natural language processing, robotics, search engines, software engineering, speech and handwriting recognition, pattern matching, gaming, computational anatomy, bioinformatics systems, etc.In practice, machine learning can also be used in financial and marketing systems, such as user behavior analysis, online advertising, economic evaluation, financial market analysis, etc.

[0174] The following provides information on combining Figures 1 to 6 Other details of the methods, systems, devices and components described. In an embodiment, the present invention discloses a method and system for cloud-based machine pattern recognition based on remote simulated industrial sensor fusion. For example, data streams from vibration, pressure, temperature, acceleration, magnetic field, electric field and other simulated sensors can be multiplexed or otherwise fused, forwarded through a network relay and fed to a cloud-based machine learning facility, which can use one or more models related to the operating characteristics of industrial machines, industrial processes or their components or elements. The model can be created by a person with relevant experience in an industrial environment and can be associated with a training data set, such as a model created by manual analysis or machine analysis of data collected by sensors in the environment or other similar environments. Then, the learning machine can operate on other data, initially using a set of rules or elements of the model to provide various outputs, such as classifying data, identifying certain patterns (such as patterns indicating the presence of faults, or patterns indicating operating conditions such as fuel efficiency, capacity generation, etc.). The machine learning facility can accept feedback, such as one or more inputs or success metrics, so that it can train or improve its initial model, such as by adjusting weights, rules, parameters, etc. based on feedback. For example, a fuel consumption model for an industrial machine may include physical model parameters that characterize weight, motion, drag, momentum, inertia, acceleration, and other factors indicative of consumption; and chemical model parameters, such as parameters that predict energy produced and / or consumed by combustion, by chemical reactions in battery charging and discharging, and the like. The model may be refined by being fed with data from sensors disposed in the machine environment, in the machine, and data indicative of actual fuel consumption, so that the machine may provide increasingly accurate sensor-based fuel consumption estimates, and may also provide outputs indicating changes that may be made to increase fuel consumption (such as changing the machine's operating parameters or changing other elements of the environment, such as ambient temperature, operation of nearby machines, and the like). For example, if a resonance effect between two machines adversely affects one of the machines, the model may take this into account and automatically provide outputs that change the operation of one of the machines, such as reducing the resonance, increasing the fuel efficiency of one or both machines. By continuously adjusting parameters to bring the output into line with actual conditions, the machine learning facility may self-organize to provide a highly accurate model of environmental conditions, such as for predicting failures, optimizing operating parameters, and the like. This can be used to increase fuel efficiency, reduce wear, increase output, extend operating life, avoid failure conditions, and many other purposes.

[0175] Fig.14 Shows the components and interactions of a data collection architecture that involves applying cognitive and machine learning systems to data collection and processing. Fig.14 , the data collection system 102 can be set in an environment, such as an industrial environment in which one or more complex systems of electromechanical systems and machines are manufactured, assembled or operated. The data collection system 102 can include onboard sensors and can receive input from one or more sensors (such as any type of analog or digital sensor disclosed herein) and from one or more input sources 116 (such as through WiFi, Bluetooth, NFC or other local network connections or sources available through the Internet) through one or more input interfaces or ports 4008, etc. Multiple sensors can be combined and multiplexed for processing, such as using one or more multiplexers 4002. Data can be cached or buffered in a cache / buffer 4022 and can be used for external systems such as the remote host processing system 112 described elsewhere in the present invention (which can include a wide range of processing architectures 4024, including any elements described in conjunction with other embodiments described in the present invention and the figures) through one or more output interfaces and ports 4010 (in embodiments, it can be separate from or the same as the input interface and port 4008). The data collection system 102 can be configured to obtain inputs from the main processing system 112, such as inputs from the analysis system 4018, which can operate on data from the data collection system 102 and data from other input sources 116 to provide analysis results, which in turn can be provided to the data collection system as learning feedback input 4012 to assist in the configuration and operation of the data collection system 102.

[0176] The combination of inputs (including the selection of which sensors or input sources to turn "on" or "off") may be performed using a local cognitive input selection system 4004, an optional remote cognitive input selection system 4114, or a combination of both, etc., under the control of machine-based intelligence. The cognitive input selection systems 4004, 4014 may use intelligence and machine learning capabilities as described elsewhere herein, such as using detected conditions (such as conditions reported by input sources 116 or sensors), state information (including state information determined by a machine state recognition system 4020 that can determine states), such as those related to operating states, environmental states, states in a known process or workflow, states involving fault or diagnostic conditions, or many other states. This may include optimizing input selection and configuration based on learning feedback from a learning feedback system 4012, which may include providing training data (such as data from the host processing system 112 or from other data collection systems 102 directly or through the host 112) and may include providing feedback metrics, such as success metrics calculated within an analysis system 4018 of the host processing system 112. For example, if a data stream consisting of a particular sensor and input combination produces positive results under a given set of conditions (e.g., provides improved pattern recognition, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, etc.), metrics related to the results from the analysis system 4018 can be provided to the cognitive input selection system 4004, 4014 through the learning feedback system 4012 to help configure future data collection to select combinations under these conditions (e.g., by powering down other sensors, allowing other input sources to be deselected). In embodiments, under the control of one or more cognitive input selection systems 4004, selection and deselection of sensor combinations may occur in an automatically changing manner, such as using genetic programming techniques, so that effective combinations for a given state or set of conditions are promoted and less effective combinations are demoted over time based on learning feedback 4012 from the analysis system 4018, etc., so that the local data collection system is gradually optimized and adapted for each unique environment. Thus, an automatically adapting multi-sensor data collection system is provided in which cognitive input selection is used with feedback to improve the effectiveness, efficiency, or other performance parameters of the data collection system in its specific environment. The performance parameters may be related to overall system metrics (e.g., financial benefits, process optimization results, energy production or use, etc.), analytical metrics (e.g., successful pattern recognition, prediction, classification of data, etc.), and local system metrics (e.g., bandwidth utilization, memory utilization, power consumption, etc.). In an embodiment, the host's analysis system 4018, state system 4020, and cognitive input selection system 4114 can receive data from multiple data collection systems 102 so that optimization (including optimization of input selection) can be performed by coordinating the 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 X-axis vibration data, the X-axis vibration sensor of another data collection system may be turned off, which is beneficial for obtaining Y-axis data from another data collector 102. Therefore, through the coordinated collection of the master cognitive input selection system 4114, the activities of multiple collectors 102 on many different sensors can provide a rich data set for the master processing system 112 without wasting energy, bandwidth, storage space, etc. As described above, optimization can be based on overall system success metrics, analytical success metrics, and local system metrics, or a combination of the above.

[0177] Disclosed herein are methods and systems for cloud-based machine pattern analysis of multiple industrial sensor state information to provide expected state information for industrial systems. In an embodiment, machine learning can utilize a state machine, such as tracking the states of multiple analog and / or digital sensors, feeding the states to a pattern analysis facility, and determining the expected state of an industrial system based on historical data about a sequence of state information. For example, in the case where the temperature state of an industrial machine exceeds a certain threshold and then a fault condition occurs (such as a failure of a set of bearings), the temperature state can be tracked by a pattern recognizer, and the pattern recognizer can generate an output data structure indicating an expected bearing fault state whenever a high temperature input state is identified. Various measurements and expected states of temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, current state, magnetic field state, electric field state, capacitance state, charge and discharge state, motion, position, and many other parameters can be managed by a state machine. The state can include a combined state, where the data structure includes a series of states, each state represented by a position in a byte-like data structure. For example, the characteristics of industrial machines can be genetic structures, such as structures that provide pressure, temperature, vibration, and acoustic data, the measurements of which occupy a position in the data structure, so that the combined state can be operated on a byte-like structure, such as for succinctly describing the current combined state of the machine or environment or succinctly describing the expected state. Such byte-like structures can be used by state machines for machine learning, such as pattern recognition that operates on the structure to determine the pattern of the combined effect that affects multiple conditions. Many such structures can be tracked and used, such as in machine learning, to represent various combinations of various lengths of different elements that can be sensed in an industrial environment. In an embodiment, the byte-like structure can be used in genetic programming techniques, such as by replacing different types of data or data from different sources and tracking the results over time, so as to generate one or more favorable structures based on the success of these structures when used in real-world scenarios, the above-mentioned successful examples include successful predictions indicating expected states or achieving successful operational results, such as increased efficiency, successful routing information, and achieving profit growth. That is, by varying the data types and sources used in a byte-like structure (for machine optimization over time), a genetic programming-based machine learning facility can "evolve" a set of data structures that include a favorable mix of data types (e.g., pressure, temperature, and vibration) from a favorable mix of data sources (e.g., temperature from sensor X, vibration from sensor Y) for a given purpose. Different desired outcomes can result in different data structures that best support the effective achievement of those outcomes over time through genetic programming with applied machine learning and structural upgrades that have favorable results (for the desired outcome of interest).The upgraded data structure can provide concise and efficient data for various activities described throughout the present invention, including storage in a data pool (which can be optimized by storing favorable data structures that provide the best operating results for a given environment), presentation in a data market (such as presentation as the most efficient structure for a given purpose), and other activities.

[0178] In an embodiment, a platform is provided with cloud-based machine pattern analysis of state information from multiple simulated industrial sensors to provide expected state information for industrial systems. In an embodiment, a host processing system 112, such as provided in the cloud, may include a state system 4020 that may be used to infer or calculate a current state or to determine an expected future state associated with a data collection system 102 or an aspect of an environment in which the data collection system 102 is located, such as a machine state, component, workflow, process, event (e.g., whether an event has occurred), object, person, condition, function, etc. Maintaining state information allows the host processing system 112 to perform analysis in one or more analysis systems 4018 to determine contextual information, apply semantic and conditional logic, and perform many other functions implemented by the processing architecture 4024 described throughout the present invention.

[0179] In an embodiment, a platform is provided with a cloud-based policy automation engine for IoT (creation, deployment and management of IoT devices). In an embodiment, the platform 100 includes (or is integrated with, or includes) a main processing system 112 (e.g., located on a cloud platform), a policy automation engine 4032 for automating the creation, deployment and policy management of IoT devices. Policies including access policies, network usage policies, memory usage policies, bandwidth usage policies, device connection policies, security policies, rule-based policies, role-based policies, and other policies may be needed to manage the use of IoT devices. For example, since IoT devices may have many different networks and data communications than other devices, policies may be needed to indicate the devices to which a given device can be connected, the data that can be transmitted, and the data that can be received. Since billions of devices with countless potential connections are expected to be deployed in the near future, people cannot configure policies for IoT devices on a connection-by-connection basis. Therefore, the intelligent policy automation engine 4032 may include cognitive features for creating, configuring, and managing policies. The policy automation engine 4032 may use possible policy information from a policy database or library, etc., which may contain one or more public sources of available policies. These policies can be written in one or more conventional policy languages ​​or scripts. The policy automation engine 4032 can apply policies based on 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) can include a policy that only a verified local controller can change certain parameters of power generation, thereby avoiding remote "takeover" by hackers. This can in turn be achieved by automatically finding and applying security policies that prevent the control infrastructure of the machine from connecting to the Internet by requiring access authentication, etc. The policy automation engine 4032 can include cognitive features, such as changing the application of policies, the configuration of policies, etc., such as features based on state information from the state system 4020. The policy automation engine 4032 can receive feedback from the learning feedback system 4012 based on one or more analysis results from the analysis system 4018, based on overall system results (such as the extent of security vulnerabilities, policy violations, etc.), local results, and analysis results. Through changes and selections based on such feedback, the policy automation engine 4032 can learn over time to automatically create, deploy, configure, and manage policies on a large number of devices, such as managing policies for configuring connections between IoT devices.

[0180] Disclosed herein are methods and systems for device-built-in sensors and data storage for industrial IoT devices, including device-built-in sensors and data storage for industrial IoT devices, wherein data from multiple sensors are multiplexed in a device for storing fused data streams. For example, pressure and temperature data can 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 the data structure, so that pressure and temperature remain associated in time without the need to process the streams separately through an external system), or by addition, division, multiplication, subtraction, etc., so that the fused data can be stored on the device. Any sensor data type described throughout the present invention can be fused in this manner and stored in a local data pool, memory, or on an IoT device, such as a data collector, machine component, etc.

[0181] In an embodiment, a platform is provided that has device-built-in sensors and data storage for industrial IoT devices. In an embodiment, a cognitive system is used for a self-organizing storage system 4028 of a data collection system 102. Sensor data, especially simulated sensor data, can consume a large amount of storage capacity, especially when the data collector 102 has multiple onboard sensor inputs or sensor inputs from the local environment. Simply storing all data indefinitely is usually not a good option, and even transmitting all data may exceed bandwidth limits, bandwidth licenses (such as exceeding cellular data plan capacity), etc. Therefore, storage strategies are required. These strategies typically include capturing only portions of the data (such as snapshots), storing data for a limited period of time, storing portions of the data (such as intermediate or abstract forms), etc. There are many possible choices in these options and other options, and determining the appropriate storage strategy may be extremely complex. In an embodiment, the self-organizing storage system 4028 can use a cognitive system based on learning feedback 4012, such as using various metrics from the analysis system 4018 or another system of the main cognitive input selection system 4114, such as overall system metrics, analysis metrics, and local performance indicators. The self-organizing storage system 4028 can automatically change storage parameters such as storage location (including local storage on the data collection system 102, storage on nearby data collection systems 102 (such as using peer organization), and remote storage, such as network-based storage), storage volume, storage duration, data types stored (including individual sensors or input sources 116, as well as various combinations or multiplexed data, such as data selected under the cognitive input selection system 4004, 4014), storage type (such as use of RAM, flash memory, or other short-term storage relative to available hard drive space), storage organization (such as in raw form, hierarchical, etc.), and other parameters. Changes to parameters can be made based on feedback so that over time, the data collection system 102 adapts its data storage to optimize itself based on the conditions of its environment (such as a particular industrial environment) so that it stores the right amount of data needed and the right type of data available to the user.

[0182] In embodiments, the local cognitive input selection system 4004 may organize the fusion of data from various onboard sensors, external sensors (e.g., in the local environment), and other input sources 116 to the local collection system 102 into one or more fused data streams, thereby creating various signals using multiplexers 4002, etc., such signals representing the combination, arrangement, blending, layering, extraction, data-metadata combination, etc. of the source analog and / or digital data processed by the data collection system 102. The selection of a particular sensor fusion may be determined locally by the cognitive input selection system 4004 based on learning feedback (e.g., various overall system, analysis system, and local system results and metrics), etc. from the learning feedback system 4012. In embodiments, the system may learn a particular combination and arrangement of fused sensors to best achieve appropriate state expectations, etc., as indicated by feedback from the analysis system 4018 regarding its ability to predict future states (e.g., various states processed by the state system 4020). For example, the input selection system 4004 may direct the selection of a subset of sensors from a larger set of available sensors, and the inputs from the selected sensors may be combined, for example, by placing the inputs into bytes of a defined multi-bit data structure (e.g., by acquiring the signals at a given sampling rate or time and placing the results into a byte structure, and then collecting and processing the bytes over time to combine), by multiplexing in the multiplexer 4002 (e.g., by combining by additive mixing of consecutive signals), etc. Any of a variety of signal processing and data processing techniques for combining and fusion may be used, including convolution techniques, auto-conversion techniques, transformation techniques, etc. The particular fusion in question may be adapted to a given context through cognitive learning, such as by having the cognitive input selection system 4004 learn based on feedback 4012 from the results (e.g., transmitted by the analysis system 4018), so that the local data collection system 102 performs context-adaptive sensor fusion.

[0183] In an embodiment, the analysis system 4018 can be adapted to any of a variety of analysis techniques, including statistical and econometric techniques (e.g., linear regression analysis, using similarity metrics, heat map-based techniques, etc.), reasoning techniques (e.g., Bayesian reasoning, rule-based reasoning, inductive reasoning, etc.), iterative techniques (e.g., feedback, recursion, feedforward, and other techniques), signal processing techniques (e.g., Fourier and other transformations), pattern recognition techniques (e.g., Kalman and other filtering techniques), search techniques, probabilistic techniques (e.g., random walks, random forest algorithms, etc.), simulation techniques (e.g., random walks, random forest algorithms, linear optimization, etc.), and other techniques. This can include calculating various statistical values ​​or metrics. In an embodiment, the analysis system 4018 can be at least partially disposed on the data collection system 102, so that the local analysis system can calculate one or more metrics, such as metrics related to any of the items mentioned throughout the present invention. For example, metrics of efficiency, power utilization, memory utilization, redundancy, entropy, and other factors can be calculated on-board, so that the data collection 102 can implement the various cognitive and learning functions mentioned throughout the present invention without relying on a remote (e.g., cloud-based) analysis system.

[0184] In embodiments, the host processing system 112, the data collection system 102, or both may include, be connected to, or be integrated with a self-organizing network system 4020, which may include a cognitive system for providing machine-based intelligence or organizing network utilization for data in the data collection system (e.g., processing analog data and other sensor data or other sources between one or more local data collection systems 102 and the host system 112, etc.). This may include organizing network utilization for source data transmitted to the data collection system, feedback data (analytic data provided to or provided by the learning feedback system 4012), data used to support the market (such as data described in conjunction with other embodiments), and output data provided from one or more data collection systems 102 through output interfaces and ports 4010.

[0185] Disclosed herein are methods and systems for a self-organizing data marketplace for industrial IoT data, including organizing available data elements in a marketplace for consumption by consumers based on training a self-organizing facility using a training set and feedback from a marketplace success metric. A marketplace may be initially established to provide available data collected from one or more industrial environments, such as presenting data by type, source, environment, machine, one or more modes, etc., such as a menu or hierarchy. The marketplace may change the data collected, the organization of the data, the presentation of the data (including pushing the data to an external site, providing links, configuring an API that can access the data, etc.), the pricing of the data, etc., based on machine learning, etc., which may change any of the aforementioned different parameters. The machine learning facility can manage all of these parameters through self-organization, for example, by changing parameters over time (including changing elements of the data types presented), the source data used to obtain each data type, the data structure presented (such as byte-like structures, fused or multiplexed structures (such as representing multiple sensor types), and statistical structures (such as various mathematical results representing sensor information, etc.), data pricing, data presentation location, data presentation method (such as through API, link, push message, etc.), data storage method, data acquisition method, etc. As parameters change, feedback can be obtained on success metrics such as number of views, revenue per visit (e.g., payment price), Total revenue, unit profit, total profit, and many other metrics, and the self-organizing machine learning facility can promote configurations that improve the success metric and demote configurations that do not improve, so that over time, the market is gradually configured to present favorable data type combinations (e.g., combinations that provide stable predictions of the expected state of a given type of specific industrial environment) from favorable sources (e.g., reliable, accurate, and low-priced sources) at effective pricing (e.g., pricing that tends to provide higher total profits from the market). The market may include spiders, web crawlers, etc. that find input data sources, such as finding data pools that publish potentially relevant data, connected IoT devices, etc. These can be trained by human users and improved by machine learning in a manner similar to that described elsewhere in this invention.

[0186] In an embodiment, a platform is provided that has a self-organizing data marketplace for industrial IoT data. Fig.15In an embodiment, a platform is provided having a cognitive data marketplace 4102 (in some cases referred to as a self-organizing data marketplace) for data collected by one or more data collection systems 102 or from other sensors or input sources 116 located in various data collection environments (such as industrial environments). In addition to the data collection system 102, the data may include data collected, processed, or exchanged by IoT devices, such as cameras, monitors, embedded sensors, mobile devices, diagnostic devices and systems, instrumentation systems, telematics systems, etc., for monitoring various parameters and characteristics of machines, equipment, components, parts, operations, functions, conditions, states, events, workflows, and other elements (collectively referred to as the term "state") in the above environments. The data may also include metadata about any of the foregoing, such as describing the data, indicating the origin, indicating elements regarding identity, access, roles, and permissions, providing an overview of the data or extracting or otherwise supplementing one or more data items to enable further processing, such as for extracting, transforming, loading, and processing the data. This data (a term that includes metadata unless the context indicates otherwise) can be extremely valuable to third parties, either as individual elements (e.g., data about the state of an environment can be used as an example of a condition in a process), or as an aggregate (e.g., data collected on many systems and devices in different environments can be selectively used to develop behavioral models, train learning systems, etc.). With the deployment of billions of IoT devices (using countless connections), the amount of available data will explode. To enable access and utilization of data, the cognitive data market 4102 enables various components, features, services, and processes to be supplied, found, consumed, and traded by users in the form of data packets (e.g., batch data, data streams (including event streams), data from various data pools 4120, etc.). In an embodiment, the cognitive data market 4102 can be included in, connected to, or integrated with one or more other components of the main processing architecture 4024 of the main processing system 112 (e.g., a cloud-based system), and can be connected to various sensors, input sources 115, data collection systems 102, etc. The cognitive data marketplace 4102 may include a marketplace interface 4108, which may include one or more supplier interfaces through which data suppliers may provide available data and one or more consumer interfaces through which data may be found and collected. The consumer interface may include an interface to a data marketplace search system 4118, which may include features that enable a user to indicate the type of data desired to be acquired (e.g., by entering keywords in a natural language search interface featuring data or metadata).The search interface may use a variety of search and filtering techniques, including keyword matching, collaborative filtering (e.g., using known preferences or characteristics of consumers to match other similar consumers and other consumers' past results), and ranking techniques (e.g., ranking according to various metrics based on the success of past results as described in conjunction with other embodiments of the present invention). In an embodiment, the supply interface may allow data owners or suppliers to supply data through the cognitive data market 4102 in the form of one or more packages (e.g., packaging batch data, data streams, etc.). Suppliers may pre-package data by providing data from a single input source 116, a single sensor, etc., or by providing combinations, arrangements, etc. (e.g., multiplexed analog data, mixed bytes of data from multiple sources, extract, load and transform results, convolution results, etc.), and by providing metadata related to any of the foregoing. Packaging includes pricing based on per batch, streaming (e.g., reservations for event feeds or other feeds or streams), per item, revenue sharing, or other basis. For data involving pricing, the data trading system 4114 may track orders, deliveries, and utilization, including completion of orders. Transaction system 4114 may include rich transaction features, including digital rights management, for example, by managing keys that control access to purchased data and control usage (e.g., allowing a limited set of users or roles to use the data for a limited time, within a limited domain, or for a limited purpose). Transaction system 4114 may manage payments by processing credit cards, wire transfers, debits, and other forms of consideration.

[0187] In an embodiment, the cognitive data packaging system 4012 of the market 4102 can use machine-based intelligence to package data, for example, by automatically configuring data packets into batches, streams, pools, etc. In an embodiment, packaging can be based on one or more rules, models, or parameters, for example, 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 mentioned throughout the present invention) can be aggregated based on metadata indicating the type of data or by identifying features or characteristics in the data stream that indicate the nature of the data. In an embodiment, packaging can be performed using machine learning and cognitive capabilities, for example, by learning the combination, arrangement, blending, layering, etc. of input sources 116, sensors; information from the data pool 4120 and information from the data collection system 102 is likely to meet user needs or generate success metrics. Learning can be based on learning feedback 4012, such as based on metrics determined by the analysis system 4018, such as system performance metrics, data collection metrics, analysis metrics, etc. In an embodiment, the success metric may be related to a market success metric, such as a package view, a package occupancy, a package purchase or license, a payment made for a package, etc. These metrics can be calculated in the analysis system 4018, including associating specific feedback metrics with search terms and other inputs, so that the cognitive packaging system 4110 can find and configure the design to provide higher value to consumers and provide higher returns for data suppliers. In an embodiment, the cognitive data packaging system 4110 can use learning feedback 4012 to automatically change packaging, for example, using different combinations, arrangements, mixing, etc. and changing the weights applied to given input sources, sensors, data pools, etc., to promote favorable packages and no longer emphasize less favorable packages. This can be carried out using genetic programming and similar techniques for comparing the results of different packages. Feedback can include state information (such as information about various operating states, etc.) from the state system 4020, and information about market conditions and states (such as pricing and availability information of other data sources). Therefore, a self-adaptive cognitive data packaging system 4110 is provided, which automatically adapts to conditions to provide favorable data packets for the market 4102.

[0188] In an embodiment, a cognitive data pricing system 4112 may be provided to set pricing for data packages. In an embodiment, the data pricing system 4112 may set pricing using a set of rules, models, and the like, such as pricing based on supply conditions, demand conditions, various available sources, and the like. For example, the pricing of a package may be configured to be set based on the sum of the prices of the constituent elements (e.g., input sources, sensor data, and the like), or based on a rule-based discount on the sum of the prices of the constituent elements, and the like. Rules and conditional logic may be applied, such as rules that take into account cost factors (e.g., bandwidth and network usage, peak demand factors, scarcity factors, and the like), rules that take into account usage parameters (e.g., purpose, domain, user, role, duration, and the like of the package), and many other rules. In an embodiment, the cognitive data pricing system 4112 may include full cognitive intelligence features using genetic programming and the like, such as automatically changing pricing and tracking feedback on results (e.g., metrics based on various financial domains, utilization metrics, and the like, which may be provided by computing metrics from the data from the data trading system 4114 in the analysis system 4018).

[0189] Disclosed herein are methods and systems for self-organizing data pools, which may include self-organization of data pools based on utilization and / or revenue metrics, including utilization and / or revenue metrics tracked for multiple data pools. The data pool may initially include an unstructured or loosely structured data pool (containing data from an industrial environment), such as sensor data from or about industrial machines or components. For example, the data pool may obtain data streams from various machines or components in the environment (e.g., turbines, compressors, batteries, reactors, engines, motors, vehicles, pumps, rotors, axles, bearings, valves, and many other components), wherein the data streams contain analog and / or digital sensor data (of various types), published data about operating conditions, diagnostic and fault data, identification data of the machine or component, asset tracking data, and many other types of data. Each data stream may have an identifier in the pool, such as an identifier indicating its source and (optionally) type. The data pool may be accessed by an external system, for example, through one or more interfaces or APIs (e.g., RESTful APIs), or by a data integration element, such as a gateway, a proxy, a bridge, a connector, etc., and the data pool may use similar functionality to access available data streams. The data pool may be managed by a self-organizing machine learning facility that may configure the data pool, e.g., by managing sources for the pool, managing available streams, and managing APIs or other connections into and out of the data pool. Self-organization may receive feedback, e.g., based on success metrics, utilization and benefit metrics (including consideration of the cost of acquiring and / or storing data and the benefit of the pool, as measured by profit or other metrics including indications of user usefulness, etc.). For example, a self-organizing data pool may identify that chemical and radiation data for an energy production environment is regularly accessed and extracted, while vibration and temperature data are not being used, in which case the data pool may be automatically reorganized, e.g., by ceasing to store vibration and / or temperature data, or by obtaining better sources of such data. Such automatic reorganization may also be applied to data structures, e.g., promoting different data types, different data sources, different data structures, etc., through incremental iteration and feedback.

[0190] In an embodiment, a platform with data pool self-organization based on utilization and / or revenue metrics is provided. In an embodiment, the data pool 4020 can be a self-organizing data pool 4020, such as organized by cognitive capabilities, as described throughout the present invention. The data pool 4020 can self-organize in response to learning feedback 4012, such as feedback based on metrics and results, including metrics and results calculated in the analysis system 4018. The organization can include determining the data or data packets to be stored in the pool (e.g., representing a specific combination, arrangement, aggregation, etc.), the structure of such data (e.g., flat, hierarchical, linked or other structure), storage duration, the nature of the storage medium (e.g., hard disk, flash memory, SSDs, network-based storage, etc.), the arrangement of storage bits, and other parameters. The content and nature of the storage can change so that the data pool 4020 can learn and adapt, such as based on the state of the host system 112, one or more data collection systems 102, storage environment parameters (e.g., capacity, cost and performance factors), data collection environment parameters, market parameters, and many other parameters. In an embodiment, the data pool 4020 can learn and adapt, for example, by changing the above parameters and other parameters in response to benefit metrics, such as return on investment, power utilization optimization, revenue optimization.

[0191] Disclosed herein are methods and systems for training AI models based on industry-specific feedback, including training AI models based on industry-specific feedback that reflects a measure of utilization, revenue, or impact, and wherein the AI ​​models operate on sensor data from an industrial environment. As described above, these models may include models for industrial environments, machines, workflows, models for predicting states, models for predicting failures and optimizing maintenance, models for self-organizing storage (on devices, in data pools, and / or in the cloud), models for optimizing data transmission (e.g., for optimizing network coding, network condition-sensitive routing, etc.), models for optimizing data markets, and many other models.

[0192] In an embodiment, a platform with an AI model trained based on industry-specific feedback is provided. In an embodiment, various embodiments of the cognitive system disclosed herein can obtain input and feedback from industry-specific and domain-specific sources 116, such as those related to the optimization of specific machines, devices, components, processes, etc. Therefore, learning and adaptation of storage organization, network usage, combination of sensor and input data, data aggregation, data packaging, data pricing, and other features (e.g., for market 4102 or for other purposes of host processing system 112) can be configured by learning domain-specific feedback metrics for a given environment or application, such as applications involving IoT devices, such as industrial environments. This can include efficiency optimization (e.g., in electrical, electromechanical, magnetic, physical, thermodynamic, chemical, and other processes and systems), output optimization (e.g., for generating energy, materials, products, services, and other outputs), fault prediction, avoidance, and mitigation (e.g., in the above systems and processes), performance metric optimization (e.g., return on investment, benefit, profit, gross profit, revenue, etc.), cost reduction (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, etc.), benefit optimization (e.g., benefits related to safety, satisfaction, health), workflow optimization (e.g., optimizing time and resource allocation for processes), etc.

[0193] Disclosed herein is a method and system for a self-organizing group of industrial data collectors, including a self-organizing group of industrial data collectors, which is organized between industrial data collectors based on the capabilities and conditions of group members to optimize data collection. Each member in the group can be configured to have intelligence and the ability to coordinate with other members. For example, group members can track information about the data being processed by other members, so that data collection activities, data storage, data processing, and data publishing can be intelligently allocated throughout the group, taking into account environmental conditions, the capabilities of group members, operating parameters, rules (e.g., from a rule engine that manages group operations) and the current status of members. For example, among four collectors, a collector with a relatively low current power level (e.g., low battery) may be temporarily assigned the role of publishing data because it may receive a certain amount of power from a reader or interrogation device (e.g., an RFID reader) when it needs to publish data. A second collector with a good power level and strong processing power may be assigned more complex functions, such as processing data, fusing data, organizing the rest of the group (including self-organization under machine learning, so as to optimize the group over time, including by adjusting operating parameters, rules, etc. based on feedback), etc. A third collector in the swarm with significant storage capabilities may be assigned the task of collecting and storing a class of data, such as vibration sensor data, which consumes considerable bandwidth. A fourth collector in the swarm (e.g., one with lower storage capabilities) may be assigned the role of collecting data that is typically disposable, such as data about current diagnostic conditions, where only data about faults needs to be maintained and communicated. Swarm members may be connected in a peer-to-peer relationship, with one member acting as a "lead" or "hub," or by having them connected in a series or ring, where each member passes data (including commands) to the next member and understands the nature of the capabilities and commands appropriate to the previous and / or next member. Swarms may be used to distribute storage across the swarm (e.g., using the memory of each storage as an aggregate data store), such as to support a distributed ledger that can store transactional data, such as transactions involving data collected by the swarm, transactions generated in an industrial environment, and other data, such as data used to manage the swarm, the environment, or the machine or its components. The swarm can self-organize by setting up machine learning capabilities on one or more members of the swarm or based on instructions from an external machine learning facility that can optimize storage, data collection, data processing, data presentation, data transmission, and other functions based on managing parameters associated with each member.The machine learning facility may start with an initial configuration and change parameters of the swarm related to any of the above (including also changing the membership of the swarm), for example, iterating based on providing feedback to the machine learning facility regarding success metrics (e.g., utilization metrics, efficiency metrics, predicted or expected state success metrics, productivity metrics, revenue metrics, profit metrics, etc.) Over time, the swarm may be optimized to an advantageous configuration to achieve the desired success metrics of the owner, operator, or host of the industrial environment or its machines, components, or processes.

[0194] In an embodiment, a platform is provided that includes a self-organizing group of industrial data collectors. In an embodiment, a host processing system 112 having its processing architecture 4024 (and optionally including a collection with or including a cognitive data market 4102) can be integrated with, connected to, or use data of a self-organizing group 4202 of data collectors 102. In an embodiment, a self-organizing group 4202 can organize two or more data collection systems 102 by deploying cognitive features on one or more data collection systems 102, such as to provide collaboration of the group 4202. The group 4202 can be organized based on a hierarchical organization (e.g., where a master data collector 102 organizes and directs the activities of one or more slave data collectors 102), a collaborative organization (e.g., where the organization decisions of the group 4202 are distributed among the data collectors 102 (e.g., using various decision models, such as voting systems, point systems, least cost routing systems, priority systems, etc.), etc. In embodiments, one or more data collectors 102 may have mobile capabilities, such as where the data collector is disposed on or in a mobile robot, drone, mobile submersible, etc., so that the organization may include the location and positioning of the data collector 102. The data collection systems 102 may communicate with each other and with the host processing system 112, including sharing a collection of allocated storage space, which involves storage on one or more collectors or storage accessible by one or more collectors (in embodiments, the collection of allocated storage space may be considered unified storage space even if physically distributed, such as using virtualization capabilities). The organization may be automated based on one or more rules, models, conditions, processes, etc., such as embodied or executed by conditional logic, and the organization may be managed by policies, such as processed by a policy engine. Rules may be based on industry, application, and domain specific objects, classes, events, workflows, processes, and systems, such as by setting up a group 4202 to collect selected types of data at a specified location and time, such as in coordination with the foregoing. For example, group 4202 can assign data collectors 102 to collect diagnostic, sensor, instrumentation and / or telematics data in a serial manner from each machine in a series of machines performing an industrial process (e.g., a robotic manufacturing process), such as the time and location of inputs and outputs of each of those machines.In embodiments, self-organization may be cognitive, such as when the swarm changes one or more collected parameters over time and adapts the selection of parameters, weights applied to parameters, etc., such as in response to learning and feedback from learning feedback system 4012, etc., such as based on various feedback metrics that may be determined by applying analysis system 4018 (which in embodiments may reside on swarm 4202, host processing system 112, or a combination thereof) to data processed by swarm 4202 or other elements of the embodiments disclosed herein (including market elements, etc.). Thus, swarm 4202 may exhibit adaptive behavior, such as adapting to the current state 4020 or the expected state of its environment (considering market behavior), the behavior of various objects (e.g., IoT devices, machines, components, and systems), processes (including events, states, workflows, etc.), and other factors at a given time. Parameters may change during a process of change (e.g., neural networks, self-organizing maps, etc.), selection, promotion, etc. (e.g., parameters implemented through genetic programming or other artificial intelligence-based techniques). Parameters that may be managed, changed, selected, and adjusted through cognitive machine learning may include storage parameters (location, type, duration, number, structure, etc. on the swarm 4202), network parameters (e.g., how the swarm 4202 is organized, such as 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 network parameters), security parameters (e.g., settings for various security applications and services), location and positioning parameters (e.g., routing the movement of mobile data collectors 102 to locations, positioning and orienting collectors 102 relative to data collection points, relative to each other, and relative to network availability, etc.), input selection parameters (e.g., sensors for each collector 102 and the collection of data, 16, etc.), data combination parameters (e.g., parameters for sensor fusion, input combination, multiplexing, blending, layering, convolution, and other combinations), power parameters (e.g., parameters based on power levels and power availability of one or more collectors 102 or other objects, devices, etc.), states (including the expected state and condition of the swarm 4202, a single collection system 102, a host processing system 112, or one or more objects in the environment), events, etc. The feedback can be based on any type of feedback described herein so that the swarm can adapt its current and expected conditions over time to achieve various desired goals.

[0195] Disclosed herein are methods and systems for an Industrial Internet of Things distributed ledger, including a distributed ledger that supports tracking transactions performed on Industrial IoT data in an automated data marketplace. The distributed ledger can distribute storage across devices using a secure protocol, such as a protocol used for cryptocurrency, such as a blockchain used to support BitcoinTM currency.TM Protocol. A ledger or similar transaction record may include a structure in which each successive member of the chain stores data for previous transactions and a competition may be established to determine the "best" structure (e.g., the most complete structure) among alternative data storage data structures, which may be stored on a data collector, industrial machine or component, data pool, data market, cloud computing element, server, and / or enterprise's IT infrastructure (e.g., an owner, operator, or host of an industrial environment or system disclosed herein). The ledger or transactions may be optimized by machine learning to provide storage efficiency, security, redundancy, etc.

[0196] In an embodiment, the cognitive data marketplace 4102 may use a secure architecture to track and settle transactions, such as a distributed ledger 4004, where transactions in data packets are tracked in a chained distributed data structure (e.g., Blockchain™), allowing forensic analysis and verification, where each device stores a portion of the ledger representing the transactions in the data packet. The distributed ledger 4004 may be distributed to IoT devices, data pools 4020, data collection systems 102, etc., so that transaction information can be verified without relying on a single central information repository. The transaction system 4114 may be configured to store data in the distributed ledger 4004 and retrieve data from it (and from constituent devices) in order to settle transactions. Thus, a distributed ledger 4004 for processing data transactions, such as transactions for IoT data packets, is provided. In an embodiment, a self-organizing storage system 4028 may be used to optimize the storage of distributed ledger data, and to organize the storage of data (such as IoT data) packets that can be presented in the marketplace 4102.

[0197] Disclosed herein are methods and systems for self-organizing collectors, including self-organizing, multi-sensor data collectors that can optimize data collection, power, and / or revenue based on conditions in their environment. For example, a collector can organize data collection by turning on and off specific sensors, such as past utilization patterns or success metrics managed by an iterative configuration and a machine learning facility that tracks successful measurements. For example, a multi-sensor collector can learn to turn off certain sensors when power levels are low or turn them off during a period of time when data utilization from such sensors is low. Self-organization can also automatically organize how data is collected (which sensors, from what external source), how data is stored (at which granularity or compression level, duration, etc.), how data is presented (such as using a fusion or multiplexing structure, a byte-like structure, or using an intermediate statistical structure, such as addition, subtraction, division, multiplication, square root, normalization, scaling, or other operations), and the like. This can be improved over time from an initial configuration by training a self-organizing facility based on a data set from a real working environment, such as based on feedback metrics (including the various feedback types described throughout the present invention).

[0198] Disclosed herein are methods and systems for network-sensitive collectors, including network condition-sensitive, self-organizing, multi-sensor data collectors that can optimize based on bandwidth, quality of service, pricing, and / or other network conditions. Network sensitivity can include understanding of data transmission prices (e.g., allowing the system to receive or push data during off-peak hours or within the available parameters of a paid data plan), network quality (e.g., avoiding periods of potential errors), quality of environmental conditions (e.g., delaying transmission until signal quality is good, e.g., when the collector emerges from a shielded environment, avoiding wasting power during signal search when shielded by large metal structures, etc., typical of industrial environments), etc.

[0199] Disclosed herein are methods and systems for remotely organizing a universal data collector that can power on and off sensor interfaces based on needs and / or conditions identified in an industrial data collection environment. For example, an interface can identify available sensors and can open interfaces and / or processors to receive input from these sensors, including hardware interfaces that allow sensors to be plugged into a data collector, wireless data interfaces (e.g., a collector can ping a sensor, optionally providing some power via an interrogation signal), and software interfaces, such as for processing specific types of data. Thus, a collector that can process a variety of data can be configured to adapt to specific uses in a given environment. In an embodiment, configuration can be automatic or under machine learning, which can improve configuration by optimizing parameters based on feedback metrics that vary over time.

[0200] Disclosed herein are methods and systems for self-organizing storage for multi-sensor data collectors, including self-organizing storage for multi-sensor data collectors for industrial sensor data. Self-organizing storage can allocate storage based on the application of machine learning, which can improve storage configuration based on feedback measurements that change over time. Storage can be optimized by configuring the data type used (e.g., a byte-like structure, a structure representing fused data from multiple sensors, a structure representing statistics or metrics calculated by applying mathematical functions to data, etc.), by configuring compression, by configuring data storage duration, by configuring write strategies (e.g., by using a protocol in which one device stores instructions for other devices in the chain, etc., to split data on multiple storage devices), and by configuring storage hierarchies (e.g., by providing pre-computed intermediate statistics for faster access to frequently accessed data items). Thus, over time, a highly intelligent storage system can be configured and optimized based on feedback.

[0201] Disclosed herein are methods and systems for self-organizing network coding for multi-sensor data networks, including self-organizing network coding for data networks that transmit data from multiple sensors in an industrial data collection environment. Network coding, including random linear network coding, enables efficient and reliable transmission of large amounts of data over a variety of networks. Different network coding configurations can be selected based on machine learning to optimize network coding and other network transmission characteristics based on network conditions, environmental conditions, and other factors (e.g., the nature of the data being transmitted, environmental conditions, operating conditions, etc.) (including by training a network coding selection model over time based on feedback from a success metric, such as any of the metrics described herein).

[0202] In an embodiment, a platform with self-organizing network coding for multi-sensor data networks is provided. The cognitive system can change one or more parameters for networking, such as network type selection (e.g., selecting among available local, cellular, satellite, WiFi, Bluetooth, NFC, Zigbee, and other networks), network selection (e.g., selecting a specific network, such as a network known to have expected security features), network coding selection (e.g., selecting a network coding type for efficient transmission), network timing selection (e.g., configuring transmission based on network pricing conditions, traffic, etc.), network feature selection (e.g., selecting cognitive features, security features, etc.), network conditions (e.g., network quality based on current environment or operating conditions), network feature selection (e.g., enabling available authentication, licensing, and similar systems), network protocol selection (e.g., selecting between HTTP, IP, TCP / IP, cellular, satellite, serial, packet, streaming, and many other protocols), etc. Selecting the best network configuration can be very complex and depends on the specific situation, taking into account bandwidth limitations, price changes, sensitivity to environmental factors, security considerations, etc. The self-organizing network system 4030 can change the combination and arrangement of these parameters while obtaining input from the learning feedback system 4012 (such as using information from the analysis system 4018 regarding various result metrics (such as overall system metrics, analysis success metrics, and local performance indicators), information from various sensors and input sources 116, information about the status of the status system 4020 (including events, environmental conditions, working conditions, and others), or other information or obtaining other input. By changing and selecting alternative configurations of network parameters in different states, the self-organizing network system can find a configuration that is fully adapted to the environment monitored or controlled by the host system 112, such as an instance where one or more data collection systems 102 are located, and fully adapt to emerging network conditions. Therefore, a self-organizing, network condition adaptive data collection system is provided.

[0203] refer to Fig.14, the data collection system 102 may have one or more output interfaces and / or ports 4010. These interfaces and / or ports may include network ports and connections, application programming interfaces, and the like. A method and system for a tactile or multi-sensory user interface is disclosed herein, including a wearable tactile or multi-sensory user interface for an industrial sensor data collector and having vibration, thermal, electrical, and / or sound output. For example, based on a data structure configured to support the interface, the interface may be configured to provide input or feedback to a user, such as based on data from a sensor in the environment. For example, if a fault condition based on vibration data is detected (e.g., a fault condition caused by bearing wear, shaft misalignment, or a resonance condition between machines), it may be presented in the tactile interface by vibration of the interface, such as by shaking a wrist-worn device. Similarly, thermal data indicating overheating may be presented by heating or cooling a wearable device, for example, when a worker is working on a machine and may not necessarily be able to view the user interface. Similarly, electrical or magnetic data may be presented by a humming sound, etc., to indicate the presence of an open electrical connection or wire, etc. That is, multi-sensory interfaces can intuitively help users, such as users with wearable devices getting a quick indication of what is happening in the environment, and wearable interfaces have various interaction modes that do not require the user to focus on the graphical UI, which can be very difficult or impossible in many industrial environments where the user needs to focus on the environment.

[0204] In an embodiment, a platform is provided having a wearable haptic user interface for an industrial sensor data collector having a vibration, thermal, electrical, and / or acoustic output. In an embodiment, a haptic user interface 4302 is provided as an output of a data collection system 102, such as a system for processing information of the vibration, thermal, electrical, and / or acoustic output and providing it to one or more components of the data collection system 102 or other systems, such as a wearable device, a mobile phone, etc. The data collection system 102 may be provided in a form factor suitable for delivering tactile input to a user, such as vibration, heating or cooling, buzzing, etc., such as arranged in a headgear, armband, wristband or watch, belt, clothing, uniform, etc. In this case, the data collection system 102 may be integrated with equipment, uniforms, equipment, etc. worn by a user (e.g., an individual responsible for operating or monitoring an industrial environment). In an embodiment, signals from various sensors or input sources (or selective combinations, permutations, blends, etc. managed by one or more of the cognitive input selection systems 4004, 4014) may trigger tactile feedback. For example, if a nearby industrial machine is overheating, the tactile interface can warn the user by heating up or by sending a signal to another device (e.g., a mobile phone) to heat it up. If the system is experiencing abnormal vibrations, the tactile interface may vibrate. Thus, through various forms of tactile input, the data collection system 102 can notify the user that attention needs to be paid to one or more devices, machines, or other factors, such as factors in an industrial environment, without requiring the user to read the message or divert their visual attention from the task at hand. The tactile interface and the output selection that should be provided can be considered in the cognitive input selection system 4004, 4014. For example, user behavior (e.g., response to input) can be monitored and analyzed in the analysis system 4018, and feedback can be provided through the learning feedback system 4012 so that signals can be provided at the right time and in the right way based on the correct collection or packaging of sensors and inputs to optimize the effectiveness of the tactile system 4202. This can include rule-based or model-based feedback (e.g., providing output that corresponds to the source data being transmitted in some logical way). In embodiments, a cognitive haptic system may be provided in which the selection of inputs or triggers for haptic feedback, the selection of outputs, timing, intensity levels, durations, and other parameters (or weights applied thereto) may be varied in a process of variation, upgrading, and selection (e.g., using genetic programming), wherein real-world feedback responds to feedback in actual situations or feedback based on simulations and test results of user behavior. Thus, an adaptive haptic interface for a data collection system 102 is provided that can learn and adjust feedback to meet relevant requirements and optimize the impact on user behavior, e.g., for overall system results, data collection results, analysis results, etc.

[0205] Disclosed herein are methods and systems for an AR / VR industrial eyewear presentation layer, wherein heat map elements are presented based on patterns and / or parameters in the collected data. Disclosed herein are methods and systems for condition-sensitive, self-organizing adjustment of an AR / VR interface based on feedback metrics and / or training in an industrial environment. In an embodiment, any data, metrics, etc. described throughout the present invention may be presented by visual elements, overlays, etc., to be presented in an AR / VR interface, for example, in industrial eyewear, on an AR / VR interface on a smartphone or tablet, on an AR / VR interface on a data collector (which may be embodied in a smartphone or tablet), on a display on a machine or component, and / or on a display in an industrial environment.

[0206] In an embodiment, a platform with a heat map displaying AR / VR collected data is provided. In an embodiment, a platform with a heat map 4204 is provided, and the heat map 4204 displays the data collected from the data collection system 102 for providing input to the AR / VR interface 4208. In an embodiment, a heat map interface 4304 is provided as an output of the data collection system 102, for example, for processing various sensor data and other data (e.g., map data, simulated sensor data, and other data) and providing it to one or more components of the data collection system 102 or other systems, such as mobile devices, tablets, dashboards, computers, AR / VR devices, etc. The data collection system 102 can be provided in a form factor suitable for conveying visual input to a user, such as presenting a map that includes indicators of levels of analog and digital sensor data, such as data indicating levels of rotation, vibration, heating or cooling, pressure, and many other conditions. In this case, the data collection system 102 can be integrated with a device used by a person responsible for operating or monitoring an industrial environment, etc. In an embodiment, signals from various sensors or input sources (or selective combinations, permutations, blends, etc. managed by one or more of the cognitive input selection systems 4004, 4014) can provide input data to the heat map. Coordinates can include real-world location coordinates (e.g., geographic locations or locations on an environmental map) as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow analog sensor signals, digital signals, input source information, and various combinations to be represented in map-based visualizations, so that colors can represent different input levels along relevant dimensions. For example, if a nearby industrial machine is overheating, the heat map interface can alert the user by displaying the machine in bright red. If the system is experiencing abnormal vibrations, the heat map interface may display different colors for machine visual elements, or may cause icons or display elements representing the machine to vibrate in the interface, thereby drawing attention to the element. Clicking, touching, or otherwise interacting with the map can allow the user to gain insight into the underlying sensor or input data used as input to the heat map display. Thus, through various forms of display, the data collection system 102 can notify users that one or more devices, machines, or other factors, such as factors in an industrial environment, need to be paid attention to without requiring them to read text-based messages or inputs. The heat map interface and the output selections that should be provided can be considered in the cognitive input selection system 4004, 4014. For example, user behavior (e.g., responses to inputs or displays) can be monitored and analyzed in the analysis system 4018, and feedback can be provided through the learning feedback system 4012 so that signals can be provided at the right time and in the right manner based on the correct collection or packaging of sensors and inputs to optimize the effectiveness of the heat map UI 4304.This may include rule-based or model-based feedback (e.g., providing outputs that correspond in some logical way to the source data being transmitted). In embodiments, a cognitive heat map system may be provided in which selection of inputs or triggers for heat map displays, selection of outputs, colors, visual representation elements, timing, intensity levels, durations, and other parameters (or weights applied thereto) may be varied in a process of variation, upgrading, and selection (e.g., using genetic programming) based on real-world feedback in response to feedback in actual situations or based on feedback from simulations and test results of user behavior. Thus, an adaptive heat map interface for data collection system 102 or data collected thereby or data processed by host processing system 112 is provided that can learn and adjust feedback to meet relevant requirements and optimize the impact on user behavior and reactions, e.g., for overall system results, data collection results, analysis results, etc.

[0207] In an embodiment, a platform is provided having an automatically adjusting AR / VR visualization of data collected by a data collector. In an embodiment, a platform is provided having an automatically adjusting AR / VR visualization system 4308 for visualizing data collected by a data collection system 102, for example, where the data collection system 102 has an AR / VR interface 4208, or where input is provided to an AR / VR interface 4308 (e.g., a mobile phone in virtual reality, or an AR headset, AR glasses, etc.). In an embodiment, an AR / VR system 4308 is provided as an output interface for the data collection system 102, such as a system for processing and providing visualization of various sensor data and other data (e.g., map data, simulated sensor data, and other data) to one or more components of the data collection system 102 or other systems, such as a mobile device, tablet, dashboard, computer, AR / VR device, etc. The data collection system 102 may be provided in a form factor suitable for delivering AR or VR visual, auditory, or other sensory input to a user, such as by presenting one or more displays, such as 3D reality visualizations, objects, maps, camera overlays or other overlay elements, maps, etc., that include or correspond to indicators of levels of analog and digital sensor data, such as data indicating levels of rotation, vibration, heating or cooling, pressure, or other conditions, relative to an input source 116, etc. In this case, the data collection system 102 may be integrated with a device used by an individual responsible for operating or monitoring an industrial environment, etc.

[0208] In an embodiment, signals from various sensors or input sources (or selective combinations, arrangements, mixes, etc. managed by one or more of the cognitive input selection systems 4004, 4014) can provide input data to populate, configure, modify, or otherwise determine AR / VR elements. Visual elements may include various icons, map elements, menu elements, sliders, triggers, colors, shapes, sizes, etc., for representing analog sensor signals, digital signals, input source information, and various combinations. For example, the color, shape, and size of the visual overlay element may represent different input levels along the relevant dimensions of the sensor or sensor combination. For example, if a nearby industrial machine overheats, the AR element may warn the user by displaying an icon representing that type of machine in a flashing red in a portion of a pair of AR glasses display. If the system is experiencing abnormal vibration, a virtual reality interface that displays a visualization of machine components (e.g., a camera view of the machine overlaid with a 3D visualization element) may display the vibrating component in a highlighted color and motion, etc., to ensure that the component is highlighted in a virtual reality environment that is used to help the user monitor or maintain the machine. Clicking, touching, moving eyes toward, or otherwise interacting with visual elements in an AR / VR interface may allow the user to gain insight into the underlying sensor or input data used as input to the display. Thus, through various forms of display, the data collection system 102 may notify the user that attention is required for one or more devices, machines, or other factors, such as in an industrial environment, without requiring the user to read text-based messages or inputs or to divert attention from the applicable environment (whether a real environment with AR features or a virtual environment for simulation, training, etc.).

[0209] The selection and configuration of the AR / VR output interface 4208 and the output or display that should be provided can be processed in the cognitive input selection system 4004, 4014. For example, user behavior (e.g., response to input or display) can be monitored and analyzed in the analysis system 4018, and feedback can be provided through the learning feedback system 4012, so that AR / VR display signals can be provided at the right time and in the right way based on the correct collection or packaging of sensors and inputs to optimize the effectiveness of the AR / VR UI4308. This can include rule-based or model-based feedback (e.g., providing output that corresponds to the source data being transmitted in some logical way). In an embodiment, a cognitive adjustment AR / VR interface control system 4308 can be provided, in which the selection of inputs or triggers of AR / VR display elements, the selection of outputs (e.g., color, visual representation elements, timing, intensity level, duration, and other parameters (or weights applied to them)), and other parameters of the VR / AR environment can be changed in a process of change, upgrade, and selection (e.g., using genetic programming), where feedback based on real-world feedback responds to feedback in actual situations or feedback based on simulations and test results of user behavior. Thus, an adaptively adjustable AR / VR interface for the data collection system 102 or data collected thereby or data processed by the host processing system 112 is provided, which can learn and adjust feedback to meet relevant requirements and optimize the impact on user behavior and reactions, such as for overall system results, data collection results, analysis results, etc.

[0210] As described above, methods and systems for continuous ultrasonic monitoring are disclosed herein, including providing continuous ultrasonic monitoring of rotating elements and bearings of energy production facilities. Embodiments include using continuous ultrasonic monitoring of industrial environments as a source of cloud-deployed pattern recognizers. Embodiments include using continuous ultrasonic monitoring to provide updated state information to a state machine, which is used as an input to a cloud-deployed pattern recognizer. Embodiments include providing continuous ultrasonic monitoring information to users based on policies declared in a policy engine. Embodiments include storing continuous ultrasonic monitoring data in a fusion data structure on an industrial sensor device along with other data. Embodiments include making continuous ultrasonic monitoring data streams from industrial environments available as services in a data market. Embodiments include feeding continuous ultrasonic data streams into a self-organizing data pool. Embodiments include training a machine learning model to monitor continuous ultrasonic monitoring data streams, wherein the model is based on a training set created from manual analysis of such data streams and is improved based on performance data collected in an industrial environment. Embodiments include a data collector group, including at least one data collector for continuous ultrasonic monitoring of an industrial environment and at least one other type of data collector. Embodiments include using a distributed ledger to store time series data from continuous ultrasonic monitoring across multiple devices. Embodiments include collecting continuous ultrasonic monitoring data streams in a self-organizing data collector. Embodiments include collecting continuous ultrasonic monitoring data streams in a network-sensitive data collector.

[0211] Embodiments include collecting continuous ultrasonic monitoring data streams in a remotely organized data collector. Embodiments include collecting continuous ultrasonic monitoring data streams in a data collector with self-organizing storage. Embodiments include transmitting ultrasonic data streams collected from an industrial environment using self-organizing network coding. Embodiments include transmitting parameter indicators of continuously collected ultrasonic data streams via a sensor interface of a wearable device. Embodiments include transmitting parameter indicators of continuously collected ultrasonic data streams via a heat map visual interface of a wearable device. Embodiments include transmitting parameter indicators of continuously collected ultrasonic data streams via an interface that operates through self-organizing regulation of an interface layer.

[0212] As described above, the present invention discloses a method and system for cloud-based machine pattern recognition based on remote simulated industrial sensor fusion. Embodiments include obtaining input from multiple simulated sensors arranged in an industrial environment, multiplexing the sensors into multiplexed data streams, feeding the data streams into a cloud-deployed machine learning facility, and training the model of the machine learning facility to recognize defined patterns associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer for input states from a state machine that characterizes the state of the industrial environment. Embodiments include deploying policies through a policy engine that manages which users can use which data and for what purpose in cloud-based machine learning. Embodiments include feeding input from multiple devices stored on a device with a fusion of multiple sensor streams into a cloud-based pattern recognizer. Embodiments include making the output from a cloud-based machine pattern recognizer available as a data service in a data market, which analyzes fused data from remote simulated industrial sensors. Embodiments include using a cloud-based platform to identify data patterns in multiple data pools containing data published from industrial sensors. Embodiments include training a model to identify a preferred set of sensors to diagnose a condition of an industrial environment, wherein a training set is created by a human user, and the model is improved based on collected data feedback regarding the condition of the industrial environment.

[0213] Embodiments include swarms of data collectors controlled by policies that automatically propagate through the swarm. Embodiments include storing sensor fusion information on multiple devices using a distributed ledger. Embodiments include feeding input from a set of self-organizing data collectors to a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment. Embodiments include feeding input from a set of network-sensitive data collectors to a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment. Embodiments include feeding input from a set of remotely organized data collectors to a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment.

[0214] Embodiments include feeding inputs from a set of data collectors with self-organizing storage to a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment. Embodiments include a data collection system in an industrial environment with self-organizing network coding for data transmission of fused data from multiple sensors in the environment. Embodiments include transmitting information formed by fusing inputs from multiple sensors in an industrial data collection system in a multi-sensor interface. Embodiments include transmitting information formed by fusing inputs from multiple sensors in an industrial data collection system in a heat map interface. Embodiments include transmitting information formed by fusing inputs from multiple sensors in an industrial data collection system in an interface that operates with self-organizing adjustments at the interface layer.

[0215] As described above, the present invention discloses a method and system for performing cloud-based machine pattern analysis of state information from multiple simulated industrial sensors to provide expected state information of an industrial system. Embodiments include providing cloud-based pattern analysis of state information from multiple simulated industrial sensors to provide expected state information of an industrial system. Embodiments include using a policy engine to determine state information that can be used for cloud-based machine analysis. Embodiments include feeding input from multiple devices stored on a device with a fusion of multiple sensor streams into a cloud-based pattern recognizer to determine the expected state of an industrial environment. Embodiments include making the expected state information from a cloud-based machine pattern recognizer available as a data service in a data market, the cloud-based machine pattern recognizer analyzing fused data from remote simulated industrial sensors. Embodiments include using a cloud-based pattern recognizer to determine the expected state of an industrial environment based on data collected from a data pool containing information streams from machines in the environment. Embodiments include training a model to identify preferred state information to diagnose the condition of an industrial environment, wherein a training set is created by a human user, and the model is improved based on data feedback collected about the condition of the industrial environment. Embodiments include a group of data collectors that feed a state machine that maintains current state information of an industrial environment. Embodiments include using a distributed ledger to store historical state information of fused sensor states, and a self-organizing data collector feeds a state machine that maintains current state information of an industrial environment. Embodiments include a network-sensitive data collector that feeds a state machine that maintains current state information of an industrial environment. Embodiments include a remotely organized data collector that feeds a state machine that maintains current state information of an industrial environment. Embodiments include a data collector with self-organizing storage that feeds a state machine that maintains current state information of an industrial environment. Embodiments include a data collection system in an industrial environment that has self-organizing network coding for data transmission and maintains expected state information of the environment. Embodiments include transmitting expected state information in an industrial data collection system determined by machine learning in a multi-sensor interface. Embodiments include transmitting expected state information determined by machine learning in an industrial data collection system in a heat map interface. Embodiments include transmitting expected state information determined by machine learning in an industrial data collection system in an interface that operates through self-organizing regulation of an interface layer.

[0216] As described above, the present invention discloses a method and system for a cloud-based policy automation engine for the Internet of Things, wherein the creation, deployment and management of Internet of Things devices (including a cloud-based policy automation engine for the Internet of Things) enables the creation, deployment and management of policies applicable to Internet of Things devices. Embodiments include deploying policies regarding data usage to an on-device storage system storing fused data from multiple industrial sensors. Embodiments include deploying policies regarding who can provide what data in a self-organizing market for IoT sensor data. Embodiments include deploying policies across a set of self-organizing data pools that contain data streamed from industrial sensor devices in order to manage the use of data in the data pools. Embodiments include training models to determine policies that should be deployed in industrial data collection systems. Embodiments include deploying policies that manage how self-organizing groups should be organized for specific industrial environments. Embodiments include storing policies on devices that manage the use of storage functions of the device for distributed ledgers. Embodiments include deploying policies that manage how self-organizing data collectors should be organized for specific industrial environments. Embodiments include deploying policies that manage how network bandwidth should be used by network-sensitive data collectors for specific industrial environments. Embodiments include deploying policies that govern how a remotely organized data collector should collect data about a specified industrial environment and provide available data. Embodiments include deploying policies that govern how a data collector should self-organize storage of a specific industrial environment. Embodiments include a data collection system in an industrial environment, the system having a policy engine for deploying policies within the system and self-organizing network coding for data transmission. Embodiments include a data collection system in an industrial environment, the system having a policy engine for deploying policies within the system, wherein a policy is applied to how data will be presented in a multi-sensor interface. Embodiments include a data collection system in an industrial environment, the system having a policy engine for deploying policies within the system, wherein a policy is applied to how data will be presented in a heat map visual interface. Embodiments include a data collection system in an industrial environment, the system having a policy engine for deploying policies within the system, wherein a policy is applied to how data will be presented in a heat map visual interface. Embodiments include a data collection system in an industrial environment, the system having a policy engine for deploying policies within the system, wherein policies are applicable to how data is presented in an interface that operates with self-organizing regulation of the interface layer.

[0217] As described above, methods and systems for on-device sensor fusion and data storage for industrial IoT devices are disclosed herein, including on-device sensor fusion and data storage for industrial IoT devices, wherein data from multiple sensors are multiplexed in a device for storing fused data streams. Embodiments include a self-organizing marketplace for presenting fused sensor data extracted from on-device storage of IoT devices. Embodiments include streaming fused sensor information from multiple industrial sensors and from on-device data storage facilities to a data pool. Embodiments include training models to determine data that should be stored on devices in a data collection environment. Embodiments include a group of self-organizing industrial data collectors that are organized with each other to optimize data collection, wherein at least some data collectors have on-device storage of fused data from multiple sensors. Embodiments include storing distributed ledger information together with fused sensor information on industrial IoT devices. Embodiments include on-device sensor fusion and data storage for self-organizing industrial data collectors. Embodiments include on-device sensor fusion and data storage for network-sensitive industrial data collectors. Embodiments include on-device sensor fusion and data storage for remotely organized industrial data collectors. Embodiments include on-device sensor fusion and self-organizing data storage for industrial data collectors. Embodiments include a data collection system in an industrial environment having on-device sensor fusion and self-organizing network coding for data transmission. Embodiments include a data collection system with on-device sensor fusion for industrial sensor data, wherein data structures are stored to support an alternative multi-sensor presentation mode. Embodiments include a data collection system with on-device sensor fusion for industrial sensor data, wherein data structures are stored to support a visual heat map presentation mode. Embodiments include a data collection system with on-device sensor fusion for industrial sensor data, wherein data structures are stored to support an interface that self-organizes and regulates operations through an interface layer.

[0218] As described above, methods and systems for a self-organizing data market for industrial IoT data are disclosed herein, including a self-organizing data market for industrial IoT data, wherein available data elements are organized in the market for consumption by consumers based on training a self-organizing facility using a training set and feedback from market success metrics. Embodiments include organizing a set of data pools in the self-organizing data market based on utilization metrics of the data pools. Embodiments include training models to determine pricing for data in the data market. Embodiments include feeding a data market with a data stream from a self-organizing group of industrial data collectors. Embodiments include using a distributed ledger to store transaction data for a self-organizing market for industrial IoT data. Embodiments include feeding a data market with a data stream from a self-organizing industrial data collector. Embodiments include feeding a data market with a data stream from a set of network-sensitive industrial data collectors. Embodiments include feeding a data market with a data stream from a set of remotely organized industrial data collectors. Embodiments include feeding a data market with a data stream from a set of industrial data collectors with self-organizing storage. Embodiments include transmitting sensor data collected in an industrial environment to a market using self-organizing network coding. Embodiments include providing a library of data structures suitable for presenting data in an alternative multi-sensor interface mode in a data marketplace. Embodiments include providing a library of data structures suitable for presenting data in a heat map visualization in a data marketplace. Embodiments include providing a library of data structures suitable for presenting data in an interface that operates through self-organizing regulation of an interface layer in a data marketplace.

[0219] As described above, methods and systems for self-organizing data pools are disclosed herein, including self-organization of data pools based on utilization and / or revenue metrics, including utilization and / or revenue metrics tracked for multiple data pools. Embodiments include training models to present the most valuable data in a data market, wherein the training is based on industry-specific success metrics. Embodiments include populating a set of self-organizing data pools with data from a self-organizing group of data collectors. Embodiments include using a distributed ledger to store transaction information for data deployed in a data pool, wherein the distributed ledger is distributed in the data pool. Embodiments include self-organizing data pools based on utilization and / or revenue metrics tracked for multiple data pools, wherein these data pools contain data from self-organizing data collectors. Embodiments include populating a set of self-organizing data pools with data from a set of network-sensitive data collectors. Embodiments include populating a set of self-organizing data pools with data from a set of remotely organized data collectors. Embodiments include populating a set of self-organizing data pools with data from a set of data collectors with self-organizing storage. Embodiments include a data collection system in an industrial environment, the system having a self-organizing pool for data storage and a self-organizing network coding for data transmission. Embodiments include a data collection system in an industrial environment having self-organizing pools for data storage, the self-organizing pools including source data structures for supporting data presentation in a multi-sensor interface. Embodiments include a data collection system in an industrial environment having self-organizing pools for data storage, the self-organizing pools including source data structures for supporting data presentation in a heat map interface. Embodiments include a data collection system in an industrial environment having self-organizing pools for data storage, the self-organizing pools including source data structures for supporting data presentation in an interface that operates with self-organizing adjustments of an interface layer.

[0220] As described above, disclosed herein are methods and systems for training an AI model based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, benefit, or impact, wherein the AI ​​model operates on sensor data from an industrial environment. Embodiments include training a group of data collectors based on industry-specific feedback measures. Embodiments include training an AI model to identify and use available storage locations in an industrial environment for storing distributed ledger information. Embodiments include training a group of self-organizing data collectors based on industry-specific feedback measures. Embodiments include training a network-sensitive data collector based on network and industrial conditions in an industrial environment. Embodiments include training a remote manager for a remotely organized data collector based on industry-specific feedback measures. Embodiments include training a self-organizing data collector to configure storage based on industry-specific feedback. Embodiments include a data collection system in an industrial environment having cloud-based training of a network coding model used for organizing data transmission. Embodiments include a data collection system in an industrial environment that performs cloud-based training on a facility that manages presentation of data in a multi-sensor interface. Embodiments include a data collection system in an industrial environment that performs cloud-based training on a facility that manages presentation of data in a heat map interface. Embodiments include a data collection system in an industrial environment that provides cloud-based training for a facility that manages presentation of data in an interface that operates through self-organizing regulation of an interface layer.

[0221] As described above, disclosed herein are methods and systems for self-organizing groups of industrial data collectors, including self-organizing groups of industrial data collectors that organize among industrial data collectors based on the capabilities and conditions of group members to optimize data collection. Embodiments include deploying a distributed ledger data structure in a data group. Embodiments include self-organizing groups of self-organizing data collectors for data collection in an industrial environment. Embodiments include self-organizing groups of network-sensitive data collectors for data collection in an industrial environment. Embodiments include self-organizing groups of network-sensitive data collectors for data collection in an industrial environment, wherein the self-organizing pool is also used for remote organization. Embodiments include self-organizing groups of data collectors with self-organizing storage for data collection in an industrial environment. Embodiments include a data collection system in an industrial environment, the system having a self-organizing group of data collectors and self-organizing network coding for data transmission. Embodiments include a data collection system in an industrial environment, the system having a self-organizing group of data collectors that forwards information for use in a multi-sensor interface. Embodiments include a data collection system in an industrial environment, the system having a self-organizing group of data collectors that forwards information for use in a heat map interface. Embodiments include a data collection system in an industrial environment having an ad hoc group of data collectors that forward information for use in an interface that operates through ad hoc regulation of an interface layer.

[0222] As described above, methods and systems for industrial IoT distributed ledgers are disclosed herein, including a distributed ledger that supports tracking transactions performed for industrial IoT data in an automated data market. Embodiments include a self-organizing data collector configured to distribute the collected information to a distributed ledger. Embodiments include a network-sensitive data collector configured to distribute the collected information to a distributed ledger based on network conditions. Embodiments include a remotely organized data collector configured to distribute the collected information to a distributed ledger based on intelligent remote management of the distribution. Embodiments include a data collector with self-organizing local storage, which is configured to distribute the collected information to a distributed ledger. Embodiments include a data collection system in an industrial environment, the system using a distributed ledger for data storage and a self-organizing network coding for data transmission. Embodiments include a data collection system in an industrial environment, the system using a distributed ledger to data store data structures supporting a tactile interface for data presentation. Embodiments include a data collection system in an industrial environment, the system using a distributed ledger to data store data structures supporting a heat map interface for data presentation. Embodiments include a data collection system for an industrial environment that uses a distributed ledger to store data in a data structure that supports an interface that operates through self-organizing regulation of an interface layer.

[0223] As described above, disclosed herein are methods and systems for self-organizing collectors, including self-organizing, multi-sensor data collectors that can optimize data collection, power, and / or revenue based on conditions in their environment. Embodiments include self-organizing data collectors that organize based at least in part on network conditions. Embodiments include self-organizing data collectors that are also responsive to remote organization. Embodiments include self-organizing data collectors with self-organizing storage for collected data in an industrial data collection environment. Embodiments include data collection systems in industrial environments, the systems having self-organizing data collection and self-organizing network coding for data transmission. Embodiments include a data collection system in an industrial environment, the system having a self-organizing data collector that feeds a data structure supporting a tactile or multi-sensory wearable interface for data presentation. Embodiments include a data collection system in an industrial environment, the system having a self-organizing data collector that feeds a data structure supporting a heat map interface for data presentation. Embodiments include a data collection system in an industrial environment, the system having a self-organizing data collector that feeds a data structure supporting an interface for data presentation that operates through self-organizing regulation of an interface layer.

[0224] As described above, methods and systems for network-sensitive collectors are disclosed herein, including network condition-sensitive, self-organizing multi-sensor data collectors that can be optimized based on bandwidth, quality of service, price, and / or other network conditions. Embodiments include a remotely organized, network condition-sensitive universal data collector that can power on and off sensor interfaces based on needs and / or conditions (including network conditions) identified in an industrial data collection environment. Embodiments include a network condition-sensitive data collector having self-organizing storage for data collected in an industrial data collection environment. Embodiments include a network condition-sensitive data collector having self-organizing network coding for data transmission in an industrial data collection environment. Embodiments include a data collection system in an industrial environment having a network-sensitive data collector that forwards data structures supporting a tactile wearable interface for data presentation. Embodiments include a data collection system in an industrial environment having a network-sensitive data collector that forwards data structures supporting a heat map interface for data presentation. Embodiments include a data collection system in an industrial environment having a network-sensitive data collector that transmits data structures supporting an interface for self-organizing regulation of operations through an interface layer.

[0225] As described above, disclosed herein are methods and systems for a remotely organized universal data collector that can power on and off sensor interfaces based on needs and / or conditions identified in an industrial data collection environment. Embodiments include a remotely organized universal data collector having self-organizing storage for data collected in an industrial data collection environment. Embodiments include a data collection system in an industrial environment having data collection remote control and self-organizing network coding for data transmission. Embodiments include a remotely organized data collector for storing sensor data and transmitting instructions for using the data in a tactile or multi-sensory wearable interface. Embodiments include a remotely organized data collector for storing sensor data and transmitting instructions for using the data in a heat map visual interface. Embodiments include a remotely organized data collector for storing sensor data and transmitting instructions for using the data in an interface that operates with self-organizing regulation of the interface layer.

[0226] As described above, disclosed herein are methods and systems for self-organizing storage of multi-sensor data collectors, including self-organizing storage of multi-sensor data collectors for industrial sensor data. Embodiments include a data collection system in an industrial environment having self-organizing data storage and self-organizing network coding for data transmission. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for converting the data for use in a tactile wearable interface. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for converting the data for use in a heat map presentation interface. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for converting the data for use in an interface that operates in or with self-organizing regulation of an interface layer.

[0227] As described above, disclosed herein are methods and systems for self-organizing network coding for multi-sensor data networks, including self-organizing network coding for data networks for transmitting multiple sensor data in an industrial data collection environment. Embodiments include a data collection system in an industrial environment, the system having self-organizing network coding for data transmission and a data structure supporting a tactile wearable interface for data presentation. Embodiments include a data collection system in an industrial environment, the system having self-organizing network coding for data transmission and a data structure supporting a heat map interface for data presentation. Embodiments include a data collection system in an industrial environment, the system having self-organizing network coding for data transmission and a self-organizing regulation of an interface layer for data presentation.

[0228] As described above, disclosed herein are methods and systems for tactile or multi-sensory user interfaces, including wearable tactile or multi-sensory user interfaces for industrial sensor data collectors having vibration, thermal, electrical, and / or sound outputs. Embodiments include wearable tactile user interfaces for transmitting industrial status information from a data collector having vibration, thermal, electrical, and / or sound outputs. Embodiments include wearable tactile user interfaces for transmitting industrial status information from a data collector having vibration, thermal, electrical, and / or sound outputs, wherein the wearable interface also has a visual presentation layer for presenting a heat map indicating data parameters. Embodiments include condition-sensitive self-organizing regulation of AR / VR interfaces and multi-sensory interfaces based on feedback metrics and / or training in industrial environments.

[0229] As described above, disclosed herein are methods and systems for an AR / VR industrial eyewear presentation layer, wherein heat map elements are presented based on patterns and / or parameters in the collected data. Embodiments include condition-sensitive self-organizing adjustments to heat map AR / VR interfaces based on feedback metrics and / or training in industrial environments. As described above, disclosed herein are methods and systems for condition-sensitive self-organizing adjustments to AR / VR interfaces based on feedback metrics and / or training in industrial environments.

[0230] The following illustrative clauses describe certain embodiments of the present invention. The data collection system mentioned in the following invention can be a local data collection system 102, a host processing system 112 (for example, using a cloud platform), or a combination of a local system and a host system. In an embodiment, a data collection system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has IP front-end signal conditioning on a multiplexer to improve the signal-to-noise ratio. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a multiplexer to continuously monitor alarm features. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses a distributed CPLD chip with a dedicated bus to perform logical control of multiple MUXs and data acquisition parts. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has high current input capabilities using solid-state relays and design topologies. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has power-off capability for at least one analog sensor channel and component boards. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has unique electrostatic protection for trigger and vibration inputs. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a precise voltage reference for an A / D zero reference.

[0231] In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a phase-locked loop bandpass tracking filter for acquiring slow RPMs and phase information. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses an onboard timer to digitally derive the phase relative to the input and trigger channels. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a peak detector for automatic scaling that is routed to a separate analog-to-digital converter for peak detection. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and routes raw or buffered trigger channels to other analog channels. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses higher input oversampling for the oversampled A / D to obtain a lower sample rate output to minimize AA filter requirements. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses a CPLD as a clock divider for an oversampling analog-to-digital converter to achieve lower sampling rates without the need for digital resampling.

[0232] In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has long data blocks acquired at a high sampling rate, rather than multiple sets of data collected at different sampling rates. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and stores calibration data with an onboard card group maintenance history. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has fast route creation capabilities using hierarchical templates. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has intelligent management of data collection bands. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a neural network expert system that utilizes intelligent management of data collection bands.

[0233] In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses a database hierarchy in sensor data analysis. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has an expert system GUI graphical method for defining expert system smart data collection bands and diagnostics. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a graphical method for back calculation definition. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and proposes a bearing analysis method. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses transient signal analysis for torsional vibration detection / analysis. In an embodiment, a data collection and processing system is provided that uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and uses analog and digital methods to improve integration.

[0234] In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has an adaptive scheduling technology for continuously monitoring analog data in a local environment. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a data collection resident feature. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has a self-contained data acquisition box. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has an SD card memory. In an embodiment, a data collection and processing system is provided, which uses an analog crosspoint switch to collect data with a variable set of analog sensor inputs and has an extended onboard statistical capability for continuous monitoring. In an embodiment, a data collection and processing system is provided, which us...

Claims

1. A method for monitoring a machine having at least one shaft supported by a set of bearings, the method comprising: monitoring a first data channel assigned to a single-axis sensor located at a constant position associated with the machine; monitoring second, third and fourth data channels respectively assigned to axes of the three-axis sensor; simultaneously recording gapless digital waveform data from all of the data channels while the machine is running; as well as determining a change in relative phase based on the digital waveform data; Also included is a simultaneous monitoring reference channel assigned to a constant reference position associated with a set of monitored machines. 2 . The method of claim 1 , wherein the triaxial sensor is positioned at a plurality of locations associated with the machine while acquiring the digital waveform data.

3. The method of claim 1, wherein the second, third and fourth channels are assigned together to a series of three-axis sensors respectively located at different locations associated with the machine. The method of claim 3 , wherein data is received from all sensors simultaneously.

5. The method of claim 1, further comprising determining an operational deflection shape based on the change in relative phase and the waveform data. The method of claim 1 , wherein the constant position is a position associated with an axis of the machine.

7. The method of claim 3, wherein the three-axis sensors in the series of three-axis sensors are respectively located at different locations and are respectively associated with different bearings in the machine.

8. The method of claim 3, wherein the constant position is a position associated with the shaft of the machine, and wherein three-axis sensors in the series of three-axis sensors are respectively located at different positions and are respectively associated with different bearings supporting the shaft in the machine.

9. The method according to claim 1, further comprising: monitoring the first data channel assigned to the single-axis sensor at a constant position on a second machine; monitoring the second, the third, and the fourth data channels respectively assigned to axes of a three-axis sensor located at a location associated with the second machine; as well as Gapless digital waveform data from all data channels of the second machine are simultaneously recorded while both machines are running.

10. The method of claim 9, wherein the method further comprises characterizing the contribution from each machine in the simultaneously recorded gapless digital waveform data from the second machine.

11. A method of monitoring a machine having at least one shaft supported by a set of bearings, the method comprising: monitoring a first data channel assigned to a single-axis sensor located at a constant position associated with the machine; monitoring second, third and fourth data channels respectively assigned to axes of the three-axis sensor; simultaneously recording gapless digital waveform data from all of the data channels while the machine is running; determining a change in relative phase based on the digital waveform data; monitoring the first data channel assigned to the single-axis sensor located at a constant position on a second machine; monitoring the second, the third, and the fourth data channels respectively assigned to axes of a three-axis sensor located at a location associated with the second machine; as well as Gapless digital waveform data from all data channels of the second machine are simultaneously recorded while both machines are running.

12. The method of claim 11, wherein the triaxial sensor is positioned at a plurality of locations associated with the machine while acquiring the digital waveform data.

13. The method of claim 11, wherein the second, third and fourth channels are assigned together to a series of three-axis sensors respectively located at different locations associated with the machine. The method of claim 13 , wherein data is received from all sensors simultaneously.

15. The method of claim 11, further comprising determining an operating deformation mode based on the change in relative phase and the waveform data.

16. The method of claim 11, wherein the constant position is a position associated with an axis of the machine.

17. The method of claim 13, wherein three-axis sensors in the series of three-axis sensors are respectively located at different locations and are respectively associated with different bearings in the machine.

18. The method of claim 13, wherein the constant position is a position associated with the shaft of the machine, and wherein three-axis sensors in the series of three-axis sensors are respectively located at different positions and are respectively associated with different bearings supporting the shaft in the machine.

19. The method of claim 11, further comprising characterizing a contribution from each of the machines in the gapless digital waveform data simultaneously from the second machine.

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