Method and system for data collection, learning, and streaming of machine signals for analysis and maintenance using the industrial internet of things

Abstract

To provide a predictive maintenance system for an industrial machine.SOLUTION: A predictive maintenance system for an industrial machine includes: an industrial machine data analysis facility which generates a stream of industrial machine soundness monitoring data by applying machine learning to data representing a state of a part of the industrial machine received via a data collection network; an industrial machine predictive maintenance facility which generates an industrial machine service recommendation responding to the soundness monitoring data by applying a machine failure detection and classification algorithm; a computerized maintenance management system which generates an order or / and request of a service and component responding to the industrial machine service recommendation; and a service and provision adjustment facility which processes information regarding a service implemented by the industrial machine in response to the order or / and request of service and component, and verifies the implemented service while generating a ledger of the service activity and result of each industrial machine.SELECTED DRAWING: None

Description

[Technical Field]

[0001] This application is a continuation of U.S. Provisional Patent Application No. 62 / 714,078, filed August 2, 2018, entitled "METHODS AND SYSTEMS FOR STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS," U.S. Provisional Patent Application No. 62 / 713,897, filed August 2, 2018, entitled "METHODS AND SYSTEMS FOR DATA COLLECTION AND LEARNING USING THE INDUSTRIAL INTERNET OF THINGS," and U.S. Provisional Patent Application No. 62 / 713,897, filed November 8, 2018, entitled "METHODS AND SYSTEMS FOR STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS." U.S. Provisional Patent Application No. 62 / 757,166, filed January 31, 2019, entitled "METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS"; U.S. Provisional Patent Application No. 62 / 799,732, filed September 26, 2018, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH FREQUENCY BAND ADJUSTMENTS FOR DIAGNOSING OIL AND GAS PRODUCTION EQUIPMENT"; U.S. Provisional Patent Application No. 16 / 143, filed September 26, 2018, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH FREQUENCY BAND ADJUSTMENTS FOR DIAGNOSING OIL AND GAS PRODUCTION EQUIPMENT";This application claims priority to U.S. Patent Application No. 15 / 973,406, filed May 7, 2018, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS."

[0002] U.S. Patent Application No. 16 / 143,286, filed September 26, 2018, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH FREQUENCY BAND ADJUSTIMENTS FOR DIAGNOSING OIL AND GAS PRODUCTION EQUIPMENT," has filed at least one U.S. Provisional Patent Application No. 62 / 335,589, filed May 9, 2016, entitled "STRONG FORCE INDUSTRIAL IOT MATRIX," and U.S. Provisional Patent Application No. 62 / 335,589, filed June 15, 2016, entitled "STRATEGY FOR HIGH SAMPLING RATE DIGITAL RECORDING OF MEASUREMENT WAVEFORM DATA OF AN AUTOMATED SEQUENTIAL LIST THAT STREAMS LONG-DURATION AND GAP-FREE WAVEFORM DATA TO STORAGE FOR MORE U.S. Provisional Patent Application No. 62 / 350,672, entitled "FLEXIBLE POST-PROCESSING," filed October 26, 2016, U.S. Provisional Patent Application No. 62 / 412,843, entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS," filed November 28, 2016, U.S. Provisional Patent Application No. 62 / 417, entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS," filed November 28, 2016,No. 15 / 973,406, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS," filed on May 7, 2018, and published on February 7, 2019, as WO 2019 / 028269, which claims priority to U.S. Patent Application No. PCT / US2017 / 031721, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS," filed on May 9, 2017, and published on November 16, 2017, as WO 2017 / 19682, which claims priority to U.S. Patent Application No. 15 / 973,406, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS," filed on August 2, 2018, and published on February 7, 2019, as WO 2019 / 028269, which claims priority to U.S. Patent Application No. PCT / US2017 / 031721 ... This is a bypass continuation of International Application No. PCT / US2018 / 045306, entitled "Industrial Internet of Things Data Collection Environment with Large Data Sets." In U.S. Patent Application No. 16 / 143,286, International Application No. PCT / US2018 / 045306 and U.S. Patent Application No. 15 / 973,406, U.S. Provisional Patent Application No. 62 / 540,557, filed August 2, 2017, entitled "SMART HEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS," U.S. Provisional Patent Application No. 62 / 562,487, filed September 24, 2017, entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS," U.S. Provisional Patent Application No. 62 / 584, filed November 8, 2017, entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS,"This application claims priority to U.S. Provisional Patent Application No. 62 / 540,513, filed August 2, 2017, entitled "SYSTEMS AND METHODS FOR SMART HEATING SYSTEM THAT PRODUCES AND USES HYDROGEN FUEL." This application also claims priority to U.S. Provisional Patent Application No. 62 / 713,897, filed August 2, 2018, entitled "METHODS AND SYSTEMS FOR DATA COLLECTION AND LEARNING USING THE INDUSTRIAL INTERNET OF THINGS," and U.S. Provisional Patent Application No. 62 / 757,166, filed November 2, 2018, entitled "METHODS AND SYSTEMS FOR STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS." These applications are U.S. Provisional Patent Application No. 62 / 333,589, filed May 9, 2016, entitled "STRONG FORCE INDUSTRIAL IOT MATRIX," U.S. Provisional Patent Application No. 62 / 350,672, filed June 15, 2016, entitled "STRATEGY FOR HIGH SAMPLING RATE DIGITAL RECORDING OF MEASUREMENT WAVEFORM DATA OF AN AUTOMATED SEQUENTIAL LIST THAT STREAMS LONG-DURATION AND GAP-FREE WAVEFORM DATA TO STORAGE FOR MORE FLEXIBLE POST-PROCESSING," and U.S. Provisional Patent Application No. 62 / 412, filed October 26, 2016, entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS," respectively.No. 843, and a bypass continuation-in-part of International Application PCT / US2017 / 031721, entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS," filed May 9, 2017, and published November 16, 2017, as WO 2017 / 196821, which claims priority to U.S. Provisional Patent Application No. 62 / 427,141, filed November 28, 2016, and entitled "METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS." This application also claims priority to U.S. Provisional Patent Application No. 62 / 540,557, filed August 2, 2017, entitled "SMART HEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS," U.S. Provisional Patent Application No. 62 / 562,487, filed September 24, 2017, entitled "METHODS AND SYSTEM FOR THE INDUSTRIAL INTERNET OF THINGS," and U.S. Patent Application No. 583,487, filed November 8, 2017, entitled "METHODS AND SYSTEM FOR THE INDUSTRIAL INTERNET OF THINGS." Each of the foregoing applications is incorporated herein by reference as if fully set forth in its entirety.

[0003] The present disclosure relates to methods and systems for data collection in industrial environments and for utilizing the collected data for monitoring, remote control, autonomous action, etc. in industrial environments. [Background technology]

[0004] Heavy industrial environments include large-scale manufacturing environments (e.g., aircraft, ships, trucks, automobiles, and large industrial machinery manufacturing), energy production environments (e.g., oil and gas plants and renewable energy environments), and energy extraction environments (e.g., mining and drilling). Construction environments, such as large-scale building construction, feature highly complex machinery, equipment, and systems, along with highly complex workflows in which operators must consider many parameters, metrics, and other factors to optimize the design, development, deployment, and operation of various technologies to improve overall outcomes. Historically, in heavy industrial environments, data has been collected by humans using dedicated data collectors, often recording specific batches of sensor data onto media such as tape or hard drives for later analysis. Traditionally, data batches have been returned to a central office, where signal processing and other analysis has been performed on the data collected by various sensors, which has then been used as the basis for diagnosing environmental issues or proposing ways to improve operations. This work has historically been performed on timescales of weeks to months and on limited data sets.

[0005] The advent of the Internet of Things (IoT) has enabled continuous connectivity to a wider range of devices, most of which are consumer devices such as lights and thermostats. In more complex industrial environments, the range of available data is often limited, and the complexity of handling data from multiple sensors continues to make creating effective "smart" solutions for the industrial sector much more challenging. A need exists for improved methods and systems for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, intelligent diagnosis of problems, and intelligent optimization of operations in various heavy industrial environments.

[0006] Industrial systems in diverse environments present many challenges in utilizing data from multiple sensors. Many industrial systems have extensive computing resources and network capabilities at a given location at a given time, for example, due to the fact that parts of the system are upgraded or replaced on various timescales, mobile equipment enters and leaves the location, and the capital costs and risks associated with upgrading equipment. Furthermore, many industrial systems are located in challenging environments where network connectivity fluctuates, numerous noise sources, such as vibration noise and electromagnetic (EM) noise sources, exist in significant and varied locations, and parts of the system have high pressure, high noise, high temperature, and corrosive materials. Many industrial processes are subject to high variability in process operating parameters and nonlinear responses to out-of-specification operation. Therefore, sensing requirements for industrial processes can change over time, the operational stage of the process, equipment aging, and operating conditions. Traditionally, known industrial processes suffer from conservative sensing configurations that detect many parameters that are not required during most of the operation of the industrial system, or that tolerate risk within the process and do not detect parameters that are only occasionally utilized in system characterization. Additionally, previously known industrial systems lack the flexibility to rapidly configure detected parameters in real time and manage system fluctuations, such as intermittent network availability. Industrial systems often use similar components throughout the system, such as pumps, mixers, tanks, and fans. However, previously known industrial systems lack a mechanism for leveraging data from similar components that may be used in different types of processes, and this may be unavailable due to competitive concerns. Additionally, previously known industrial systems do not integrate data from offset systems into sensor planning and execution in real time.

[0007] Industrial environments are home to a wide range of large, complex, and heavy machinery designed to have extremely long service lives, requiring ongoing service, including scheduled maintenance and unplanned repairs.

[0008] Many large industrial machines require ongoing maintenance, service, and repair, preferably with minimal or no interruption, because they are involved in expensive production processes or other processes, such as energy production, manufacturing, mining, excavation, and transportation. Unforeseen problems or lengthy delays in service operations that require the shutdown of machines critical to such processes can cost thousands, or even millions, of dollars per day. The embodiments disclosed herein and documents incorporated by reference provide a platform with improved devices, systems, components, processes, and methods for collecting, processing, and using data from and about industrial machines, including, among other things, for predicting failures, predicting maintenance needs, and facilitating repairs. However, in some regions, the workforce performing maintenance, service, and repair of heavy industrial machinery is aging. When workers leave the company, much of the specialized knowledge is lost, and new workers often lack even basic factual information about the machinery (e.g., its internal structure), operational information (e.g., how it is intended to operate in various work modes), and / or procedural information (e.g., how to perform routine maintenance tasks), let alone the know-how or expertise to handle more complex procedures, such as repairs that may require multi-step procedures with unfamiliar parts and tools. Another challenge is locating parts and components related to industrial machinery in a timely manner, such as those needed for emergency repairs, so that they are available where and when needed to perform the work. Without information about the machine's internal structure, parts, and components, workers may have to guess what the problem is, which parts are involved, and how the repair needs to be performed. Repairs may require one or more visits to discover the nature of the problem, the parts that need to be replaced, the tools required, etc., and may also require one or more visits to perform the repair once the relevant parts and tools arrive. This can mean delays of many days at great cost to the machine operator.This process may be repeated months or even years later, as the next worker may have no way of accessing the knowledge acquired about the machine's internal workings, parts, or components acquired by the first worker. Summary of the Invention [Problem to be solved by the invention]

[0009] What is needed are improved methods and systems for collecting, discovering, capturing, disseminating, managing, and processing information about industrial machinery, including factual information such as internal structure, parts, and components, operational information, and procedural information, including know-how and other information related to maintenance, service, and repair. What is also needed are improved methods and systems for locating workers with the know-how and expertise related to the maintenance, service, and repair of specific machinery. What is also needed are improved methods and systems for locating, ordering, and fulfilling orders for related parts and components so that maintenance, service, and repair work can be performed seamlessly with minimal interruptions. [Means for solving the problem]

[0010] In an embodiment, an industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates a stream of industrial machine health monitoring data by applying machine learning to data representative of the condition of a portion of the industrial machine received via a data collection network. The system may further include an industrial machine predictive maintenance facility that generates industrial machine service recommendations responsive to the health monitoring data by applying a machine fault detection and classification algorithm. The system may further include a computerized maintenance management system (CMMS) that generates at least one of service and parts orders and requests responsive to receiving the industrial machine service recommendations. And, the system may include a service and delivery coordination facility that receives and processes information regarding services performed on the industrial machine in response to at least one of the service and parts orders and requests, thereby verifying the services performed while generating a ledger of service activities and results for each industrial machine.

[0011] In an embodiment, a method for predicting service events from vibration data may include a series of operational steps, including capturing vibration data from at least one vibration sensor positioned to capture vibrations of a portion of an industrial machine. The captured vibration data may be processed to determine at least one of a frequency, an amplitude, and a gravity of the captured vibration. A segment of a multi-segment vibration frequency spectrum bounded by the captured vibration may then be determined, for example, based on the determined frequency. Accordingly, calculating vibration severity units for the captured vibration may be based on the determined segment and at least one of a peak amplitude and a gravity force derived from the vibration data. Further, the method may include generating a signal in a predictive maintenance circuit to perform a maintenance action on the portion of the industrial machine based on the severity units.

[0012] In embodiments, zero-gap signal capture at a streaming sample rate may include sampling a signal at the streaming sample rate, thereby generating multiple samples of the signal. The multiple samples of the signal may be assigned to a signal routing circuit that generates a first portion of the multiple samples of the signal to a first signal analysis circuit, the portion being assigned to the first signal analysis circuit based on a first signal analysis sampling rate that is less than the streaming sample rate. The multiple portions of the samples of the signal may be assigned to a signal routing circuit that generates a second portion of the multiple portions of the samples of the signal to a second signal analysis circuit, the portion being assigned based on a second signal analysis sampling rate that is less than the streaming sample rate. In embodiments, zero-gap signal capture may further include storing the multiple portions of the samples of the signal, the output of the first signal analysis circuit, and the output of the second signal analysis circuit. In embodiments, the assigned first and second portions of the stored multiple samples are tagged with indicators that reference the corresponding stored signal analysis outputs.

[0013] Provided herein are methods and systems for data collection in industrial environments and improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments. These methods and systems include methods, systems, components, devices, workflows, services, processes, etc., deployed in a variety of configurations and locations: a) at the “edge” of the Internet of Things, such as the local environment of a heavy industrial machine; (b) in data transport networks that move data between the local environment of a heavy industrial machine and other environments, such as other machines or remote controllers of companies that own or operate the machines or facilities where the machines are operated; and (c) where facilities are deployed to control the machines or their environments, such as in cloud or on-premise computing environments of companies that own or control the heavy industrial environment or the machines, devices, or systems deployed therein. These methods and systems include various methods and systems for providing improved data collection and methods and systems for deploying increased intelligence at the edge, within the network, in the cloud, or on the premises of controllers of industrial environments.

[0014] Disclosed herein are methods and systems for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of energy production equipment; for cloud-based systems including machine pattern recognition based on fusion of remote analog industrial sensors or machine pattern analysis of condition information from multiple analog industrial sensors; for on-device sensor fusion and data storage for Industrial IoT devices, including on-device sensor fusion and data storage for Industrial IoT devices where data from multiple sensors are multiplexed at the device for storage of a fused data stream. 1. A self-organizing system for a self-organizing system, including a self-organizing data marketplace for industrial IoT data, wherein available data elements are organized within the marketplace for consumption by consumers based on feedback from training a self-organizing facility with a training set and measures of marketplace success. 2. A self-organizing system for a self-organizing data pool, including self-organizing the data pools based on utilization and / or yield metrics, wherein utilization and / or yield metrics are tracked for multiple data pools. 3. A self-organizing system for a self-organizing data pool, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the swarm members, wherein the self-organizing collection device includes a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the swarm members, the self-organizing collection device including a multi-sensor data collector that can optimize data collection, power, and / or yield based on the conditions of its environment.Self-organizing storage for multi-sensor data collectors, including self-organizing storage for multi-sensor data collectors for industrial sensor data; self-organizing network coding for multi-sensor data networks, including self-organizing network coding for data networks that transfer data from multiple sensors in an industrial data collection environment.

[0015] Disclosed herein are methods and systems for training artificial intelligence ("AI") models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects utilization, yield, or impact measures when the AI ​​model operates on sensor data from an industrial environment for an industrial IoT distributed ledger. The industrial IoT distributed ledger includes a distributed ledger that supports tracking of transactions performed in an automated data marketplace for industrial IoT data for network-sensitive collectors. The network-sensitive collectors include self-organizing multi-sensor data collectors that are sensitive to network conditions and can optimize based on bandwidth, quality of service, pricing, and / or other network conditions for remotely organized universal data collectors that can power up and down sensor interfaces based on needs identified in the industrial data collection environment and / or other considerations, and for tactile or multi-sensor user interfaces, including wearable tactile or multi-sensor user interfaces for industrial sensor data collectors with vibration, thermal, electrical, and / or sound outputs.

[0016] Disclosed herein are methods and systems for a presentation layer for augmented reality and virtual reality (AR / VR) industrial glasses, where heatmap elements are presented based on patterns and / or parameters of collected data, and for state-sensitive, self-organized tuning of the AR / VR interface based on feedback metrics and / or training in an industrial environment.

[0017] In an embodiment, a system for data collection, processing, and utilization of signals from at least a first element of at least a first machine in an industrial environment includes a platform including a computing environment connected to a local data collection system having at least 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 in the local data collection system configured to be connected to the first machine and a second sensor in the local data collection system. The system further includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs, including a first input connected to the first sensor and a second input connected to the second sensor. Throughout this disclosure, wherever a crosspoint switch, multiplexer (MUX) device, or other multiple-input, multiple-output data collection or communication device is described, any multi-sensor collection device is also contemplated herein. In certain embodiments, the multi-sensor collection device includes one or more channels configured for or compatible with analog sensor inputs. The plurality of outputs includes a first output and a second output configured to be switchable between a state in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a state in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of the plurality of inputs is configured to be individually assigned to one of the plurality of outputs or to be coupled to any subset of the inputs to the outputs. Unassigned outputs are configured to be switched off, for example, by generating a high impedance state.

[0018] In an embodiment, the first sensor signal and the second sensor signal are continuous vibration data related to an industrial environment. In an embodiment, a second sensor of the local data collection system is configured to be connected to a first machine. In an embodiment, a second sensor in the local data collection system is configured to be connected to a second machine in the 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 Internet Protocol front-end signal conditioning for improved signal-to-noise ratio. In an embodiment, the crosspoint switch includes a third input configured to have a continuously monitored alarm with a predetermined trigger condition when the third input is not assigned to or detected by any of the multiple outputs.

[0019] In an embodiment, the local data collection system includes a plurality of multiplexing units and a plurality of data collection units that receive a plurality of data streams from a plurality of machines in the industrial environment. In an embodiment, the local data collection system includes a distributed complex programmable logic device ("CPLD") chip dedicated to a data bus for logical control of the plurality of multiplexing units and the plurality of data collection units, respectively. In an embodiment, the local data collection system is configured to provide high amperage input capability using solid-state relays. In an embodiment, the local data collection system is configured to power down at least one of the analog sensor channels and the component board.

[0020] In an embodiment, the local data collection system includes a phase-locked loop bandpass tracking filter configured to acquire slow revolutions per minute ("RPMs") and phase information. In an embodiment, the local data collection system is configured to digitally derive the phase using at least one trigger channel and an onboard timer relative to at least one of the multiple inputs. In an embodiment, the local data collection system includes a peak detector configured to autoscale using a separate analog-to-digital converter for peak detection. In an embodiment, the local data collection system is configured to route the raw buffered at least one trigger channel to at least one of the multiple inputs. In an embodiment, the local data collection system includes at least one delta-sigma analog-to-digital converter configured to increase the oversampling rate of the input to reduce the sampling rate of the output, minimizing anti-aliasing filter requirements. In an embodiment, a distributed CPLD chip dedicated to a data bus for logical control of the plurality of multiplexing units and the plurality of data acquisition units includes a high frequency crystal clock reference configured to be divided by at least one of the distributed CPLD chips for a delta-sigma analog-to-digital converter of at least one of the distributed CPLD chips to achieve a lower sampling rate without digital resampling.

[0021] In an embodiment, the local data collection system is configured to acquire long blocks of data at a single, relatively high sampling rate, as opposed to multiple sets of data acquired at different sampling rates. In an embodiment, the single, relatively high sampling rate corresponds to a maximum frequency of approximately 40 kilohertz. In an embodiment, the long blocks of data are greater than one minute in duration. In an embodiment, the local data collection system includes a plurality of data collection units, each having an onboard card set configured to store calibration information and maintenance history for the data collection unit in which the onboard card set is located. In an embodiment, the local data collection system is configured to plan a data collection route based on a hierarchical template.

[0022] In an embodiment, the local data collection system is configured to manage data collection bands. In an embodiment, the data collection bands define specific frequency bands and at least one of a group of spectral peaks, a true peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. In an embodiment, the local data collection system includes a neural net expert system that uses intelligent management of the data collection bands. In an embodiment, the local data collection system is configured to create data collection routes based on hierarchical templates, each of which includes a data collection band associated with a machine associated with the data collection route. In an embodiment, at least one of the hierarchical templates is associated with multiple 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 a second machine. In an embodiment, at least one of the hierarchical templates is associated with the positional proximity of at least the first machine to the second machine.

[0023] In an embodiment, the local data collection system includes a graphical user interface (“GUI”) system configured to manage data collection bands. In an embodiment, the GUI system includes an expert system diagnostic tool. In an embodiment, the platform includes cloud-based machine pattern analysis of condition information from a plurality of sensors to provide predicted condition information of the 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 yield metrics. In an embodiment, the platform includes a self-organized swarm of industrial data collectors. In an embodiment, the local data collection system includes a wearable tactile user interface for industrial sensor data collectors having at least one of a vibration output, a thermal output, an electrical output, and an acoustic output.

[0024] In an embodiment, the multiple 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 is a signal from a single-axis sensor at a fixed position associated with the first machine. In an embodiment, the second sensor is a three-axis sensor. In an embodiment, the local data acquisition system is configured to simultaneously record gap-free 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 simultaneously recorded gap-free digital waveform data. In an embodiment, the second sensor is configured to be movable to multiple positions associated with the first machine while acquiring the simultaneously recorded gap-free digital waveform data. In an embodiment, the multiple outputs of the crosspoint switch include a third output and a fourth output. The second output, the third output, and the fourth output are jointly assigned to a sequence of three-axis sensors each located at a different position associated with the machine. In an embodiment, the platform is configured to determine an operating deflection shape based on the change in relative phase and the simultaneously recorded gap-free digital waveform data.

[0025] In an embodiment, the invariant position is a position associated with an axis of rotation of the first machine. In an embodiment, each triaxial sensor in the sequence of triaxial sensors is located at a different position on the first machine, but is associated with a different bearing on the first machine. In an embodiment, each triaxial sensor in the sequence of triaxial sensors is located at a similar position associated with a similar bearing, but is associated with a different machine. In an embodiment, the local data collection system is configured to acquire simultaneously recorded gap-free digital waveform data from the first machine while both the first machine and the second machine are operating. In an embodiment, the local data collection system is configured to characterize contributions from the first machine and the second machine in the simultaneously recorded gap-free digital waveform data from the first machine. In an embodiment, the simultaneously recorded gap-free digital waveform data has a duration of more than one minute.

[0026] In an embodiment, a method for monitoring a machine having at least one shaft supported by a set of bearings includes monitoring a first data channel assigned to a one-axis sensor at a fixed position associated with the machine, monitoring second, third, and fourth data channels assigned to respective axes of a three-axis sensor, simultaneously recording gap-free 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.

[0027] In an embodiment, the triaxial sensors are positioned at multiple locations associated with the machine while acquiring the digital waveform. In an embodiment, the second, third, and fourth channels are assigned together to a sequence of triaxial sensors, each positioned at a different location associated with the machine. In an embodiment, data is received simultaneously from all sensors. In an embodiment, the method includes determining an operational deflection shape based on the change in relative phase information and the waveform data. In an embodiment, the unchanged location is a location associated with the shaft of the machine. In an embodiment, each of the series of triaxial sensors is positioned at a different location and associated with a different bearing of the machine. In an embodiment, the unchanged location is a location associated with the shaft of the machine. Each of the triaxial sensors of the sequence of triaxial sensors is positioned at a different location and associated with a different bearing supporting the shaft of the machine.

[0028] In an embodiment, the method includes monitoring a first data channel assigned to a single-axis sensor at a fixed position associated with the second machine. The method includes monitoring a second data channel, a third data channel, and a fourth data channel assigned to axes of a three-axis sensor at a position associated with the second machine. The method also includes simultaneously recording gap-free digital waveform data from all of the data channels from the second machine while both of the second machines are operating. In an embodiment, the method includes characterizing a contribution from each machine when simultaneously recording gap-free digital waveform data from the second machines.

[0029] In an embodiment, a method for data collection, processing, and signal utilization by a platform monitoring at least a first element of at least a first machine in an industrial environment includes automatically acquiring at least a first sensor signal and a second sensor signal with a local data collection system monitoring the at least first machine using a computing environment. The method includes connecting a first input of a crosspoint switch of the local data collection system to the first sensor and connecting a second input of the crosspoint switch to the second sensor of the local data collection system. The method includes switching between a state in which a first output of the crosspoint switch alternates between delivery of at least the first sensor signal and the second sensor signal and a state in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output of the crosspoint switch. The method also includes switching an unassigned output of the crosspoint switch to a high impedance state.

[0030] In an embodiment, the first sensor signal and the second sensor signal are continuous vibration data from an industrial environment. In an embodiment, a second sensor of the local data collection system is connected to a first machine. In an embodiment, the second sensor of the local data collection system is connected to a second machine in the industrial environment. In an embodiment, the method includes automatically comparing, with a computing environment, a relative phase 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 a first input of the crosspoint switch includes Internet Protocol front-end signal conditioning for improved signal-to-noise ratio.

[0031] In an embodiment, the method includes continuously monitoring at least a third input of the crosspoint switch with an alarm having a predetermined trigger condition when the third input is not assigned to any of the multiple outputs of the crosspoint switch. In an embodiment, the local data collection system includes a plurality of multiplexing units and a plurality of data collection units that receive a plurality of data streams from a plurality of machines in the industrial environment. In an embodiment, the local data collection system includes a distributed CPLD chip that is dedicated to a data bus for logic control of the multiplexing units and the plurality of data collection units that receive the multiple data streams from the plurality of machines in the industrial environment. In an embodiment, the local data collection system provides high amperage input capability using solid state relays.

[0032] In an embodiment, the method includes powering down at least one of the analog sensor channels and a component board of the local data acquisition system. In an embodiment, the local data acquisition system includes an external voltage reference for an A / D zero reference independent of the voltages of the first sensor and the second sensor. In an embodiment, the local data acquisition system includes a phase-locked loop bandpass tracking filter that obtains low speed rotation speed and phase information. In an embodiment, the method includes digitally deriving the phase using an on-board timer relative to at least one trigger channel and at least one of a plurality of inputs on the crosspoint switch.

[0033] In an embodiment, the method includes autoscaling with a peak detector using a separate analog-to-digital converter for peak detection. In an embodiment, the method includes routing at least one raw, buffered trigger channel to at least one of a plurality of inputs on a crosspoint switch. In an embodiment, the method includes increasing the oversampling rate of the input using at least one delta-sigma analog-to-digital converter to reduce the sampling rate output and minimize anti-aliasing filter requirements. In an embodiment, the distributed CPLD chips are dedicated to data buses for logic control of the multiplexing unit and the multiplexed data acquisition unit, respectively, and each includes a high-frequency crystal clock reference divided by at least one of the distributed CPLD chips for the delta-sigma analog-to-digital converter of at least one of the distributed CPLD chips to achieve the lower sampling rate without digital resampling. In an embodiment, the method includes acquiring long blocks of data at a single, relatively high sampling rate with a local data acquisition system, as opposed to multiple sets of data acquired 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 block of data is greater than one minute in duration. In an embodiment, the local data collection system includes a plurality of data collection units, each data collection unit having an onboard card set that stores calibration information and maintenance history for the data collection unit in which the onboard card set is located.

[0034] In an embodiment, a method includes planning a data collection route 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 defining specific frequency bands and at least one of a group of spectral peaks, a true peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. In an embodiment, the local data collection system includes a neural net expert system that uses intelligent management of the data collection bands. In an embodiment, the local data collection system creates a data collection route based on hierarchical templates, each of which includes a data collection band associated with a machine associated with the data collection route. In an embodiment, at least one of the hierarchical templates is associated with multiple 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 a second machine. In an embodiment, at least one of the hierarchical templates is associated with the positional proximity of at least the first machine to the second machine.

[0035] In an embodiment, the method includes controlling a GUI system of a local data collection system to manage data collection bands. The GUI system includes an expert system diagnostic tool. In an embodiment, the platform computing environment includes cloud-based machine pattern analysis of condition information from a plurality of sensors to provide predicted condition information of the industrial environment. In an embodiment, the platform computing environment provides self-organization of a data pool based on at least one of utilization metrics and yield metrics. In an embodiment, the platform computing environment includes a self-organized group of industrial data collectors. In an embodiment, each of a plurality of inputs of a crosspoint switch is individually assignable to any of a plurality of outputs of the crosspoint switch.

[0036] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for capturing multiple streams of sensed data from sensors positioned to monitor an aspect of an industrial machine associated with at least one moving part of the machine, at least one of the streams including multiple frequencies of data. The method may include identifying a subset of data included in at least one of the multiple streams that corresponds to data representing at least one predefined frequency. The at least one predefined frequency is represented by a set of data collected from an alternative sensor positioned to monitor the aspect of the industrial machine associated with at least one moving part of the industrial machine. The method may further include processing the identified data using data processing equipment with an algorithm configured to be applied to the set of data collected from the alternative sensor. Finally, the method may include storing at least one of the streams of data, the identified subset of data, and a result of processing the identified data in an electronic dataset.

[0037] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for applying data captured from sensors deployed to monitor an aspect of the industrial machine associated with at least one moving part of the machine. The data is captured at a predefined line of resolution covering a predefined frequency range and sent to a frequency matching function that identifies a subset of data streamed from other sensors deployed to monitor the aspect of the industrial machine associated with at least one moving part of the machine. The streamed data includes multiple lines of resolution and frequency range. The identified subset of data corresponds to the line of resolution and the predefined frequency range. The method may include storing the subset of data in an electronic data record in a format corresponding to the format of the data captured at the predefined line of resolution and signaling the existence of the stored subset of data to data processing equipment. The method may optionally include processing the subset of data with at least one set of algorithms, models, and pattern recognizers associated with processing data captured at the predefined line of resolution covering the predefined frequency range.

[0038] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for identifying a subset of streamed sensor data, sensor data captured from sensors positioned to monitor an aspect of the industrial machine associated with at least one moving part of the machine, a method for identifying the subset of streamed sensor data, and a method for establishing a first logical path for electronic communication between a first computing facility and a second computing facility that performs the identification. In embodiments, the identified subset of streamed sensor data is communicated exclusively via the established first logical path when communicating the subset of streamed sensor data from the first facility to the second facility. The method may further include establishing a second logical path for electronic communication between the first computing facility and the second computing facility for at least a portion of the streamed sensor data that is not the identified subset. Additionally, the method may further include establishing a third logical path for electronic communication between the first computing facility and the second computing facility for at least one portion of the streamed sensor data including the identified subset and at least one other portion of the data not represented by the identified subset.

[0039] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a first data sensing and processing system that captures first data from a first set of sensors deployed to monitor an aspect of the industrial machine associated with at least one moving part of the machine, the first data covering a set of lines of resolution and a frequency range. The system may also include a second data sensing and processing system that captures and streams second data from a second set of sensors deployed to monitor the aspect of the industrial machine associated with at least one moving part of the machine, the second data covering a plurality of resolution lines including the set of lines of resolution and a plurality of frequencies including a range of frequencies, and the second data covering a plurality of resolution lines including the set of lines of resolution and a plurality of frequencies including a range of frequencies. The system may select a portion of the second data corresponding to the set of lines of resolution and the frequency range of the first data and enable the selected portion of the second data to be processed by the first data sensing and processing system.

[0040] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for automatically processing a portion of the stream of sensed data. The sensed data is received from a first set of sensors deployed to monitor an aspect of an industrial machine associated with at least one moving part of the machine. The sensed data is responsive to an electronic data structure that facilitates extracting a subset of the stream of sensed data corresponding to a set of sensed data received from a second set of sensors deployed to monitor an aspect of the industrial machine associated with at least one moving part of the machine. The set of sensed data is constrained to a frequency range. The method of claim 1, wherein the stream of sensed data includes a frequency range exceeding the frequency range of the set of sensed data, and the processing comprises executing an algorithm on a portion of the stream of sensed data that is constrained to the frequency range of the set of sensed data, the algorithm being configured to process the set of sensed data.

[0041] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for receiving first data from a sensor deployed to monitor an aspect of the industrial machine associated with at least one moving part of the industrial machine. The method may further include detecting at least one of a frequency range and a line of resolution represented by the first data and receiving a stream of data from the sensor deployed to monitor the aspect of the industrial machine associated with at least one moving part of the machine. The stream of data includes: (1) a plurality of frequency ranges and a plurality of lines of resolution exceeding the frequency ranges and the line of resolution represented by the first data; (2) a set of data extracted from the stream of data corresponding to at least one of the frequency ranges and the line of resolution represented by the first data; and (3) the set of extracted data processed with a data processing algorithm configured to process data within the frequency ranges and the line of resolution represented by the first data.

[0042] Provided herein are methods and systems for identifying the condition of a target in an industrial environment using mobile devices, including wearable devices, mobile robots, mobile vehicles, and / or handheld devices. The mobile devices include one or more sensors that may be configured to record measurements related to the condition of the target based, for example, on vibration, temperature, electricity, magnetism, sound, and / or other measurements. Data captured using some or all of these mobile devices may be processed by an intelligent system onboard the mobile devices and / or at a server communicating with the mobile devices via a network. The intelligent system includes intelligence for processing the data captured using each mobile device. Processing the data may include identifying the condition of the target for which the measurements were recorded, for example, by comparing the condition-related measurements from the wearable device with information stored in a database (e.g., which may be part of a knowledge base related to the industrial environment). In embodiments, corrective actions may be identified and implemented in response to the condition-related measurements captured using the mobile devices.

[0043] In an embodiment, a method for using a wearable device to identify a condition of a target in an industrial environment is disclosed. In an embodiment, the method includes recording condition-related measurements of the target using one or more sensors of the wearable device, transmitting the condition-related measurements to a server over a network, and processing the condition-related measurements against pre-recorded data for the target using an intelligent system associated with the server. In an embodiment, processing the condition-related measurements against the pre-recorded data for the target includes identifying the pre-recorded data for the target in a knowledge base related to the industrial environment and identifying the condition indicated by the pre-recorded data for the target in the knowledge base as the condition of the target.

[0044] In an embodiment, a system for identifying a condition of a target in an industrial environment is disclosed. In the embodiment, the system includes a first wearable device including one or more sensors configured to record a first type of condition-related measurement, a second wearable device including one or more sensors configured to record a second type of condition-related measurement, a server receiving the first type of condition-related measurement from the first wearable device and the second type of condition-related measurement from the second wearable device, and the server including an intelligent system configured to process the first type of condition-related measurement and the second type of condition-related measurement against pre-recorded data stored in a knowledge base to identify a condition of the target, and update the pre-recorded data according to at least one of the first type of condition-related measurement or the second type of condition-related measurement.

[0045] In an embodiment, a method for using a mobile data collector to identify a state of a target in an industrial environment is disclosed. In an embodiment, the method comprises controlling the mobile data collector to approach a location of a target in the industrial environment, recording state-related measurements of the target using one or more sensors of the mobile data collector, transmitting the state-related measurements to a server over a network, and processing the state-related measurements against pre-recorded data for the target using an intelligent system associated with the server. In an embodiment, processing the state-related measurements against the pre-recorded data of the target includes identifying the pre-recorded data of the target in a knowledge base related to the industrial environment and identifying the state indicated by the pre-recorded data of the target in the knowledge base as the state of the target.

[0046] In an embodiment, a system for identifying a condition of a target in an industrial environment is disclosed. In the embodiment, the system includes: a first mobile data collector including one or more sensors configured to record a first type of condition-related measurement; a second mobile data collector including one or more sensors configured to record a second type of condition-related measurement; a server that receives the first type of condition-related measurement from the first mobile data collector and the second type of condition-related measurement from the second mobile data collector; and the server including an intelligent system configured to process the first type of condition-related measurement and the second type of condition-related measurement against pre-recorded data stored in a knowledge base to identify a condition of the target and update the pre-recorded data according to at least one of the first type of condition-related measurement or the second type of condition-related measurement.

[0047] In an embodiment, a method for using a handheld device to identify a condition of a target in an industrial environment is disclosed. In an embodiment, the method includes recording condition-related measurements of the target using one or more sensors of the handheld device, transmitting the condition-related measurements to a server over a network, and processing the condition-related measurements against pre-recorded data of the target using an intelligent system associated with the server. In an embodiment, processing the condition-related measurements against pre-recorded data of the target includes identifying the pre-recorded data of the target in a knowledge base associated with the industrial environment and identifying the condition indicated by the pre-recorded data of the target in the knowledge base as the condition of the target.

[0048] In an embodiment, a system for identifying a condition of a target in an industrial environment is disclosed. In the embodiment, the system includes a first portable (handheld) device including one or more sensors configured to record a first type of condition-related measurement, a second handheld device including one or more sensors configured to record a second type of condition-related measurement, a server receiving the first type of condition-related measurement from the first handheld device and the second type of condition-related measurement from the second handheld device, and the server including an intelligent system configured to: process the first type of condition-related measurement and the second type of condition-related measurement against pre-recorded data stored in a knowledge base to identify a condition of the target; and update the pre-recorded data according to at least one of the first type of condition-related measurement or the second type of condition-related measurement.

[0049] Provided herein are methods and systems for a computer vision system configured to identify operational characteristics, such as vibration or other suitable characteristics, of one or more industrial IoT devices using input from one or more data capture devices. The one or more data capture devices may include image data capture devices that capture visible and non-visible light, sensors that measure various characteristics of the one or more industrial IoT devices, or other suitable data capture devices. The computer vision system is configured to generate an image dataset from the input and analyze visual aspects of the image dataset to identify operational characteristics of the industrial IoT devices. Further, the computer vision system is configured to determine whether to take corrective action in response to the operational characteristics of the industrial IoT devices.

[0050] In an embodiment, an apparatus for detecting operational characteristics of a manufacturing equipment includes a memory and a processor. The memory includes instructions executable by the processor to generate one or more image data sets using raw data captured by one or more data capture devices. The memory further includes instructions executable by the processor to identify one or more values ​​corresponding to a portion of the manufacturing equipment within a point of interest represented by the one or more image data sets. The memory further includes instructions executable by the processor to record the one or more values, compare the recorded one or more values ​​with corresponding expected values, and generate a variance data set based on a comparison of the recorded one or more values ​​with the corresponding expected values. The memory further includes instructions executable by the processor to identify operational characteristics of the manufacturing equipment based on the variance data and generate instructions indicative of the operational characteristics.

[0051] In an embodiment, a method for detecting operational characteristics of a manufacturing equipment includes generating one or more image data sets using raw data captured by one or more data capture devices. The method also includes identifying one or more values ​​corresponding to a portion of the manufacturing equipment within a point of interest represented by the one or more image data sets, recording the one or more values, and comparing the recorded one or more values ​​to corresponding predicted values. The method also includes generating a variance data set based on a comparison of the recorded one or more values ​​to the corresponding predicted values, and identifying an operational characteristic of the manufacturing equipment based on the variance data. The method further includes generating an indication indicative of the operational characteristic.

[0052] In an embodiment, a system for detecting operational characteristics of a manufacturing equipment includes at least one data capture device configured to capture raw data of a point of interest of the manufacturing equipment, a memory, and a processor. The memory includes instructions executable by the processor to generate one or more image data sets using the captured raw data and identify one or more values ​​corresponding to a portion of the manufacturing equipment within the point of interest represented by the one or more image data sets. The memory further includes instructions executable by the processor to record the one or more values ​​and compare the recorded one or more values ​​with corresponding predicted values. The memory further includes instructions executable by the processor to generate a variance data set based on a comparison of the recorded one or more values ​​with the corresponding predicted values, identify operational characteristics of the manufacturing equipment based on the variance data, and generate instructions indicative of the operational characteristics.

[0053] In an embodiment, a computer vision system for detecting operational characteristics of a manufacturing equipment includes at least one data capture device configured to capture raw data of a point of interest of the manufacturing equipment, a memory, and a processor. The memory includes instructions executable by the processor to generate one or more image data sets using the captured raw data and visually identify one or more values ​​corresponding to a portion of the manufacturing equipment within the point of interest represented by the one or more image data sets. The memory further includes instructions executable by the processor to record one or more values ​​and visually compare the recorded one or more values ​​to corresponding predicted values. The memory further includes instructions executable by the processor to generate a variance data set based on a comparison of the recorded one or more values ​​to the corresponding predicted values ​​and identify an operational characteristic of the manufacturing equipment based on the variance data. The memory further includes instructions executable by the processor to compare the operational characteristic to a threshold value and determine whether the operational characteristic is within an acceptable range based on whether the operational characteristic is greater than the threshold. The memory further includes instructions executable by the processor to generate an indication indicative of the operational characteristic.

[0054] In an embodiment, a computer vision system for detecting operational characteristics of a device includes at least one data capture device configured to capture raw data of a point of interest of the device, a memory, and a processor. The memory includes instructions executable by the processor to generate one or more image data sets using the captured raw data and visually identify one or more values ​​corresponding to a portion of the device within the point of interest represented by the one or more image data sets. The memory further includes instructions executable by the processor to record the one or more values ​​and visually compare the recorded one or more values ​​to corresponding predicted values. The memory further includes instructions executable by the processor to generate a variance data set based on a comparison of the recorded one or more values ​​to the corresponding predicted values. The memory includes instructions executable by the processor to identify an operational characteristic of the device based on the variance data and compare the operational characteristic to a threshold value. The memory includes instructions executable by the processor to determine whether the operational characteristic is within an acceptable range based on whether the operational characteristic is greater than the threshold value and generate an indication indicative of the operational characteristic.

[0055] Methods and systems are provided herein that include combinations of the embodiments disclosed herein. In an embodiment, the method includes receiving vibration data representative of vibrations of at least a portion of an industrial machine from a wearable device including at least one vibration sensor used to capture the vibration data; processing the captured vibration data to determine a frequency of the captured vibration; determining a segment of a multi-segment vibration frequency spectrum bounding the captured vibration based on the frequency; calculating a severity unit for the captured vibration based on the determined segment; and generating a signal in a predictive maintenance circuit for performing a maintenance action on at least the portion of the industrial machine based on the severity unit. In an embodiment, the at least one vibration sensor of the wearable device captures the vibration data based on a waveform derived from a vibration envelope associated with at least the portion of the industrial machine. In an embodiment, the method further includes detecting, using the wearable device, proximity of the industrial machine to the wearable device and causing the wearable device to capture vibration data in response to detecting proximity of the industrial machine to the wearable device. In an embodiment, the method further includes: That is, detecting a change in vibration level of at least a portion of the industrial machine using at least one vibration sensor of the wearable device, and using the wearable device to capture vibration data in response to detecting the change in vibration level. In an embodiment, the method further includes transmitting a signal to the wearable device to trigger performance of a maintenance action. In an embodiment, calculating severity units of the captured vibration based on the determined segment includes mapping the captured vibration to severity units based on the determined segment as follows:The method further comprises mapping the captured vibration to a first severity unit if the frequency of the captured vibration corresponds to below a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the captured vibration to a second severity unit if the frequency of the captured vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the captured vibration to a third severity unit if the frequency of the captured vibration corresponds to above a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes training the intelligent system to determine whether the vibration is mapped to the first, second, or third severity unit. In an embodiment, the severity unit represents an impact of the maintenance operation on at least a portion of the industrial machine based on the captured vibration data. In an embodiment, the method further includes processing the captured vibration data to determine an amplitude and a gravity of the captured vibration data. In an embodiment, calculating the severity unit of the captured vibration includes calculating the severity unit based on the determined segment and at least one of the amplitude or gravity. In an embodiment, the severity units represent captured vibrations that are frequency independent. In an embodiment, at least one of the signal or the maintenance action indicates increasing or decreasing a frequency for further vibration data collection and analysis using at least one vibration sensor based on the severity units. In an embodiment, the maintenance action indicates performing one of a calibration, a diagnostic test, or a visual inspection on at least a portion of the industrial machine. In an embodiment, the method further includes sending a signal to a component of the industrial machine. In an embodiment, the maintenance action indicates resurveying at least a portion of the industrial machine. In an embodiment, the component of the industrial machine causes the maintenance action to be performed in response to receiving the signal. In an embodiment, the wearable device is a first wearable device of multiple wearable devices integrated within an industrial platform.In an embodiment, a second wearable device of the plurality of wearable devices uses a temperature sensor to capture the temperature of the industrial machine. In an embodiment, the signal is generated based on severity units, the second severity units being calculated based on the captured temperature. In an embodiment, a third wearable device of the plurality of wearable devices uses an electrical sensor to capture the electrical output or usage of the industrial machine. In an embodiment, the signal is generated based on a third severity unit calculated based on the captured electrical output or usage based on severity units. In an embodiment, a fourth wearable device of the plurality of wearable devices uses a magnetic sensor to capture the level or change of an electromagnetic field of the industrial machine. In an embodiment, the signal is generated based on a fourth severity unit calculated based on the level or change of the captured electromagnetic field based on severity units. In an embodiment, a fifth wearable device of the plurality of wearable devices uses a sound sensor to capture sound waves output from the industrial machine. In an embodiment, the signal is generated based on a fifth severity unit calculated based on the captured sound waves based on severity units. In an embodiment, the wearable device is a first wearable device integrated within an article of clothing. In an embodiment, the method further includes using a second wearable device integrated within an article of accessory.

[0056] In an embodiment, a method includes deploying a mobile data collector to detect and monitor vibration activity of at least a portion of an industrial machine, the mobile data collector including one or more vibration sensors; processing vibration data representative of the vibration activity, determining a severity of the vibration activity relative to a timing by processing the vibration data generated using the one or more vibration sensors; and predicting one or more maintenance actions to be performed on at least the portion of the industrial machine based on the severity of the vibration activity. In an embodiment, processing the vibration data representative of the vibration activity, processing the vibration data generated using the one or more vibration sensors to determine a severity of the vibration activity relative to a timing includes: processing the vibration data to determine a frequency of the vibration activity; determining a segment of a multi-segment vibration frequency spectrum that bounds the vibration activity based on the frequency; and calculating severity units of the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum. In an embodiment, calculating severity units of the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum includes: mapping the vibration activity to severity units based on the determined segment of the multi-segment vibration frequency spectrum. mapping the vibration activity to a first severity unit if the frequency of the vibration activity corresponds to below a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration activity to a second severity unit if the frequency of the vibration activity corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration activity to a third severity unit if the frequency of the vibration activity corresponds to above a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes causing at least one of the mobile data collectors to perform a maintenance operation. In an embodiment, the method further includes:The method includes controlling a mobile data collector to approach a location of the industrial machine within an industrial environment including the industrial machine, causing one or more vibration sensors of the mobile data collector to record one or more measurements of vibration activity, and transmitting the one or more measurements of vibration activity as vibration data to a server over a network. In an embodiment, the vibration data is processed by the server to determine a severity of the vibration activity. In an embodiment, predicting one or more maintenance actions to be performed on at least a portion of the industrial machine based on the severity of the vibration activity includes processing the vibration data against pre-recorded data for the industrial machine using an intelligent system associated with the server. In an embodiment, processing the vibration data against pre-recorded data for the industrial machine includes identifying pre-recorded data for the industrial machine in a knowledge base associated with the industrial environment, identifying operating characteristics of at least a portion of the machine based on the pre-recorded data for the industrial machine in the knowledge base, and predicting one or more maintenance actions based on the operating characteristics. In an embodiment, the vibration activity represents a waveform derived from a vibration envelope associated with the industrial machine. In an embodiment, one or more vibration sensors detect the vibration activity when the mobile data collector is in proximity to the industrial machine. In an embodiment, the vibration activity represents speed information of at least a portion of the industrial machine. In an embodiment, the vibration activity represents frequency information about at least a portion of the industrial machine. In an embodiment, the mobile data collector is a mobile robot. In an embodiment, the mobile data collector is a mobile vehicle. In an embodiment, the mobile data collector is one of a plurality of mobile data collectors of a mobile data collector fleet. In an embodiment, the method further includes controlling movement of the mobile data collectors within an industrial environment including the industrial machine using a self-organizing system of the mobile data collector fleet. In an embodiment, the one or more vibration sensors detect vibration activity when the mobile data collector is in proximity to the industrial machine.In an embodiment, using the self-organizing system of mobile data collectors to control movement of a mobile data collector within an industrial environment includes controlling movement of the mobile data collector within the industrial environment based on movement of at least one other mobile data collector of the plurality of mobile data collectors. In an embodiment, the mobile data collector is a mobile robot and the at least one other mobile data collector of the plurality of mobile data collectors is a mobile vehicle.

[0057] In an embodiment, an industrial machinery predictive maintenance system includes one or more mobile data collectors configured to collect health monitoring data representative of the condition of one or more industrial machines located in an industrial environment. The system includes the mobile data collector fleet, an industrial machinery predictive maintenance facility that applies machine fault detection and classification algorithms to generate industrial machine service recommendations in response to the health monitoring data, and a computerized maintenance management system (CMMS) that generates at least one of service and parts orders and requests in response to receiving the industrial machine service recommendations. In an embodiment, the industrial machinery predictive maintenance system further includes a service and delivery coordination facility that receives and processes information regarding services to be performed on the industrial machines in response to the at least one of the service and parts orders and requests, thereby verifying the services performed while generating a ledger of service activities and results for individual industrial machines. In an embodiment, the ledger tracks transaction records for each of the orders and at least one of the service and parts requests using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the CMMS generates subsequent blocks in the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, parts orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger. In an embodiment, the industrial machinery predictive maintenance system further includes a self-organizing system that controls movement of one or more mobile data collectors within the industrial environment. In an embodiment, the self-organizing system sends requests for health monitoring data to the one or more mobile data collectors. In an embodiment, the mobile data collectors send the health monitoring data to the self-organizing system in response to the request. In an embodiment, the self-organizing system sends the health monitoring data to the industrial machinery predictive maintenance facility. In an embodiment, the industrial machinery predictive maintenance system further includes a data collection router that receives health monitoring data from the one or more mobile data collectors when the mobile data collectors are in proximity to the data collectors.In an embodiment, the data collection router transmits the health monitoring data to the industrial machine predictive maintenance facility. In an embodiment, one or more mobile data collectors push the health monitoring data to the data collection router. In an embodiment, the data collection router pulls the health monitoring data from the one or more mobile data collectors. In an embodiment, the industrial machine predictive maintenance system further comprises a self-organizing system that controls movement of the one or more mobile data collectors within the industrial environment. In an embodiment, the self-organizing system controls communication of the health monitoring data from the one or more mobile data collectors to the data collection router. In an embodiment, each mobile data collector of the one or more mobile data collectors is one of a mobile robot including one or more integrated sensors, a mobile robot including one or more coupled sensors, a mobile vehicle with one or more integrated sensors, or a mobile vehicle with one or more coupled sensors. In an embodiment, the industrial machine predictive maintenance facility generates an industrial machine service recommendation based on severity units calculated for the health monitoring data.

[0058] In an embodiment, the system comprises a plurality of wearable devices integrated into an industrial uniform, each wearable device of the industrial uniform comprising one or more sensors that collect measurements from an industrial machine located in an industrial environment, the measurements being representative of the condition of the industrial machine; an industrial machine predictive maintenance facility that generates service recommendations for the industrial machine based on the measurements by applying an industrial machine fault detection and classification algorithm; and a computerized maintenance management system (CMMS) that generates at least one of service and parts orders and requests in response to receiving the service recommendations for the industrial machine. In an embodiment, the system further includes a service and delivery coordination facility that receives and processes information regarding services performed on the industrial machine in response to the at least one of the service and parts orders and requests, thereby verifying the services performed while generating a ledger of service activities and results for each industrial machine. In an embodiment, the ledger tracks transaction records for each of the orders and at least one of the service and parts requests using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In embodiments, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, parts orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger. In embodiments, the one or more sensors of the first wearable device of the industrial uniform include a sensor configured to collect vibration measurements from at least one of the industrial machines. In embodiments, the one or more sensors of the second wearable device of the industrial uniform include a sensor configured to collect temperature measurements from at least one of the industrial machines. In embodiments, the one or more sensors of the first wearable device of the industrial uniform include a sensor configured to collect electrical measurements from at least one of the industrial machines.In an embodiment, the one or more sensors of a first wearable device of the industrial uniform include a sensor configured to collect magnetic measurements from at least one of the industrial machines. In an embodiment, the one or more sensors of the first wearable device of the industrial uniform include a sensor configured to collect sound measurements from at least one of the industrial machines. In an embodiment, the first wearable device of the industrial uniform is an article of clothing, and the second wearable device of the industrial uniform is an article of accessory. In an embodiment, the system further comprises a collective processing mind that controls the collection of measurements of the one or more industrial machines by the multiple wearable devices. In an embodiment, the collective processing mind sends a first command to the wearable devices of the industrial uniform, causing one or more sensors of the wearable devices to collect measurements of the one or more industrial machines. In an embodiment, the collective processing mind sends a second command to the wearable devices, causing the wearable devices to transmit the measurements to the collective processing mind. In an embodiment, the industrial machine predictive maintenance facility generates industrial machine service recommendations based on severity units calculated for the measurements.

[0059] In an embodiment, the system comprises: a plurality of wearable devices integrated within an industrial uniform, each wearable device of the industrial uniform configured with one or more sensors that collect measurements from an industrial machine disposed in an industrial environment, the measurements being representative of the condition of the industrial machine; an industrial machine predictive maintenance facility that applies machine fault detection and classification algorithms to generate industrial machine service recommendations based on the measurements; a computerized maintenance management system (CMMS) that generates at least one of a service and part order and request in response to receiving the industrial machine service recommendation; and a service and delivery coordination facility that receives and processes information regarding services performed on the industrial machine in response to the at least one of the service and part order and request, thereby verifying the services performed while generating a ledger of service activities and results for the individual industrial machine. In an embodiment, the industrial machine predictive maintenance facility generates the industrial machine service recommendations based on severity units calculated for the measurements. In an embodiment, the ledger tracks records of transactions for each of the at least one of the service and part order and request using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure.

[0060] In an embodiment, the system comprises one or more mobile data collectors configured to collect health monitoring data representative of the condition of one or more industrial machines disposed in an industrial environment. Additionally, the system comprises the mobile data collectors, an industrial machine predictive maintenance facility configured to apply machine fault detection and classification algorithms to generate industrial machine service recommendations in response to the health monitoring data, a computerized maintenance management system (CMMS) configured to generate service and parts orders and requests in response to receiving the industrial machine service recommendations, the computerized maintenance management system (CMMS) configured to generate at least one of the service and parts orders and requests in response to receiving the industrial machine service recommendations, and a service and delivery coordination facility configured to receive and process information regarding services performed on the industrial machines in response to at least one of the service and parts orders and requests, thereby verifying the services performed while generating a ledger of service activities and results for individual industrial machines. In an embodiment, the industrial machine predictive maintenance facility generates the industrial machine service recommendations based on severity units calculated for the health monitoring data. In an embodiment, the ledger tracks records of transactions for each of the orders and at least one of the service and parts requests using a blockchain structure. In an embodiment, each record is stored as a block in a blockchain structure.

[0061] In an embodiment, the method includes generating vibration data representing measured vibrations of at least a portion of an industrial machine using one or more vibration sensors in a handheld device, mapping the vibration data to one or more severity units, and determining a maintenance action to be performed on at least the portion of the industrial machine based on the severity units, thereby using the severity units for predictive maintenance of the industrial machine. In an embodiment, mapping the vibration data to the one or more severity units includes: mapping a portion of the vibration data having frequencies corresponding to a low-end knee threshold range of a vibration frequency spectrum to a first severity unit, mapping a portion of the vibration data having frequencies corresponding to a mid-range of the vibration frequency spectrum to a second severity unit, and mapping a portion of the vibration data having frequencies corresponding to a high-end knee threshold range of the vibration frequency spectrum to a third severity unit. In an embodiment, the mapping of the vibration data to the one or more severity units is performed on the handheld device. In an embodiment, the mapping of the vibration data to the one or more severity units is performed on a server. In an embodiment, the method further includes transmitting the vibration data from the handheld device to the server. In an embodiment, the method further includes using a collective processing mind associated with the handheld device to detect that the handheld device is in proximity to an industrial machine, sending a first command from the collective processing mind to cause the handheld device to generate vibration data, and after generating the vibration data, sending a second command from the collective processing mind to cause the handheld device to transmit the vibration data to the collective processing mind.

[0062] In an embodiment, the system comprises at least one vibration sensor positioned to capture vibrations of a portion of an industrial machine, the industrial machine, a mobile data collector configured to collect captured vibrations from the at least one vibration sensor to generate vibration data, a multi-segment vibration frequency spectrum structure configured to facilitate mapping the captured vibrations to one of the plurality of vibration frequency segments, a severity unit algorithm configured to receive the determined frequency of the vibration and the corresponding mapped segment and generate a severity value mapped to one of a plurality of severity units defined for the corresponding mapped segment, and a signal generation circuit configured to receive the one of the plurality of severity units and, based thereon, signal a predictive maintenance server to perform a corresponding maintenance action on the portion of the industrial machine.

[0063] Specifically, the method includes using a distributed ledger to track one or more transactions performed in an automated data marketplace for industrial Internet of Things (IoT) data. In embodiments, the distributed ledger distributes storage for data indicative of one or more transactions between one or more devices. In embodiments, the data indicative of the one or more transactions corresponds to a transaction record, and one or more mobile data collectors are used to generate sensor data representative of a condition of an industrial machine. In embodiments, the sensor data is used to determine at least one of an order or request for service and parts used to resolve an issue associated with the condition of the machine. In embodiments, the transaction record stored in the distributed ledger represents one or more of the sensor data, at least one of the condition of the industrial machine, an order or request for service and parts, the issue associated with the condition of the machine, or a hash used to identify the transaction record. In embodiments, the distributed ledger stores the transaction records using a blockchain structure. In embodiments, each of the transaction records is stored as a block in the blockchain structure. In embodiments, each mobile data collector is one of a mobile vehicle, a mobile robot, a handheld device, or a wearable device. In an embodiment, the method further comprises applying a machine fault detection and classification algorithm to the sensor data to generate an industrial machine service recommendation, and generating at least one of an order or a service and parts request based on the industrial machine service recommendation. In an embodiment, the one or more mobile data collectors generate the sensor data using a computer vision system by capturing raw image data using one or more data capture devices and processing the raw image data to generate image set data. In an embodiment, the image set data is used to generate the industrial machine service recommendation.

[0064] In an embodiment, a system comprises an IoT network connecting industrial machines and one or more mobile data collectors, each mobile data collector including one or more sensors for generating sensor data indicative of a condition of the industrial machine, a server in communication with the IoT network, the server implementing a predictive maintenance platform using a distributed ledger to track maintenance transactions related to the industrial machines, the distributed ledger storing transaction records corresponding to the maintenance transactions. In an embodiment, the predictive maintenance platform distributes at least a portion of the transaction records to the one or more mobile data collectors. In an embodiment, the system further comprises a self-organizing storage system that optimizes storage of the transaction records in the distributed ledger. In an embodiment, the system further comprises a self-organizing storage system that optimizes storage of maintenance data related to the industrial machines. In an embodiment, the system further comprises a self-organizing storage system that optimizes storage of IoT data associated with the IoT network. In an embodiment, the system further comprises a self-organizing storage system that optimizes storage of parts and service data related to the maintenance transactions. In an embodiment, the system further comprises a self-organizing storage system that optimizes storage of knowledge base data related to the industrial machines. In an embodiment, each mobile data collector is one of a mobile vehicle, a mobile robot, a handheld device, or a wearable device. In an embodiment, the system further configures an industrial machine predictive maintenance facility that generates an industrial machine service recommendation for the condition by applying a machine fault detection and classification algorithm to the sensor data. In an embodiment, the system further configures a severity unit algorithm that generates a severity value for the condition based on the sensor data. In an embodiment, the industrial machine service recommendation is generated based on the severity value.In an embodiment, at least one of the one or more mobile data collectors uses a computer vision system to generate the sensor data by capturing raw image data using the one or more data collectors and processing the raw image data to generate image set data. In an embodiment, the image set data is used to generate industrial machine service recommendations.

[0065] In an embodiment, a method comprises generating sensor data representative of a condition of an industrial machine using a mobile data collector, analyzing the sensor data to determine a severity of the condition of the industrial machine, predicting a maintenance action to be performed on the industrial machine based on the severity of the condition, and storing a transaction record of the predicted maintenance action in a service activity ledger associated with the industrial machine. In an embodiment, the method further includes: generating, in association with the predicted maintenance action, at least one of an order or request for service and parts to be used to perform the maintenance action, and including data indicative of the at least one of the order or request for service and parts in the transaction record. In an embodiment, the mobile data collector is one of a mobile vehicle, a mobile robot, a handheld device, or a wearable device. In an embodiment, the method further includes applying machine learning to the data representative of the condition of the industrial machine. In an embodiment, analyzing the frequency of vibration to determine the severity of the sensor data includes using applied machine learning to determine the severity of the sensor data based on machine learning data associated with at least one of the frequency or speed of the vibration.

[0066] In an embodiment, an industrial machinery predictive maintenance system includes: a computer vision system that generates one or more image datasets using raw data captured by one or more data capture devices and detects operational characteristics of the industrial machine based on the one or more image datasets; an industrial machinery predictive maintenance facility that generates industrial machine service recommendations by applying a machine fault detection and classification algorithm to the data indicative of the operational characteristics; a computerized maintenance management system (CMMS) that generates at least one of service and parts orders and requests responsive to receiving the industrial machine service recommendations; and a service and delivery coordination facility that receives and processes information regarding services to be performed on the industrial machine based on the at least one of the service and parts orders and requests. In an embodiment, the service and delivery coordination facility verifies the services to be performed on the industrial machine while creating a ledger of service activities and results for the industrial machine. In an embodiment, the ledger uses a blockchain structure to track records of transactions for each of the orders and at least one of the service and parts requests. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, part orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger. In an embodiment, the industrial machinery predictive maintenance facility generates industrial machine service recommendations using data stored in a knowledge base associated with the industrial machine. In an embodiment, the operating characteristic is related to detected vibrations for at least a portion of the industrial machine. In an embodiment, the industrial machinery predictive maintenance facility generates industrial machine service recommendations according to severity units calculated for the detected vibrations. In an embodiment, the severity units are calculated for the detected vibrations by determining a frequency of the detected vibrations, determining a segment of a multi-segment vibration frequency spectrum that bounds the detected vibrations, and calculating severity units for the detected vibrations based on the determined segment.In an embodiment, a segment of the multi-segment vibration frequency spectrum that constrains the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a first severity unit if the captured frequency of the vibration corresponds to a low-end knee threshold range or less of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a second severity unit if the captured frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a third severity unit if the captured frequency of the vibration corresponds to a high-end knee threshold range or more of the multi-segment vibration frequency spectrum. In an embodiment, the severity unit indicates that the detected vibration may lead to a failure of at least a portion of the industrial machinery. In an embodiment, the industrial machinery service recommendation includes a recommendation to prevent or mitigate the failure. In an embodiment, at least one of the order and the service request is for a part or service used to prevent or mitigate the failure. In an embodiment, the one or more data capture devices are external to the computer vision system. In an embodiment, the industrial machine predictive maintenance system further includes a mobile data collector configured to perform a maintenance action on the industrial machine corresponding to a service recommendation for the industrial machine using at least one of the order, service request, and part. In an embodiment, the service and delivery coordination facility receives a signal from the mobile data collector indicating the performance of the maintenance action. In an embodiment, the service and delivery coordination facility uses a ledger to record service activities and results for the industrial machine. In an embodiment, the service and delivery coordination facility generates a new record in the ledger based on the signal received from the mobile data collector.

[0067] In an embodiment, the industrial machine predictive maintenance system comprises: a computer vision system that generates one or more image datasets using raw data captured by one or more data capture devices and detects operational characteristics of the industrial machine based on the one or more image datasets; an industrial machine predictive maintenance facility that applies machine fault detection and classification algorithms to the data indicative of the operational characteristics to generate industrial machine service recommendations; and a computerized maintenance management system (CMMS) that generates at least one of service and parts orders and requests in response to receiving the industrial machine service recommendations. In an embodiment, the industrial machine predictive maintenance system further includes a service and delivery coordination facility that receives and processes information regarding services to be performed on the industrial machine based on the at least one of the service and parts orders and requests. In an embodiment, the service and delivery coordination facility verifies the services to be performed on the industrial machine while creating a ledger of service activities and results for the industrial machine. In an embodiment, the ledger tracks records of transactions for each of the orders and at least one of the service and parts requests using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, part orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger. In an embodiment, the industrial machinery predictive maintenance facility generates industrial machine service recommendations using data stored in a knowledge base associated with the industrial machine. In an embodiment, the operating characteristic is related to vibrations detected for at least a portion of the industrial machine. In an embodiment, the industrial machinery predictive maintenance facility generates industrial machine service recommendations according to severity units calculated for the detected vibrations.In an embodiment, a severity unit is calculated for a detected vibration by determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum that bounds the detected vibration, and calculating a severity unit for the detected vibration based on the determined segment. In an embodiment, the segment of the multi-segment vibration frequency spectrum that bounds the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a first severity unit if the captured frequency of the vibration corresponds to a low-end knee threshold range or less of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a second severity unit if the captured frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a third severity unit if the captured frequency of the vibration corresponds to a high-end knee threshold range or greater of the multi-segment vibration frequency spectrum. In an embodiment, the severity unit indicates that the detected vibrations may lead to a failure of at least a portion of the industrial machine. In an embodiment, the industrial machine service recommendation includes a recommendation to prevent or mitigate the failure. In an embodiment, at least one of the order and the service request is for a part or service used to prevent or mitigate the failure. In an embodiment, the one or more data capture devices are external to the computer vision system. In an embodiment, the industrial machine predictive maintenance system further includes a mobile data collector configured to perform a maintenance action on the industrial machine corresponding to the service recommendation for the industrial machine using at least one of the order and the service request and the part. In an embodiment, the service and delivery coordination facility receives a signal from the mobile data collector indicating the performance of the maintenance action. In an embodiment, the service and delivery coordination facility uses a ledger to record service activities and results for the industrial machine.In embodiments, the service and delivery coordination facility generates a new record in a ledger based on a signal received from the mobile data collector. In embodiments, the mobile data collector is a mobile vehicle. In embodiments, the mobile data collector is a mobile robot. In embodiments, the mobile data collector is a handheld device. In embodiments, the mobile data collector is a wearable device.

[0068] In an embodiment, the industrial machine predictive maintenance system comprises: a computer vision system that generates one or more image datasets using raw data captured by one or more data capture devices and detects operational characteristics of the industrial machine based on the one or more image datasets; an industrial machine predictive maintenance facility that generates an industrial machine service recommendation based on the operational characteristics; and a mobile data collector configured to perform a maintenance action corresponding to the industrial machine service recommendation for the industrial machine. In an embodiment, the mobile data collector is one mobile data collector of a fleet of mobile data collectors, and the industrial machine predictive maintenance system further comprises a self-organizing system for a fleet of mobile data collectors configured to control movement of the mobile data collectors of the fleet within an industrial environment including the industrial machine. In an embodiment, the industrial machine predictive maintenance facility generates the industrial machine service recommendation by applying a machine fault detection and classification algorithm to data indicative of the operational characteristics. In an embodiment, the industrial machine predictive maintenance facility creates the industrial machine service recommendation using data stored in a knowledge base associated with the industrial machine. In an embodiment, the operational characteristics are related to vibrations detected for at least a portion of the industrial machine. In an embodiment, an industrial machinery predictive maintenance facility generates industrial machinery service recommendations according to severity units calculated for the detected vibration. In an embodiment, the severity units are calculated for the detected vibration by determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum that bounds the detected vibration, and calculating severity units for the detected vibration based on the determined segment. In an embodiment, the segment of the multi-segment vibration frequency spectrum that bounds the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum.In an embodiment, the detected vibration is mapped to a first severity unit if the frequency of the captured vibration corresponds to a low-end knee threshold range or less of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a second severity unit if the frequency of the captured vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum. In an embodiment, the detected vibration is mapped to a third severity unit if the frequency of the captured vibration corresponds to a high-end knee threshold range or more of the multi-segment vibration frequency spectrum. In an embodiment, the severity unit indicates that the detected vibration may lead to a failure of at least a portion of the industrial machine. In an embodiment, the industrial machine service recommendation includes a recommendation for preventing or mitigating the failure. In an embodiment, the industrial machine predictive maintenance system further comprises a computerized maintenance management system (CMMS) that generates at least one of a service and parts order and request in response to receiving the industrial machine service recommendation. In an embodiment, the mobile data collector performs a maintenance action using at least one of the service and parts order and request. In an embodiment, the industrial machinery predictive maintenance system further includes a service and delivery coordination facility that receives and processes information regarding services to be performed on the industrial machine based on at least one of orders and requests for service and parts. In an embodiment, the service and delivery coordination facility verifies services to be performed on the industrial machine while generating a ledger of service activities and results for the industrial machine. In an embodiment, the ledger tracks transaction records for each of the orders and at least one of service and part requests using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, parts orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger.

[0069] In an embodiment, a method for predictive maintenance of an industrial machine includes generating data representative of a condition of the industrial machine using one or more sensors of a mobile data collector, processing the data to determine a severity of the industrial machine condition, determining an industrial machine service recommendation for the industrial machine condition based on the severity, and generating a signal indicative of the industrial machine service recommendation. In an embodiment, the mobile data collector uses a computer vision system to generate one or more image data sets using raw data captured by one or more data capture devices as data and detects an operating characteristic of the industrial machine based on the one or more image data sets. In an embodiment, the operating characteristic corresponds to the condition of the industrial machine. In an embodiment, the mobile data collector is a mobile robot. In an embodiment, the mobile data collector is a mobile vehicle. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the mobile data collector is a wearable device. In an embodiment, determining the industrial machine service recommendation for the industrial machine condition based on the severity includes using an intelligent system to apply a machine fault detection and classification algorithm to the data and the severity. In an embodiment, the condition of the industrial machine is associated with vibrations detected for at least a portion of the industrial machine, and processing the data to determine a severity of the condition of the industrial machine includes determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum that bounds the detected vibration, and calculating a severity of the detected vibration based on the determined segment. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum that bounds the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum.In an embodiment, the method further comprises mapping the detected vibration to a first severity unit if the frequency of the detected vibration corresponds to below a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the detected vibration to a second severity unit if the frequency of the detected vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the detected vibration to a third severity unit if the frequency of the detected vibration corresponds to at least a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes transmitting a signal to a mobile robot configured to perform a maintenance operation associated with the industrial machine service recommendation. In an embodiment, the method further includes storing a record of the industrial machine service recommendation in a ledger of service activities related to the industrial machine. In an embodiment, the ledger tracks records of industrial machine service recommendations for the industrial machine using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the method further includes generating at least one of a service and part order or request based on the industrial machine service recommendation. In an embodiment, the signal indicates at least one of an order or a request for service and parts.

[0070] In an embodiment, a method for predictive maintenance of an industrial machine includes generating data representative of a condition of the industrial machine using one or more wearable devices, each wearable device including one or more sensors. In an embodiment, a wearable device of the one or more wearable devices generates some or all of the data when the wearable device is in proximity to the industrial machine, processes the data to determine a severity of the industrial machine condition, determines an industrial machine service recommendation for the industrial machine condition based on the severity, and stores a record of the industrial machine service recommendation in a service activity ledger associated with the industrial machine. In an embodiment, the industrial machine condition is related to vibrations detected for at least a portion of the industrial machine, and processing the data to determine a severity of the industrial machine condition includes determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum bounding the detected vibration, and calculating a severity of the detected vibration based on the determined segment. In an embodiment, the severity corresponds to a severity unit. In embodiments, a segment of the multi-segment vibration frequency spectrum that constrains the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In embodiments, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In embodiments, the method further includes mapping the detected vibration to a first severity unit if the frequency of the detected vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the detected vibration to a second severity unit if the frequency of the detected vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the detected vibration to a third severity unit if the frequency of the detected vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum.In embodiments, determining industrial machine service recommendations for the condition of the industrial machine based on the severity includes applying a machine fault detection and classification algorithm to the data and the severity using an intelligent system. In embodiments, the intelligent system includes a YOLO (You Only Look Once) neural network. In embodiments, the intelligent system includes a look-once convolutional neural network. In embodiments, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array. In embodiments, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array and graphics processing unit hybrid component. In embodiments, the intelligent system includes user-configurable serial and parallel flows for the hybrid neural networks. In embodiments, the intelligent system includes a machine learning system for configuring a topology or workflow for the set of neural networks. In embodiments, the intelligent system includes a deep learning system for configuring a topology or workflow for the set of neural networks. In embodiments, the ledger tracks records of industrial machine service recommendations for the industrial machine using a blockchain structure. In an embodiment, each record is stored as a block in a blockchain structure. In an embodiment, the method further includes generating at least one of a service and parts order or request based on the industrial machine service recommendation. In an embodiment, the record for the industrial machine service recommendation stored in the ledger indicates at least one of the service and parts order or request. In an embodiment, the one or more wearable devices are integrated into an industrial uniform. In an embodiment, the wearable device is integrated into an article of clothing. In an embodiment, the wearable device is incorporated into an accessory article.

[0071] In an embodiment, a method for predictive maintenance of an industrial machine includes generating data representative of a condition of the industrial machine using one or more handheld devices, each handheld device including one or more sensors. In an embodiment, a handheld device of the one or more handheld devices generates some or all of the data when the handheld device is in proximity to the industrial machine, processes the data to determine a severity of the industrial machine condition, determines an industrial machine service recommendation for the industrial machine condition based on the severity, and stores a record of the industrial machine service recommendation in a service activity ledger associated with the industrial machine. In an embodiment, the industrial machine condition is related to vibrations detected for at least a portion of the industrial machine, and processing the data to determine a severity of the industrial machine condition includes: determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum that bounds the detected vibration, and calculating a severity of the detected vibration based on the determined segment. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum that bounds the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes mapping the detected vibration to a first severity unit if the frequency of the detected vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the detected vibration to a second severity unit if the frequency of the detected vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the detected vibration to a third severity unit if the frequency of the detected vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, determining an industrial machine service recommendation for the industrial machine condition based on the severity includes applying a machine fault detection and classification algorithm to the data and the severity using an intelligent system.In an embodiment, the intelligent system includes a YOLO neural network. In an embodiment, the intelligent system includes a look-once convolutional neural network. In an embodiment, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array. In an embodiment, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array and graphics processing unit hybrid component. In an embodiment, the intelligent system includes user-configurable serial and parallel flows for the hybrid neural network. In an embodiment, the intelligent system includes a machine learning system for configuring a topology or workflow for the set of neural networks. In an embodiment, the intelligent system includes a deep learning system for configuring a topology or workflow for the set of neural networks. In an embodiment, the ledger tracks records of industrial machine service recommendations for the industrial machine using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the method further includes generating at least one of a service and part order or request based on the service recommendation for the industrial machine. In an embodiment, a record for an industrial machine service recommendation stored in the ledger indicates at least one of an order or request for service and parts.

[0072] In an embodiment, a method for predictive maintenance of an industrial machine includes: generating data representing a condition of the industrial machine using one or more mobile robots, each mobile robot including one or more sensors. In an embodiment, the mobile robots of the one or more mobile robots generate some or all of the data when the mobile robots are in proximity to the industrial machine, processing the data to determine a severity of the industrial machine condition, determining an industrial machine service recommendation for the industrial machine condition based on the severity, and storing a record of the industrial machine service recommendation in a service activity ledger associated with the industrial machine. In an embodiment, the industrial machine condition is related to vibrations detected for at least a portion of the industrial machine, and processing the data to determine a severity of the industrial machine condition includes: determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum bounding the detected vibration, and calculating a severity of the detected vibration based on the determined segment. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum bounding the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the method further comprises mapping the detected vibration to a first severity unit if the frequency of the detected vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the detected vibration to a second severity unit if the frequency of the detected vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the detected vibration to a third severity unit if the frequency of the detected vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, determining an industrial machine service recommendation for the industrial machine condition based on the severity includes applying a machine fault detection and classification algorithm to the data and the severity using the intelligent system.In an embodiment, the intelligent system includes a YOLO neural network. In an embodiment, the intelligent system includes a look-once convolutional neural network. In an embodiment, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array. In an embodiment, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array and graphics processing unit hybrid component. In an embodiment, the intelligent system includes user-configurable serial and parallel flows for the hybrid neural networks. In an embodiment, the intelligent system includes a machine learning system for configuring a topology or workflow for the set of neural networks. In an embodiment, the intelligent system includes a deep learning system for configuring a topology or workflow for the set of neural networks. In an embodiment, the ledger tracks records of industrial machine service recommendations for the industrial machine using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the method further includes generating at least one of a service and part order or request based on the service recommendation for the industrial machine. In an embodiment, the record for the industrial machine service recommendation stored in the ledger indicates at least one of a service and part order or request. In an embodiment, the mobile robot is one of a plurality of mobile robots of a mobile data collection swarm. In an embodiment, the method further includes controlling the mobile data collection swarm so that the mobile robot approaches a location of the industrial machine in the industrial environment. In an embodiment, controlling the mobile data collector swarm so that the mobile robot approaches a location of the industrial machine in the industrial environment includes controlling movement of the mobile robot in the industrial environment based on positions of other mobile robots of the mobile data collector swarm in the industrial environment using a self-organizing system of the mobile data collector swarm.

[0073] In an embodiment, a method for predictive maintenance of an industrial machine includes: generating data representing a condition of the industrial machine using one or more mobile bodies, each mobile body including one or more sensors. In an embodiment, the mobile bodies of the one or more mobile bodies generate some or all of the data when the mobile bodies are in proximity to the industrial machine, processing the data to determine a severity of the industrial machine condition, determining an industrial machine service recommendation for the industrial machine condition based on the severity, and storing a record of the industrial machine service recommendation in a service activity ledger associated with the industrial machine. In an embodiment, the industrial machine condition is related to vibrations detected for at least a portion of the industrial machine, and processing the data to determine a severity of the industrial machine condition includes: determining a frequency of the detected vibration, determining a segment of a multi-segment vibration frequency spectrum bounding the detected vibration, and calculating a severity of the detected vibration based on the determined segment. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum bounding the detected vibration is determined by mapping the detected vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes mapping the detected vibration to a first severity unit if the frequency of the detected vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the detected vibration to a second severity unit if the frequency of the detected vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the detected vibration to a third severity unit if the frequency of the detected vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, determining an industrial machine service recommendation for the industrial machine condition based on the severity includes applying a machine fault detection and classification algorithm to the data and the severity using an intelligent system.In an embodiment, the intelligent system includes a YOLO neural network. In an embodiment, the intelligent system includes a look-once convolutional neural network. In an embodiment, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array. In an embodiment, the intelligent system includes a set of neural networks configured to operate on or from a field programmable gate array and graphics processing unit hybrid component. In an embodiment, the intelligent system includes user-configurable serial and parallel flows for the hybrid neural networks. In an embodiment, the intelligent system includes a machine learning system for configuring a topology or workflow for the set of neural networks. In an embodiment, the intelligent system includes a deep learning system for configuring a topology or workflow for the set of neural networks. In an embodiment, the ledger tracks records of industrial machine service recommendations for the industrial machine using a blockchain structure. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the method further includes generating at least one of a service and part order or request based on the service recommendation for the industrial machine. In an embodiment, the record for the industrial machine service recommendation stored in the ledger indicates at least one of a service and part order or request. In an embodiment, the mobile vehicle is one of a plurality of mobile vehicles of a mobile data collection swarm. In an embodiment, the method further includes controlling the mobile data collection swarm to cause the mobile to approach the location of the industrial machine in the industrial environment. In an embodiment, controlling the mobile data collection swarm to cause the mobile to approach the location of the industrial machine in the industrial environment includes controlling movement of the mobile within the industrial environment based on positions of other mobiles of the mobile data collection swarm within the industrial environment using a self-organizing system of the mobile data collection swarm.

[0074] In one embodiment, the method comprises the steps of: training a computer vision system to detect a condition of an industrial machine using a training dataset including at least one of image data and non-image data; detecting the condition of the industrial machine based on a dataset generated using one or more data capture devices using the trained computer vision; determining a severity value for the detected condition; and determining the severity value representing an impact of the detected condition on the industrial machine. Based on the severity value, generating at least one of a service and parts order or request for use in resolving an issue associated with the detected condition of the industrial machine, and storing a record of the issue associated with the detected condition of the industrial machine in a ledger associated with the industrial machine. In an embodiment, the one or more data capture devices include a radiographic device, a sound wave capture device, a LIDAR device, a point cloud capture device, or an infrared inspection device. In an embodiment, the detected condition is detected based on a vibration characteristic of the industrial machine. In an embodiment, the detected condition is detected based on a pressure characteristic of the industrial machine. In an embodiment, the detected condition is detected based on a temperature characteristic of the industrial machine. In an embodiment, the detected condition is detected based on a chemical characteristic of the industrial machine. In an embodiment, the training dataset includes at least one of image data or non-image data. Training a computer vision system to detect the condition of an industrial machine using the training dataset comprises: using a deep learning system to detect features from the at least one of the image data or non-image data; and using the detected features to train a classification model to learn to detect the condition of the industrial machine based on features of the detected features and based on result feedback. In an embodiment, the result feedback relates to at least one of maintenance, repair, uptime, downtime, profitability, efficiency, or operational optimization of the industrial machine, a process for using the industrial machine, or equipment including the industrial machine.In an embodiment, using trained computer vision to detect a condition of the industrial machine based on a dataset generated using one or more data capture devices includes using part recognition to identify one or more components of the industrial machine that contribute to a problem associated with the detected condition. In an embodiment, at least one of the service and parts orders or requests is for a replacement part for the one or more components. In an embodiment, at least one of the orders or service and parts requests is not generated if the severity value does not meet a threshold. In an embodiment, the method further comprises updating a predictive maintenance knowledge base using a predictive maintenance knowledge system in response to at least one of the detected condition, the order or service and parts request, or at least one of the records stored in the ledger.

[0075] In an embodiment, the system includes a computerized maintenance management system (CMMS) that generates at least one of a service and parts order or request in response to receiving an industrial machine service recommendation corresponding to the industrial machine and generates a signal indicative of the generated at least one order or request; and a mobile data collector that receives the signal and presents the industrial machine service recommendation or the generated at least one order or request to a worker using the mobile data collector. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the wearable device presents the industrial machine service recommendation or the manufactured service and parts order or request to the worker by outputting data indicative of the industrial machine service recommendation or the manufactured service and parts order or request on a display of the wearable device. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the handheld device indicates at least one of the service recommendation for the industrial machine or the order or request for the manufactured service and parts to the worker by outputting data indicating at least one of the service recommendation for the industrial machine or the order or request for the manufactured service and parts on a display of the handheld device. In an embodiment, the system further includes a service and delivery coordination facility that receives and processes information regarding service performed on the industrial machine in response to the at least one of the service and parts orders or requests, thereby verifying the performed service while generating a ledger of service activities and results for the industrial machine. In an embodiment, the system further includes a self-organizing data collector that causes a new record to be stored in the ledger, the new record indicating the production of at least one of the service recommendation for the industrial machine or the order or request for the service and parts. In an embodiment, the ledger uses a blockchain structure to track records of transactions for each of the at least one of the orders and the service and parts requests. In an embodiment, each record is stored as a block in the blockchain structure.In an embodiment, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, parts orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger.

[0076] In an embodiment, the system includes: a computerized maintenance management system (CMMS) that generates at least one of a service and parts order or request in response to receiving an industrial machine service recommendation corresponding to the industrial machine and generates a signal indicative of the generated order or at least one of the service and parts request; a mobile data collector that receives the signal and presents the industrial machine service recommendation or at least one of the service and parts order or request produced to an operator using the mobile data collector; and a service and delivery coordination function that receives and processes information regarding service performed on the industrial machine in response to the at least one of the order or service and parts request, thereby verifying the service performed while generating a ledger of service activities and results for the industrial machine. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the wearable device presents the industrial machine service recommendation or at least one of the service and parts order or request produced to the operator by outputting data indicative of the industrial machine service recommendation or at least one of the service and parts order or request produced on a display of the wearable device. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the handheld device indicates at least one of the industrial machine service recommendation or the manufactured service and part order or request to the worker by outputting data indicating at least one of the industrial machine service recommendation or the manufactured service and part order or request on a display of the handheld device. In an embodiment, the system further includes a self-organizing data collector that stores a new record in the ledger, the new record indicating at least one of the industrial machine service recommendation or the manufactured service and part order or request. In an embodiment, the ledger uses a blockchain structure to track transaction records for each of the at least one of the order and the service and part request. In an embodiment, each record is stored as a block in the blockchain structure.In an embodiment, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, parts orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger.

[0077] In an embodiment, the system includes: a computerized maintenance management system (CMMS) that generates at least one of a service and parts order or request in response to receiving an industrial machine service recommendation corresponding to the industrial machine and generates a signal indicative of the generated order or at least one of the service and parts request; a mobile data collector that receives the signal and indicates the at least one of the industrial machine service recommendation or the order or request for the service and parts produced to a worker using the mobile data collector; and a self-organizing data collector that stores a new record in the ledger, the new record indicating the at least one of the industrial machine service recommendation or the order or request for the service and parts produced. In an embodiment, the ledger uses a blockchain structure to track records of transactions for each of the at least one of the orders and the service and parts request. In an embodiment, each record is stored as a block in the blockchain structure. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the wearable device indicates at least one of the industrial machine service recommendation or the service and parts order or request to the worker by outputting data on a display of the wearable device indicating that at least one of the industrial machine service recommendation or the service and parts order or request has been produced. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the handheld device indicates at least one of the industrial machine service recommendation or the manufactured service and parts order or request to the worker by outputting data on a display of the handheld device indicating at least one of the industrial machine service recommendation or the manufactured service and parts order or request. In an embodiment, the system further comprises a self-organizing data collector, wherein a new record is stored in the ledger, the new record indicating at least one of the industrial machine service recommendation or the manufactured service and parts order or request.In an embodiment, the CMMS generates subsequent blocks of the ledger by combining data from at least one of shipment preparation, installation, operational sensor data, service events, parts orders, service orders, or diagnostic activities with a hash of the most recently generated block in the ledger.

[0078] In an embodiment, a method includes detecting an operational characteristic of an industrial machine using one or more sensors of a mobile data collector, transmitting data indicative of the operational characteristic to a server over a network, and processing the operational characteristic against pre-recorded data for the industrial machine using an intelligent system associated with the server. In an embodiment, processing the operational characteristic against the pre-recorded data for the industrial machine includes identifying the pre-recorded data for the industrial machine in a knowledge base associated with an industrial environment and identifying the characteristic indicated by the pre-recorded data for the industrial machine in the knowledge base as a condition of the industrial machine. The method further includes determining a severity of the condition, the severity representing an impact of the condition on the industrial machine, predicting a maintenance action to be performed on the industrial machine based on the severity of the condition, and storing a transaction record of the predicted maintenance action in a service activity ledger associated with the industrial machine. In an embodiment, the mobile data collector is a mobile robot. In an embodiment, the mobile data collector is a mobile vehicle. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the condition of the industrial machine is associated with a vibration detected for at least a portion of the industrial machine, and determining the severity of the condition includes determining a frequency of the vibration, determining a segment of a multi-segment vibration frequency spectrum that bounds the vibration, and calculating a severity of the detected vibration based on the determined segment. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum that bounds the vibration is determined by mapping the vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum.In an embodiment, the method further comprises mapping the vibration to a first severity unit if the frequency of the vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration to a second severity unit if the frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration to a third severity unit if the frequency of the vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the ledger tracks transaction records for predicted maintenance actions for the industrial machine using a blockchain structure. In an embodiment, each transaction record is stored as a block in the blockchain structure. In an embodiment, the state of the industrial machine is related to a temperature detected for at least a portion of the industrial machine. In an embodiment, the state of the industrial machine is related to an electrical output detected for at least a portion of the industrial machine. In an embodiment, the state of the industrial machine is related to a magnetic output detected for at least a portion of the industrial machine. In an embodiment, the state of the industrial machine is related to an audio output detected for at least a portion of the industrial machine.

[0079] In an embodiment, a method includes detecting an operational characteristic of an industrial machine using one or more sensors of a mobile data collector, transmitting data indicative of the operational characteristic to a server over a network, and processing the operational characteristic against pre-recorded data for the industrial machine using an intelligent system associated with the server. In an embodiment, processing the operational characteristic against pre-recorded data for the industrial machine includes identifying the pre-recorded data for the industrial machine in a knowledge base associated with an industrial environment, identifying the characteristic indicated by the pre-recorded data for the industrial machine in the knowledge base as a state of the industrial machine, and identifying the state of the industrial machine associated with a vibration detected for at least a portion of the industrial machine. The method further includes determining a severity of the condition, the severity representing an impact of the condition on the industrial machine based on a segment of a multi-segment vibration frequency spectrum bounded by the vibration. The method further includes predicting a maintenance action to be performed on the industrial machine based on the severity of the condition. In an embodiment, the mobile data collector is a mobile robot. In an embodiment, the mobile data collector is a mobile vehicle. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the severity corresponds to a severity unit. In an embodiment, a segment of a multi-segment vibration frequency spectrum bounding the vibration is determined by mapping the vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum.In an embodiment, the method further comprises mapping the vibration to a first severity unit if the frequency of the vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration to a second severity unit if the frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration to a third severity unit if the frequency of the vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes storing transaction records of the predicted maintenance actions in a ledger of service activities associated with the industrial machine. In an embodiment, the ledger uses a blockchain structure to track transaction records for predicted maintenance actions of the industrial machine. In an embodiment, each of the transaction records is stored as a block in the blockchain structure.

[0080] In an embodiment, a method includes detecting an operating characteristic of an industrial machine using one or more sensors of a mobile data collector. The operating characteristic of the industrial machine is associated with a vibration detected for at least a portion of the industrial machine. The method further includes determining a severity of the operating characteristic. The severity represents an impact of the operating characteristic on the industrial machine based on a segment of a multi-segment vibration frequency spectrum that bounds the vibration. The method further includes predicting a maintenance action to be performed on the industrial machine based on the severity of the operating characteristic. In an embodiment, the mobile data collector is a mobile robot. In an embodiment, the mobile data collector is a mobile vehicle. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum that bounds the vibration is determined by mapping the vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the method further comprises mapping the vibration to a first severity unit if the frequency of the vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration to a second severity unit if the frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration to a third severity unit if the frequency of the vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the method further includes storing transaction records of the predicted maintenance actions in a ledger of service activities associated with the industrial machine. In an embodiment, the ledger uses a blockchain structure to track transaction records for predicted maintenance actions of the industrial machine. In an embodiment, each of the transaction records is stored as a block in the blockchain structure.

[0081] In an embodiment, a method includes detecting an operating characteristic of an industrial machine using one or more sensors of a mobile data collector. The operating characteristic of the industrial machine is associated with vibrations detected for at least a portion of the industrial machine. The method further includes determining a significance of the operating characteristic, the significance representing an impact of the operating characteristic on the industrial machine based on a segment of a multi-segment vibration frequency spectrum that bounds the vibration. The method further includes predicting a maintenance action to be performed on the industrial machine based on the significance of the operating characteristic, and storing a transaction record of the predicted maintenance action in a service activity ledger associated with the industrial machine. In an embodiment, the mobile data collector is a mobile robot. In an embodiment, the mobile data collector is a mobile vehicle. In an embodiment, the mobile data collector is a handheld device. In an embodiment, the mobile data collector is a wearable device. In an embodiment, the severity corresponds to a severity unit. In an embodiment, a segment of the multi-segment vibration frequency spectrum that bounds the vibration is determined by mapping the vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. In an embodiment, the method further comprises mapping the vibration to a first severity unit if the frequency of the vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration to a second severity unit if the frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration to a third severity unit if the frequency of the vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, the ledger uses a blockchain structure to track transaction records for predicted maintenance actions for the industrial machine. In an embodiment, each of the transaction records is stored as a block in the blockchain structure.

[0082] In an embodiment, a method includes detecting an operating characteristic of an industrial machine using one or more sensors of a mobile data collector. The operating characteristic of the industrial machine is associated with vibrations detected for at least a portion of the industrial machine. The method further includes determining a severity of the operating characteristic, the severity representing an impact of the operating characteristic on the industrial machine based on a segment of a multi-segment vibration frequency spectrum that constrains the vibration. In an embodiment, the severity corresponds to a severity unit. In an embodiment, the segment of the multi-segment vibration frequency spectrum that constrains the vibration is determined by mapping the vibration to one of several severity units based on the determined segment. In an embodiment, each of the severity units corresponds to a different range of the multi-segment vibration frequency spectrum. Based on the severity of the operating characteristic, the method predicts a maintenance action to be performed on the industrial machine and stores transaction records of the predicted maintenance action in a ledger of service activities associated with the industrial machine. In an embodiment, the ledger uses a blockchain structure to track transaction records for predicted maintenance actions on the industrial machine. In an embodiment, each of the transaction records is stored as a block in the blockchain structure. In embodiments, the mobile data collector is a mobile robot. In embodiments, the mobile data collector is a mobile vehicle. In embodiments, the mobile data collector is a handheld device. In embodiments, the mobile data collector is a wearable device. In embodiments, determining the severity of the behavior characteristic includes mapping the vibration to a first severity unit if the frequency of the vibration corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration to a second severity unit if the frequency of the vibration corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration to a third severity unit if the frequency of the vibration corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum.

[0083] In an embodiment, a method includes: deploying a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine, the mobile data collector including one or more vibration sensors; controlling the mobile data collector to approach a location of the industrial machine within an industrial environment including the industrial machine and causing the one or more vibration sensors of the mobile data collector to record one or more measurements of the vibration activity; transmitting the one or more measurements of the vibration activity as vibration data to a server over a network; processing the vibration data at the server to determine a severity of the vibration activity relative to a timing; predicting, at the server, a maintenance action to be performed on at least the portion of the industrial machine based on the severity of the vibration activity; and transmitting a signal indicative of the maintenance action to the mobile data collector to cause the mobile data collector to perform the maintenance action. In an embodiment, determining a severity of the vibration data relative to a timing by processing the vibration data includes: The method further comprises: determining a frequency of the vibration activity by processing the vibration data; determining a segment of a multi-segment vibration frequency spectrum that bounds the vibration activity based on the frequency; and calculating severity units for the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum. In an embodiment, calculating severity units for the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum includes mapping the vibration activity to severity units based on the determined segment of the multi-segment vibration frequency spectrum, including mapping the vibration activity to a first severity unit if the frequency of the vibration activity corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration activity to a second severity unit if the frequency of the vibration activity corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration activity to a third severity unit if the frequency of the vibration activity corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum.In an embodiment, predicting one or more maintenance actions to be performed on at least a portion of an industrial machine based on a severity of the vibration activity includes: processing the vibration data against pre-recorded data for the industrial machine using an intelligent system associated with a server. In an embodiment, processing the vibration data against pre-recorded data for the industrial machine includes identifying pre-recorded data for the industrial machine in a knowledge base associated with an industrial environment, identifying operating characteristics of at least a portion of the machine based on the pre-recorded data for the industrial machine in the knowledge base, and predicting one or more maintenance actions based on the operating characteristics. In an embodiment, the vibration activity represents a waveform derived from a vibration envelope associated with the industrial machine. In an embodiment, one or more vibration sensors detect the vibration activity when a mobile data collector is in proximity to the industrial machine. In an embodiment, the vibration activity represents speed information of at least a portion of the industrial machine. In an embodiment, the vibration activity represents frequency information for at least a portion of the industrial machine. In an embodiment, the mobile data collector is one of a plurality of mobile data collectors in a fleet of mobile data collectors. In an embodiment, the method further includes using the self-organizing system of a swarm of mobile data collectors to control movement of the mobile data collectors within an industrial environment including the industrial machine. In an embodiment, the one or more vibration sensors detect vibration activity when the mobile data collectors are in proximity to the industrial machine. In an embodiment, using the self-organizing system of a swarm of mobile data collectors to control movement of the mobile data collectors within the industrial environment includes controlling movement of the mobile data collectors within the industrial environment based on movement of at least one other mobile data collector of the plurality of mobile data collectors. In an embodiment, the mobile data collector is a mobile robot and the at least one other mobile data collector of the plurality of mobile data collectors is a mobile vehicle.

[0084] In an embodiment, a method includes deploying a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine, the mobile data collector including one or more vibration sensors. The method further includes controlling the mobile data collector to approach a location of the industrial machine within an industrial environment including the industrial machine, causing the one or more vibration sensors of the mobile data collector to record one or more measurements of the vibration activity, transmitting the one or more measurements of the vibration activity as vibration data to a server over a network, and processing the vibration data at the server to determine a frequency of the vibration activity. The method further includes determining, at the server, a segment of a multi-segment vibration frequency spectrum that limits the vibration activity based on the frequency; calculating, at the server, a severity unit for the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum; predicting, at the server, a maintenance action to be performed on at least the portion of the industrial machine based on the severity unit; transmitting a signal indicative of the maintenance action to the mobile data collector to cause the mobile data collector to perform the maintenance action; and causing the mobile data collector to perform the maintenance action. In an embodiment, calculating a severity unit for the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum includes: and mapping the vibration activity to severity units based on the determined segments of the multi-segment vibration frequency spectrum, including mapping the vibration activity to a first severity unit if a frequency of the vibration activity corresponds to below a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration activity to a second severity unit if a frequency of the vibration activity corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration activity to a third severity unit if a frequency of the vibration activity corresponds to above a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, predicting one or more maintenance actions to be performed on at least a portion of the industrial machine based on the severity units includes:and processing the vibration data against pre-recorded data for the industrial machine using an intelligent system associated with the server. In an embodiment, processing the vibration data against the pre-recorded data for the industrial machine includes identifying the pre-recorded data for the industrial machine in a knowledge base associated with the industrial environment, identifying operating characteristics of at least a portion of the machine based on the pre-recorded data for the industrial machine in the knowledge base, and predicting one or more maintenance actions based on the operating characteristics. In an embodiment, the vibration activity represents a waveform derived from a vibration envelope associated with the industrial machine. In an embodiment, one or more vibration sensors detect the vibration activity when a mobile data collector is in proximity to the industrial machine. In an embodiment, the vibration activity represents speed information of at least a portion of the industrial machine. In an embodiment, the vibration activity represents frequency information for at least a portion of the industrial machine. In an embodiment, the mobile data collector is one of a plurality of mobile data collectors in a fleet of mobile data collectors. In an embodiment, the method further comprises using a self-organizing system of mobile data collectors to control movement of the mobile data collectors within an industrial environment including the industrial machine. In an embodiment, the one or more vibration sensors detect vibration activity when the mobile data collector is in proximity to the industrial machine. In an embodiment, using the self-organizing system of mobile data collectors to control movement of the mobile data collectors within the industrial environment includes controlling movement of the mobile data collectors within the industrial environment based on movement of at least one other mobile data collector of the plurality of mobile data collectors. In an embodiment, the mobile data collector is a mobile robot, and the at least one other mobile data collector of the plurality of mobile data collectors is a mobile vehicle.

[0085] In an embodiment, a method comprises: deploying a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine, the mobile data collector including one or more vibration sensors; controlling the mobile data collector to approach a location of the industrial machine within an industrial environment including the industrial machine; causing the one or more vibration sensors of the mobile data collector to record one or more measurements of the vibration activity; and transmitting the one or more measurements of the vibration activity as vibration data to a server over a network; and processing, at the server, the vibration data to determine a severity of the vibration activity relative to a timing; predicting, at the server, a maintenance action to be performed on at least the portion of the industrial machine based on the severity of the vibration activity; transmitting a signal indicative of the maintenance action to the mobile data collector to cause the mobile data collector to perform the maintenance action; and storing a record of the predicted maintenance action in a ledger associated with the industrial machine. In an embodiment, determining a severity of the vibration data relative to a timing by processing the vibration data comprises: determining a frequency of the vibration activity by processing the vibration data, determining a segment of a multi-segment vibration frequency spectrum that bounds the vibration activity based on the frequency, and calculating severity units of the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum. In an embodiment, calculating severity units of the vibration activity based on the determined segment of the multi-segment vibration frequency spectrum includes: mapping the vibration activity to severity units based on the determined segment of the multi-segment vibration frequency spectrum.This includes mapping the vibration activity to a first severity unit if the frequency of the vibration activity corresponds to less than a low-end knee threshold range of the multi-segment vibration frequency spectrum, mapping the vibration activity to a second severity unit if the frequency of the vibration activity corresponds to a mid-range of the multi-segment vibration frequency spectrum, and mapping the vibration activity to a third severity unit if the frequency of the vibration activity corresponds to greater than or equal to a high-end knee threshold range of the multi-segment vibration frequency spectrum. In an embodiment, predicting one or more maintenance actions to be performed on at least a portion of the industrial machine based on the severity of the vibration activity includes: processing the vibration data against pre-recorded data for the industrial machine using an intelligent system associated with the server. In an embodiment, processing the vibration data against the pre-recorded data for the industrial machine includes identifying pre-recorded data for the industrial machine in a knowledge base associated with the industrial environment, identifying operating characteristics of at least a portion of the machine based on the pre-recorded data for the industrial machine in the knowledge base, and predicting one or more maintenance actions based on the operating characteristics. In an embodiment, the vibration activity represents a waveform derived from a vibration envelope associated with the industrial machine. In an embodiment, one or more vibration sensors detect vibration activity when the mobile data collector is in proximity to the industrial machine. In an embodiment, the vibration activity represents speed information of at least a portion of the industrial machine. In an embodiment, the vibration activity represents frequency information about at least a portion of the industrial machine. In an embodiment, the mobile data collector is one of a plurality of mobile data collectors in a fleet of mobile data collectors. In an embodiment, the method further comprises using a self-organizing system of a fleet of mobile data collectors to control movement of the mobile data collectors within an industrial environment including the industrial machine. In an embodiment, the one or more vibration sensors detect vibration activity when the mobile data collector is in proximity to the industrial machine.In an embodiment, using the self-organizing system of mobile data collectors to control movement of mobile data collectors within an industrial environment includes controlling movement of the mobile data collectors within the industrial environment based on movement of at least one other mobile data collector of the plurality of mobile data collectors. In an embodiment, the mobile data collector is a mobile robot and the at least one other mobile data collector of the plurality of mobile data collectors is a mobile vehicle. In an embodiment, the ledger tracks transaction records for predicted maintenance actions of the industrial machinery using a blockchain structure. In an embodiment, each of the transaction records is stored as a block in the blockchain structure. [Brief explanation of the drawings]

[0086] [Figure 1] 1-5 are each perspective views depicting a portion of an overall view of an industrial IoT data collection, monitoring, and control system according to the present disclosure. [Figure 2] 1-5 are each perspective views depicting a portion of an overall view of an industrial IoT data collection, monitoring, and control system according to the present disclosure. [Figure 3] 1-5 are each perspective views depicting a portion of an overall view of an industrial IoT data collection, monitoring, and control system according to the present disclosure. [Figure 4] 1-5 are each perspective views depicting a portion of an overall view of an industrial IoT data collection, monitoring, and control system according to the present disclosure. [Figure 5] 1-5 are each perspective views depicting a portion of an overall view of an industrial IoT data collection, monitoring, and control system according to the present disclosure.

[0087] [Figure 6]FIG. 6 is a perspective view of a platform including a local data collection system deployed in an industrial environment to collect data from or about elements of the environment, such as machines, components, systems, subsystems, ambient conditions, states, workflows, processes, and other elements, in accordance with the present disclosure.

[0088] [Figure 7] FIG. 7 is a perspective view illustrating elements of an industrial data collection system for collecting analog sensor data in an industrial environment according to the present disclosure.

[0089] [Figure 8] FIG. 8 is a perspective view of a rotating or vibrating machine having a data collection module configured to collect waveform data in accordance with the present disclosure.

[0090] [Figure 9] FIG. 9 is a perspective view of an example three-axis sensor mounted to a motor bearing of an example rotating machine according to the present disclosure.

[0091] [Figure 10] 10 and 11 are perspective views of an exemplary three-axis sensor and a single-axis sensor mounted on an exemplary rotating machine in accordance with the present disclosure; [Figure 11] 10 and 11 are perspective views of an exemplary three-axis sensor and a single-axis sensor mounted on an exemplary rotating machine in accordance with the present disclosure;

[0092] [Figure 12] FIG. 12 is a perspective view of multiple machines under investigation with an ensemble of sensors according to the present disclosure.

[0093] [Figure 13] FIG. 13 is a diagram illustrating a hybrid relational metadata and binary storage approach in accordance with the present disclosure.

[0094] [Figure 14] FIG. 14 is a diagram illustrating components and interactions of a data collection architecture including the application of cognitive and machine learning systems to data collection and processing in accordance with the present disclosure.

[0095] [Figure 15] FIG. 15 is a diagram illustrating components and interactions of a data collection architecture including applications of a platform with a cognitive data marketplace in accordance with the present disclosure.

[0096] [Figure 16] FIG. 16 is a diagram illustrating the components and interactions of a data collection architecture involving the application of a self-organizing swarm of data collectors in accordance with the present disclosure.

[0097] [Figure 17] FIG. 17 is a diagram illustrating the components and interactions of a data collection architecture including the application of a haptic user interface according to the present disclosure.

[0098] [Figure 18] FIG. 18 is a perspective view of a multi-format streaming data collection system according to the present disclosure.

[0099] [Figure 19] FIG. 19 is a diagram illustrating combined legacy and streaming data collection and storage in accordance with the present disclosure.

[0100] [Figure 20] FIG. 20 is a perspective view of industrial machine sensing using both legacy and updated streamed sensor data processing in accordance with the present disclosure.

[0101] [Figure 21] FIG. 21 is a perspective view of an industrial machine sensing data processing system that facilitates the use and alignment of portal algorithms of legacy and streamed sensor data in accordance with the present disclosure.

[0102] [Figure 22] FIG. 22 illustrates components and interactions of a data collection architecture including streaming data collection equipment receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0103] [Figure 23] FIG. 23 illustrates components and interactions of a data collection architecture including an alarm module, an expert analysis module, and a streaming data collection appliance with a driver API for facilitating communication with cloud network facilities in accordance with the present disclosure.

[0104] [Figure 24] FIG. 24 illustrates components and interactions of a data collection architecture including a streaming data collector and a first-in, first-out memory architecture to provide a real-time operating system in accordance with the present disclosure.

[0105] [Figure 25] 25-30 are perspective views of screens showing four analog sensor signals, transfer functions between the signals, analysis of each signal, and operational controls for navigating and editing the entire streaming signal obtained from the sensors in accordance with the present disclosure. [Figure 26] 25-30 are perspective views of screens showing four analog sensor signals, transfer functions between the signals, analysis of each signal, and operational controls for navigating and editing the entire streaming signal obtained from the sensors in accordance with the present disclosure. [Figure 27]25-30 are perspective views of screens showing four analog sensor signals, transfer functions between the signals, analysis of each signal, and operational controls for navigating and editing the entire streaming signal obtained from the sensors in accordance with the present disclosure. [Figure 28] 25-30 are perspective views of screens showing four analog sensor signals, transfer functions between the signals, analysis of each signal, and operational controls for navigating and editing the entire streaming signal obtained from the sensors in accordance with the present disclosure. [Figure 29] 25-30 are perspective views of screens showing four analog sensor signals, transfer functions between the signals, analysis of each signal, and operational controls for navigating and editing the entire streaming signal obtained from the sensors in accordance with the present disclosure. [Figure 30] 25-30 are perspective views of screens showing four analog sensor signals, transfer functions between the signals, analysis of each signal, and operational controls for navigating and editing the entire streaming signal obtained from the sensors in accordance with the present disclosure.

[0106] [Figure 31] FIG. 31 illustrates the components and interactions of a data collection architecture including multiple streaming data collection devices that receive analog sensor signals and digitize those signals for acquisition by a streaming hub server in accordance with the present disclosure.

[0107] [Figure 32] FIG. 32 illustrates the components and interactions of a data collection architecture including a master raw data server that processes new streaming data and previously extracted and processed data in accordance with the present disclosure.

[0108] [Figure 33] 33, 34, and 35 illustrate the components and interactions of a data collection architecture including processing, analysis, reporting, and archiving servers that handle new streaming data and previously extracted and processed data in accordance with the present disclosure. [Figure 34] 33, 34, and 35 illustrate the components and interactions of a data collection architecture including processing, analysis, reporting, and archiving servers that handle new streaming data and previously extracted and processed data in accordance with the present disclosure. [Figure 35] 33, 34, and 35 illustrate the components and interactions of a data collection architecture including processing, analysis, reporting, and archiving servers that handle new streaming data and previously extracted and processed data in accordance with the present disclosure.

[0109] [Figure 36] FIG. 36 is a diagram illustrating the components and interactions of a data collection architecture, including a relational database server and a data archive, and their connections with cloud network facilities, in accordance with the present disclosure.

[0110] [Figure 37] 37-42 illustrate components and interactions of a data collection architecture including a virtual streaming data collection appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 38] 37-42 illustrate components and interactions of a data collection architecture including a virtual streaming data collection appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 39] 37-42 illustrate components and interactions of a data collection architecture including a virtual streaming data collection appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 40]37-42 illustrate components and interactions of a data collection architecture including a virtual streaming data collection appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 41] 37-42 illustrate components and interactions of a data collection architecture including a virtual streaming data collection appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 42] 37-42 illustrate components and interactions of a data collection architecture including a virtual streaming data collection appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0111] [Figure 43] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 44] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 45] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 46] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 47] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 48]43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 49] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 50] 43-50 are diagrams illustrating components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure.

[0112] [Figure 51] FIG. 51 is a diagram illustrating one embodiment of a data monitoring device according to the present disclosure.

[0113] [Figure 52] 52 and 53 are perspective views of an embodiment of a data monitoring device according to the present disclosure; FIG. 52 and FIG. 53 are perspective views of an embodiment of a data monitoring device according to the present disclosure; [Figure 53] 52 and 53 are perspective views of an embodiment of a data monitoring device according to the present disclosure; FIG. 54 is a perspective view of an embodiment of a data monitoring device according to the present disclosure;

[0114] [Figure 54] FIG. 54 is a diagram illustrating one embodiment of a data monitoring device according to the present disclosure.

[0115] [Figure 55] 55 and 56 are perspective views of an embodiment of a system for collecting data according to the present disclosure. [Figure 56] 55 and 56 are perspective views of an embodiment of a system for collecting data according to the present disclosure.

[0116] [Figure 57] 57 and 58 are perspective views illustrating an embodiment of a system for data collection including a plurality of data monitoring devices in accordance with the present disclosure. [Figure 58] 57 and 58 are perspective views illustrating an embodiment of a system for data collection including a plurality of data monitoring devices in accordance with the present disclosure.

[0117] [Figure 59] FIG. 59 illustrates one embodiment of a data monitoring device incorporating sensors in accordance with the present disclosure.

[0118] [Figure 60] 60 and 61 are perspective views illustrating an embodiment of a data monitoring device communicating with an external sensor in accordance with the present disclosure. [Figure 61] 60 and 61 are perspective views illustrating an embodiment of a data monitoring device communicating with an external sensor in accordance with the present disclosure.

[0119] [Figure 62] FIG. 62 is a perspective view illustrating an embodiment of a data monitoring device with further details of the signal evaluation circuitry according to the present disclosure.

[0120] [Figure 63] FIG. 63 is a perspective view illustrating an embodiment of a data monitoring device with further details of the signal evaluation circuitry according to the present disclosure.

[0121] [Figure 64] FIG. 64 illustrates an embodiment of a data monitoring device with further details of the signal evaluation circuitry according to the present disclosure.

[0122] [Figure 65] FIG. 65 illustrates an embodiment of a system for data collection according to the present disclosure.

[0123] [Figure 66] 66 illustrates an embodiment of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0124] [Figure 67] FIG. 67 illustrates one embodiment of a data monitoring device according to the present disclosure.

[0125] [Figure 68] 68 and 69 are perspective views of an embodiment of a data monitoring device according to the present disclosure. [Figure 69] 68 and 69 are perspective views of an embodiment of a data monitoring device according to the present disclosure.

[0126] [Figure 70] 70 and 71 are perspective views of an embodiment of a data monitoring device according to the present disclosure; FIG. 71 is a perspective view of an embodiment of a data monitoring device according to the present disclosure; [Figure 71] 70 and 71 are perspective views of an embodiment of a data monitoring device according to the present disclosure; FIG. 71 is a perspective view of an embodiment of a data monitoring device according to the present disclosure;

[0127] [Figure 72] 72 and 73 are perspective views illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 73] 72 and 73 are perspective views illustrating an embodiment of a data monitoring device according to the present disclosure.

[0128] [Figure 74]74 and 75 illustrate an embodiment of a system for data collection including multiple data monitoring devices in accordance with the present disclosure. [Figure 75] 74 and 75 illustrate an embodiment of a system for data collection including multiple data monitoring devices in accordance with the present disclosure.

[0129] [Figure 76] FIG. 76 is a diagram illustrating one embodiment of a data monitoring device according to the present disclosure.

[0130] [Figure 77] 77 and 78 are perspective views of an embodiment of a data monitoring device according to the present disclosure. [Figure 78] 77 and 78 are perspective views of an embodiment of a data monitoring device according to the present disclosure.

[0131] [Figure 79] FIG. 79 illustrates one embodiment of a data monitoring device according to the present disclosure.

[0132] [Figure 80] FIG. 80 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0133] [Figure 81] 81 and 82 are perspective views illustrating an embodiment of a system for data collection according to the present disclosure. [Figure 82] 81 and 82 are perspective views illustrating an embodiment of a system for data collection according to the present disclosure.

[0134] [Figure 83] 83 and 84 illustrate an embodiment of a system for data collection including multiple data monitoring devices in accordance with the present disclosure. [Figure 84] 83 and 84 illustrate an embodiment of a system for data collection including multiple data monitoring devices in accordance with the present disclosure.

[0135] [Figure 85] FIG. 85 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0136] [Figure 86] 86 and 87 illustrate embodiments of a data monitoring device according to the present disclosure. [Figure 87] 86 and 87 illustrate embodiments of a data monitoring device according to the present disclosure.

[0137] [Figure 88] FIG. 88 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0138] [Figure 89] 89 and 90 are perspective views illustrating an embodiment of a system for data collection according to the present disclosure. [Figure 90] 89 and 90 are perspective views illustrating an embodiment of a system for data collection according to the present disclosure.

[0139] [Figure 91] 91 and 92 illustrate an embodiment of a system for data collection including multiple data monitoring devices in accordance with the present disclosure. [Figure 92] 91 and 92 illustrate an embodiment of a system for data collection including multiple data monitoring devices in accordance with the present disclosure.

[0140] [Figure 93] FIG. 93 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0141] [Figure 94] 94 and 95 are perspective views depicting an embodiment of a data monitoring device according to the present disclosure. [Figure 95]94 and 95 are perspective views depicting an embodiment of a data monitoring device according to the present disclosure.

[0142] [Figure 96] FIG. 96 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0143] [Figure 97] 97 and 98 are perspective views illustrating an embodiment of a system for data collection according to the present disclosure. [Figure 98] 97 and 98 are perspective views illustrating an embodiment of a system for data collection according to the present disclosure.

[0144] [Figure 99] 99 and 100 illustrate embodiments of a system for data collection that includes multiple data monitoring devices in accordance with the present disclosure. [Figure 100] 99 and 100 illustrate embodiments of a system for data collection that includes multiple data monitoring devices in accordance with the present disclosure.

[0145] [Figure 101] FIG. 101 is a diagram illustrating one embodiment of a data monitoring device according to the present disclosure.

[0146] [Figure 102] 102 and 103 are perspective views of an embodiment of a data monitoring device according to the present disclosure; and FIGS. 102 and 103 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 103] 102 and 103 are perspective views of an embodiment of a data monitoring device according to the present disclosure; and FIGS. 102 and 103 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure.

[0147] [Figure 104] FIG. 104 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0148] [Figure 105] 105 and 106 are perspective views illustrating an embodiment of a system for collecting data according to the present disclosure. [Figure 106] 105 and 106 are perspective views illustrating an embodiment of a system for collecting data according to the present disclosure.

[0149] [Figure 107] 107 and 108 illustrate embodiments of a system for data collection that includes multiple data monitoring devices in accordance with the present disclosure. [Figure 108] 107 and 108 illustrate embodiments of a system for data collection that includes multiple data monitoring devices in accordance with the present disclosure.

[0150] [Figure 109] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 110] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 111] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 112]109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 113] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 114] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 115] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 116] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 117] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 118]109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 119] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 120] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 121] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 122] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 123] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 124]109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 125] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 126] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 127] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 128] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 129] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 130]109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 131] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 132] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 133] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 134] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 135] 109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure. [Figure 136]109-136 illustrate components and interactions of a data collection architecture including various neural network embodiments interacting with a streaming data collector receiving analog sensor signals and an expert analysis module in accordance with the present disclosure.

[0151] [Figure 137] 137-139 are diagrams illustrating components and interactions of a data collection architecture including a collector of root templates in an industrial environment according to the present disclosure. [Figure 138] 137-139 are diagrams illustrating components and interactions of a data collection architecture including a collector of root templates in an industrial environment according to the present disclosure. [Figure 139] 137-139 are diagrams illustrating components and interactions of a data collection architecture including a collector of root templates in an industrial environment according to the present disclosure.

[0152] [Figure 140] FIG. 140 illustrates a monitoring system employing a data collection band in accordance with the present disclosure.

[0153] [Figure 141] FIG. 141 is a diagram illustrating a system that employs vibrations and other noises in predicting conditions and outcomes in accordance with the present disclosure.

[0154] [Figure 142] FIG. 142 is a perspective view of a system for data collection in an industrial environment according to the present disclosure. FIG. 142 is a diagram illustrating a system for data collection in an industrial environment according to the present disclosure.

[0155] [Figure 143] FIG. 143 is a perspective view of an apparatus for data collection in an industrial environment according to the present disclosure. FIG. 143 is a perspective view of an apparatus for data collection in an industrial environment according to the present disclosure.

[0156] [Figure 144] FIG. 144 is a schematic flow diagram of a procedure for data collection in an industrial environment according to the present disclosure.

[0157] [Figure 145] FIG. 145 is a perspective view illustrating a system for data collection in an industrial environment according to the present disclosure.

[0158] [Figure 146] FIG. 146 is a perspective view illustrating an apparatus for data collection in an industrial environment according to the present disclosure.

[0159] [Figure 147] FIG. 147 is a schematic flow diagram of a procedure for data collection in an industrial environment according to the present disclosure.

[0160] [Figure 148] FIG. 148 is a diagram depicting industry-specific feedback in an industrial environment in accordance with the present disclosure.

[0161] [Figure 149] FIG. 149 is a diagram depicting an example user interface for smart band configuration of a system for data collection in an industrial environment according to the present disclosure.

[0162] [Figure 150] FIG. 150 is a diagram depicting a graphical approach 11300 for inverse calculations according to the present disclosure.

[0163] [Figure 151] FIG. 151 illustrates a wearable haptic user interface device for providing haptic stimulation to a user responsive to data collected in an industrial environment by a system adapted to collect data in an industrial environment in accordance with the present disclosure.

[0164] [Figure 152] FIG. 152 is a diagram depicting an augmented reality display of a heat map based on data collected in an industrial environment by a system adapted to collect data in the environment in accordance with the present disclosure.

[0165] [Figure 153] FIG. 153 is a diagram depicting an augmented reality display including real-time data overlaid with a view of an industrial environment in accordance with the present disclosure.

[0166] [Fig. 154] FIG. 154 is a diagram depicting a graphical user interface display and neural network components according to the present disclosure.

[0167] [Figure 155] FIG. 155 is a diagram illustrating components and interactions of a data collection architecture including a crowd data collector and sensor mesh protocol in an industrial environment in accordance with the present disclosure.

[0168] [Figure 156] 156-159 are perspective views of a mobile sensor platform in an industrial environment according to the present disclosure. [Figure 157] 156-159 are perspective views of a mobile sensor platform in an industrial environment according to the present disclosure. [Figure 158] 156-159 are perspective views of a mobile sensor platform in an industrial environment according to the present disclosure. [Figure 159] 156-159 are perspective views of a mobile sensor platform in an industrial environment according to the present disclosure.

[0169] [Figure 160] FIG. 160 is a diagram illustrating the components and interactions of a data collection architecture including two mobile sensor platforms inspecting a vehicle during assembly in an industrial environment in accordance with the present disclosure.

[0170] [Figure 161] 161 and 162 are perspective views of an example of a mobile sensor platform in an industrial environment according to the present disclosure, and are diagrams showing an example of a mobile sensor platform in an industrial environment according to the present disclosure. [Figure 162] 161 and 162 are perspective views of an example of a mobile sensor platform in an industrial environment according to the present disclosure, and are diagrams showing an example of a mobile sensor platform in an industrial environment according to the present disclosure.

[0171] [Figure 163] FIG. 163 is a diagram illustrating the components and interactions of a data collection architecture including two mobile sensor platforms inspecting a turbine engine during assembly in an industrial environment in accordance with the present disclosure.

[0172] [Fig. 164] FIG. 164 is a perspective view illustrating a data collection system according to some aspects of the present disclosure.

[0173] [Figure 165] FIG. 165 is a diagram depicting a system for self-organized, network-aware data collection in an industrial environment according to the present disclosure.

[0174] [Figure 166] FIG. 1666 is a diagram illustrating an apparatus for self-organized, network-aware data collection in an industrial environment according to the present disclosure.

[0175] [Figure 167] FIG. 167 is a diagram illustrating an apparatus for self-organizing, network-aware data collection in an industrial environment according to the present disclosure.

[0176] [Figure 168]FIG. 168 is a perspective view showing an apparatus for self-organized, network-aware data collection in an industrial environment according to the present disclosure; FIG. 168 shows an apparatus for self-organized, network-aware data collection in an industrial environment according to the present disclosure.

[0177] [Figure 169] 169 and 170 are perspective views illustrating embodiments of transmission conditions according to the present disclosure. [Figure 170] 169 and 170 are perspective views illustrating embodiments of transmission conditions according to the present disclosure.

[0178] [Figure 171] FIG. 171 illustrates an embodiment of a sensor data transmission protocol according to the present disclosure. FIG. 171 illustrates an embodiment of a sensor data transmission protocol according to the present disclosure.

[0179] [Fig. 172] 172 and 173 are diagrams illustrating embodiments of benchmark data according to the present disclosure. [Figure 173] 172 and 173 are diagrams illustrating embodiments of benchmark data according to the present disclosure.

[0180] [Fig. 174] FIG. 174 illustrates an embodiment of a system for data collection and storage in an industrial environment according to the present disclosure.

[0181] [Figure 175] FIG. 175 illustrates an embodiment of an apparatus for self-organizing storage for data collection for an industrial system according to the present disclosure.

[0182] [Figure 176] FIG. 176 illustrates an embodiment of a storage time definition according to the present disclosure.

[0183] [Figure 177] FIG. 177 is a diagram illustrating an embodiment of a data resolution description according to the present disclosure. FIG. 177 is a diagram illustrating an embodiment of a data resolution description according to the present disclosure.

[0184] [Figure 178] 178 and 179 are perspective views of an apparatus for self-organizing network coding for data collection in an industrial system according to the present disclosure. [Figure 179] 178 and 179 are perspective views of an apparatus for self-organizing network coding for data collection in an industrial system according to the present disclosure.

[0185] [Figure 180] 180 and 181 are perspective views of a data marketplace that interacts with data collection in an industrial system in accordance with the present disclosure. [Figure 181] 180 and 181 are perspective views of a data marketplace that interacts with data collection in an industrial system in accordance with the present disclosure.

[0186] [Figure 182] FIG. 182 is a diagram depicting a smart heating system as an element within a network in an Industrial Internet of Things ecosystem in accordance with the present disclosure.

[0187] [Figure 183] FIG. 183 is a schematic diagram of a data network including server nodes and client nodes coupled by an intermediate network.

[0188] [Figure 184] FIG. 184 is a block diagram illustrating modules that implement TCP-based communication between a client node and a server node.

[0189] [Figure 185] FIG. 185 is a block diagram illustrating modules that implement PC-TCP (Packet Coding Transmission Communication Protocol) based communication between a client node and a server node.

[0190] [Figure 186] FIG. 186 is a schematic diagram of the use of PC-TCP based communication between a server and a module device over a cellular network.

[0191] [Figure 187] FIG. 187 is a block diagram of a PC-TCP module 1 using a conventional UDP module.

[0192] [Figure 188] FIG. 188 is a block diagram of a PC-TCP module partially integrated into a client application and partially implemented using a conventional UDP module.

[0193] [Figure 189] Figure 189 is a block diagram or diagram dividing the PC-TCP module into user space and kernel space components.

[0194] [Figure 190] Figure 190 is a block diagram of the proxy architecture.

[0195] [Figure 191] Figure 191 is a block diagram of a PC-TCP based proxy architecture in which proxy nodes communicate using both PC-TCP and conventional TCP.

[0196] [Figure 192] FIG. 192 is a block diagram of a PC-TCP proxy-based architecture implemented using a gateway device.

[0197] [Figure 193] FIG. 193 is a block diagram of an alternative proxy architecture embodied within a client node.

[0198] [Figure 194] FIG. 194 is a block diagram of a second PC-TCP-based proxy architecture in which proxy nodes communicate using both PC-TCP and conventional TCP.

[0199] [Figure 195] FIG. 195 is a block diagram of a PC-TCP proxy-based architecture implemented using a wireless access device.

[0200] [Figure 196] FIG. 196 is a block diagram of a cellular network implementing a PC-TCP proxy-based architecture.

[0201] [Figure 197] FIG. 197 is a block diagram of a cable television-based data network that implements a PC-TCP proxy-based architecture.

[0202] [Figure 198] FIG. 198 is a block diagram of an intermediate proxy that communicates with a client node and a server node using separate PC-TCP connections.

[0203] [Figure 199] FIG. 199 is a block diagram of a PC-TCP proxy-based architecture embodied in a network device.

[0204] [Figure 200] FIG. 200 is a block diagram of an intermediate proxy that re-encodes communications between a client node and a server node.

[0205] [Figure 201]201 and 202 are diagrams for explaining distribution of common content to a plurality of distribution destinations. [Figure 202] 201 and 202 are diagrams for explaining distribution of common content to a plurality of distribution destinations.

[0206] [Figure 203] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 204] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 205] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 206] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 207] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 208] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 209] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 210] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 211] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 212] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches. [Figure 213] 203-213 are schematic diagrams of various embodiments of PC-TCP communication approaches.

[0207] [Figure 214] FIG. 214 is a block diagram of a PC-TCP communication approach including window and rate control modules.

[0208] [Figure 215] FIG. 215 is a schematic diagram of a data network.

[0209] [Figure 216] 216-219 are block diagrams illustrating an embodiment of a PC-TCP communication approach configured according to a number of adjustable parameters. [Figure 217] 216-219 are block diagrams illustrating an embodiment of a PC-TCP communication approach configured according to a number of adjustable parameters. [Figure 218] 216-219 are block diagrams illustrating an embodiment of a PC-TCP communication approach configured according to a number of adjustable parameters. [Figure 219] 216-219 are block diagrams illustrating an embodiment of a PC-TCP communication approach configured according to a number of adjustable parameters.

[0210] [Figure 220] FIG. 220 is a diagram illustrating a network communication system.

[0211] [Figure 221] FIG. 221 is a schematic diagram for explaining the use of stored communication parameters.

[0212] [Figure 222] FIG. 222 is a schematic diagram illustrating the first embodiment or multi-path content distribution.

[0213] [Figure 223] 223 to 225 are schematic diagrams showing a second embodiment of multi-path content delivery. [Figure 224] 223 to 225 are schematic diagrams showing a second embodiment of multi-path content delivery. [Figure 225]223 to 225 are schematic diagrams showing a second embodiment of multi-path content delivery.

[0214] [Figure 226] FIG. 226 is a diagram illustrating an integrated cooktop of an intelligent cooking system method and system according to the present teachings. FIG. 226 is a diagram illustrating an integrated cooktop of an intelligent cooking system method and system according to the present teachings.

[0215] [Figure 227] FIG. 227 is a perspective view illustrating a single intelligent burner of an intelligent cooking system according to the present teachings.

[0216] [Figure 228] FIG. 228 is a partial exterior view depicting a solar-powered hydrogen production and storage station in accordance with the present teachings.

[0217] [Figure 229] FIG. 229 is a perspective view of a low pressure storage system in accordance with the present teachings.

[0218] [Figure 230] 230 and 231 are cross-sectional views of the low-pressure accumulator of the present invention. [Figure 231] 230 and 231 are cross-sectional views of the low-pressure accumulator of the present invention.

[0219] [Figure 232] FIG. 232 is a perspective view of an electrolytic cell in accordance with the present teachings.

[0220] [Figure 233] FIG. 233 is a diagram depicting features of a platform for interaction between electronic devices and related ecosystem participants of suppliers, content providers, service providers, and regulators in accordance with the present teachings.

[0221] [Figure 234] FIG. 234 illustrates a smart home embodiment of an intelligent cooking system according to the present teachings.

[0222] [Figure 235] FIG. 235 is a perspective view of one embodiment of a hydrogen production and utilization system according to the present teachings.

[0223] [Figure 236] FIG. 236 is a perspective view of an electrolysis cell in accordance with the present teachings.

[0224] [Figure 237] FIG. 237 is a perspective view depicting a hydrogen production system incorporated into a cooking system according to the present teachings.

[0225] [Figure 238] FIG. 238 is a diagram depicting an automatic switching connection in the form of ad-hoc Wi-Fi from a cooktop via a nearby mobile device in a normal connection mode when Wi-Fi is available in accordance with the present teachings.

[0226] [Figure 239] FIG. 239 is a diagram depicting an automatically switching connection in the form of ad-hoc Wi-Fi from a cooktop through a nearby mobile device for ad-hoc use of a local mobile device for connecting to the cloud in accordance with the present teachings.

[0227] [Figure 240] FIG. 240 is a perspective view showing a three-element induction smart cooking system in accordance with the present teachings.

[0228] [Figure 241] FIG. 241 is a perspective view of a single burner gas smart cooking system in accordance with the present teachings.

[0229] [Figure 242]FIG. 242 is a perspective view of an electric hot plate smart cooking system in accordance with the present teachings.

[0230] [Figure 243] FIG. 243 is a perspective view illustrating a single induction heating element smart cooking system in accordance with the present teachings.

[0231] [Figure 244] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 245] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 246] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 247] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 248] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 249] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 250] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings. [Figure 251] 244-251 are visual interface views depicting the user interface features of a smart knob in accordance with the present teachings.

[0232] [Figure 252]FIG. 252 is a perspective view showing a smart knob deployed in a single heating element cooking system in accordance with the present teachings.

[0233] [Figure 253] FIG. 253 is a partial perspective view depicting a smart knob mounted on the side of a kitchen appliance for a single heating element cooking system in accordance with the present teachings.

[0234] [Figure 254] 254-257 are perspective views depicting smart temperature probes of a smart cooking system according to the present teachings. [Figure 255] 254-257 are perspective views depicting smart temperature probes of a smart cooking system according to the present teachings. [Figure 256] 254-257 are perspective views depicting smart temperature probes of a smart cooking system according to the present teachings. [Figure 257] 254-257 are perspective views depicting smart temperature probes of a smart cooking system according to the present teachings.

[0235] [Figure 258] 258-263 are perspective views depicting different docks for compatibility with various smartphone and tablet devices in accordance with the present teachings. [Figure 259] 258-263 are perspective views depicting different docks for compatibility with various smartphone and tablet devices in accordance with the present teachings. [Figure 260] 258-263 are perspective views depicting different docks for compatibility with various smartphone and tablet devices in accordance with the present teachings. [Figure 261] 258-263 are perspective views depicting different docks for compatibility with various smartphone and tablet devices in accordance with the present teachings. [Figure 262]258-263 are perspective views depicting different docks for compatibility with various smartphone and tablet devices in accordance with the present teachings. [Figure 263] 258-263 are perspective views depicting different docks for compatibility with various smartphone and tablet devices in accordance with the present teachings.

[0236] [Figure 264] 264 and 266 illustrate burner designs intended for use in a smart cooking system according to the present teachings.

[0237] [Figure 265] Figure 265 is a cross-sectional view of a burner design intended for use in a smart cooking system. [Figure 266] 264 and 266 illustrate burner designs intended for use in a smart cooking system according to the present teachings.

[0238] [Figure 267] 267, 269, and 271 are perspective views of a burner design intended for use in a smart cooking system according to another embodiment of the present teachings, and FIG. 267 illustrates a burner design intended for use in a smart cooking system according to another embodiment of the present teachings.

[0239] [Figure 268] Figures 268 and 270 are cross-sectional views of the burner design. [Figure 269] 267, 269, and 271 are perspective views of a burner design intended for use in a smart cooking system according to another embodiment of the present teachings, and FIG. 267 illustrates a burner design intended for use in a smart cooking system according to another embodiment of the present teachings. [Figure 270] Figures 268 and 270 are cross-sectional views of the burner design. [Fig. 271]267, 269, and 271 are perspective views of a burner design intended for use in a smart cooking system according to another embodiment of the present teachings, and FIG. 267 illustrates a burner design intended for use in a smart cooking system according to another embodiment of the present teachings.

[0240] [Fig. 272] 272-274 illustrate burner designs intended for use in a smart cooking system according to further embodiments of the present teachings. [Fig. 273] 272-274 illustrate burner designs intended for use in a smart cooking system according to further embodiments of the present teachings. [Fig. 274] 272-274 illustrate burner designs intended for use in a smart cooking system according to further embodiments of the present teachings.

[0241] [Figure 275] 275-277 illustrate burner designs intended for use in a smart cooking system in accordance with yet another example of the present teachings. [Figure 276] 275-277 illustrate burner designs intended for use in a smart cooking system in accordance with yet another example of the present teachings. [Figure 277] 275-277 illustrate burner designs intended for use in a smart cooking system in accordance with yet another example of the present teachings.

[0242] [Fig. 278] 278 and 280 are perspective views of burner designs intended for use in a smart cooking system according to further embodiments of the present teachings.

[0243] [Figure 279] Figure 279 is a cross-sectional view of a burner design intended for use in a smart cooking system. [Figure 280]278 and 280 are perspective views of burner designs intended for use in a smart cooking system according to further embodiments of the present teachings.

[0244] [Figure 281] FIG. 281 is a flowchart illustrating a method associated with a smart kitchen including a smart cooktop and an exhaust fan that may automatically turn on when water in a pot begins to boil in accordance with the present teachings.

[0245] [Figure 282] FIG. 282 depicts an embodiment method and system relating to renewable energy sources for the production, storage, distribution, and use of hydrogen in accordance with the present teachings.

[0246] [Figure 283] FIG. 283 is an alternative embodiment method and system related to renewable energy sources according to the present teachings.

[0247] [Fig. 284] FIG. 284 is an alternative embodiment method and system related to renewable energy sources according to the present teachings.

[0248] [Figure 285] Figure 285 shows the environment and production uses of the hydrogen production, storage, distribution and utilization system.

[0249] [Figure 286] 286-289 illustrate embodiments of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure. [Figure 287] 286-289 illustrate embodiments of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure. [Figure 288]286-289 illustrate embodiments of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure. [Figure 289] 286-289 illustrate embodiments of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure.

[0250] [Figure 290] 290-292 illustrate an embodiment of a system for using one or more mobile robots and / or vehicles for mobile data collection in accordance with the present disclosure. [Figure 291] 290-292 illustrate an embodiment of a system for using one or more mobile robots and / or vehicles for mobile data collection in accordance with the present disclosure. [Figure 292] 290-292 illustrate an embodiment of a system for using one or more mobile robots and / or vehicles for mobile data collection in accordance with the present disclosure.

[0251] [Figure 293] 293-296 illustrate an embodiment of a system for using one or more handheld devices for mobile data collection in accordance with the present disclosure. [Fig. 294] 293-296 illustrate an embodiment of a system for using one or more handheld devices for mobile data collection in accordance with the present disclosure. [Figure 295] 293-296 illustrate an embodiment of a system for using one or more handheld devices for mobile data collection in accordance with the present disclosure. [Figure 296] 293-296 illustrate an embodiment of a system for using one or more handheld devices for mobile data collection in accordance with the present disclosure.

[0252] [Figure 297] 297-299 are diagrams illustrating an embodiment of a computer vision system according to the present disclosure. [Figure 298] 297-299 are diagrams illustrating an embodiment of a computer vision system according to the present disclosure. [Figure 299] 297-299 are diagrams illustrating an embodiment of a computer vision system according to the present disclosure.

[0253] [Figure 300] 300-301 are perspective views illustrating an embodiment of a deep learning system for training a computer vision system in accordance with the present disclosure. [Figure 301] 300-301 are perspective views illustrating an embodiment of a deep learning system for training a computer vision system in accordance with the present disclosure.

[0254] [Figure 302] Figure 302 is a diagram illustrating the network architecture of a predictive maintenance ecosystem.

[0255] [Figure 303] Figure 303 shows using machine learning to find service workers for the predictive maintenance ecosystem in Figure 302.

[0256] [Figure 304] Figure 304 illustrates parts ordering and servicing in a predictive maintenance ecosystem.

[0257] [Figure 305] FIG. 305 illustrates the deployment of smart RFID elements in an industrial machine environment.

[0258] [Figure 306] FIG. 306 is a diagram illustrating a generalized data structure for machine information in smart RFID.

[0259] [Figure 307] Figure 307 is a block level diagram showing the storage structure of a smart RFID.

[0260] [Figure 308] FIG. 308 is a diagram showing an example of data stored in a smart RFID.

[0261] [Figure 309] FIG. 309 shows a flow diagram of a method for collecting information from gaming machines.

[0262] [Figure 310] FIG. 310 shows a flow diagram of a method for collecting data from a production environment.

[0263] [Figure 311] FIG. 311 illustrates an online maintenance management system with interfaces for data sources that update information in the online maintenance management system's data storage.

[0264] [Figure 312] Figure 312 shows a distributed ledger for predictive maintenance information with role-based access.

[0265] [Figure 313] FIG. 313 is a diagram illustrating a process for capturing an image of a part of an industrial machine.

[0266] [Figure 314] Figure 314 shows the process of using machine learning on an image to recognize what is likely the internal structure of an industrial machine.

[0267] [Figure 315]FIG. 315 is a diagram illustrating a knowledge graph for predictive maintenance information collection.

[0268] [Figure 316] FIG. 316 illustrates an artificial intelligence system that generates service recommendations and the like based on predictive maintenance analysis.

[0269] [Figure 317] FIG. 317 shows a predictive maintenance timeline superimposed on a preventive maintenance timeline.

[0270] [Figure 318] FIG. 318 shows a block diagram of potential sources of diagnostic information.

[0271] [Figure 319] Figure 319 illustrates the vendor rating process.

[0272] [Figure 320] Figure 320 shows the process of the rating procedure.

[0273] [Figure 321] Figure 321 is a diagram of Blockchain applied to transactions in the predictive maintenance ecosystem.

[0274] [Figure 322] FIG. 322 illustrates a transfer function that facilitates the conversion of vibration data into severity units.

[0275] [Figure 323] FIG. 323 shows a table that facilitates mapping vibration data to severity units.

[0276] [Figure 324] FIG. 324 shows a composite frequency graph of a conventional vibration rating and a severity unit rating.

[0277] [Figure 325] FIG. 325 shows a rendering of a portion of an industrial machine for use in an electronic user interface for depicting and discovering severity units and related information about rotating parts of the industrial machine.

[0278] [Figure 326] FIG. 326 shows a data table of rotating component design parameters for use in predicting maintenance events.

[0279] [Figure 327] FIG. 327 is a flowchart for predicting maintenance of at least one of a gear, a motor, and a roller bearing based on a severity unit such as the number of gear teeth and the number of actuators. DETAILED DESCRIPTION OF THE INVENTION

[0280] Although detailed embodiments of the present disclosure are disclosed herein, it will be understood that the disclosed embodiments are merely exemplary of the present disclosure, which may be embodied in various forms. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a basis for the claims and as a representative basis for teaching those skilled in the art how to variously employ the present disclosure in substantially any suitable detailed structure.

[0281] The methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate with existing data collection, processing, and storage systems while maintaining access to data in existing formats, frequency ranges, and resolutions that are compatible. While the industrial machine sensor data streaming equipment described herein may collect larger amounts of data (e.g., longer periods of data collection) from sensors over a wider range of frequencies and at greater resolutions than existing data collection systems, the methods and systems may be employed to provide access to data from streams of data representing one or more frequency ranges and / or one or more lines of resolution that are intentionally compatible with existing systems. Furthermore, portions of the streamed data may be identified, extracted, stored, and / or transferred to existing data processing systems to facilitate operation of existing data processing systems that substantially match the operation of existing data processing systems that use existing collection-based data. In this way, newly deployed systems for sensing aspects of industrial machinery, such as aspects of moving parts of industrial machinery, may facilitate the continued use of existing sensed data processing equipment, algorithms, models, pattern recognizers, user interfaces, and the like.

[0282] By identifying existing frequency ranges, formats, and / or resolutions, such as by accessing data structures that define these aspects of the existing data, the higher-resolution streamed data may be configured to represent specific frequencies, frequency ranges, formats, and / or resolutions. This configured streamed data may be stored in a data structure compatible with existing sensed data structures, such that existing processing systems and equipment can access and process the data substantially as if it were existing data. One approach to adapting streamed data for compatibility with existing sensory data may include aligning the streamed data with the existing data, such that portions of the streamed data that align with the existing data can be extracted, stored, and made available for processing by existing data processing methods. Alternatively, the data processing methods may be configured to process portions of the streamed data that correspond to the existing data, such as through alignment, with a method that implements substantially similar functionality to a method used to process the existing data, such as a method for processing data that includes a specific frequency range or a specific resolution.

[0283] The method used to process existing data may be associated with particular characteristics of the sensed data, such as a particular frequency range, the source of the data, etc. As an example, a method for processing bearing sensing information for moving parts of industrial machinery may process data from bearing sensors that fall within a particular frequency range. Thus, the method may be identifiable at least in part by these characteristics of the data being processed. Thus, given a set of conditions, such as the type of moving equipment being sensed, the type of industrial machinery, and the frequency of the sensed data, the data processing system may select an appropriate method. Given such a set of conditions, the industrial machinery data sensing and processing facility may also configure elements, such as data filters, routers, and processors, to process data that meets the conditions.

[0284] FIGS. 1-5 depict a portion of an overall view of an industrial IoT data collection, monitoring, and control system 10. FIG. 2 depicts a mobile ad hoc network ("MANET") 20, which may form secure, temporary network connections 22 (sometimes connected, sometimes isolated) with the cloud 30 or other remote networking systems, allowing network functions to occur within the environment via the MANET 20 without the need for an external network, while at other times allowing information to be transmitted to and from a central location. This allows industrial environments to take advantage of the benefits of networking and control technologies while providing security, such as preventing cyberattacks. The MANET 20 may use cognitive radio technologies 40, including routers 42, MAC 44, physical layer technologies 46, and other technologies that constitute the equivalent of IP protocols. In certain embodiments, the systems depicted in FIGS. 1-5 provide network-sensitive or network-aware transport of data over the network to and from data collectors or heavy industrial machinery.

[0285] Figures 3-4 depict intelligent data collection technologies deployed locally at the edge of an IoT deployment, where heavy industrial machinery is located. This includes various sensors 52, IoT devices 54, data storage capabilities (e.g., data pool 60, or distributed ledger 62) (including intelligent self-organizing storage), sensor fusion (including self-organizing sensor fusion), etc. Interfaces for data collection (including multi-sensory interfaces, tablets, smartphones 58, etc.) are shown. Figure 3 also illustrates a data pool 60 that may collect data exposed by sensors detecting the machine or its condition, and the data in the data pool 60 may be collected for later consumption by local or remote intelligence, for example. The distributed ledger system 62 may distribute storage across local storage in various elements of the environment or more broadly throughout the system. FIG. 4 also illustrates on-device sensor fusion 80, such as for storing data from multiple analog sensors 82 on-device, which may be analyzed locally or in the cloud by machine learning 84, including training a machine based on an initial model created by a human that is augmented by providing feedback when operating the methods and systems disclosed herein (such as based on a measure of success).

[0286] FIG. 1 illustrates a server-based portion of an Industrial IoT system that may be deployed on the cloud or on the premises of an enterprise owner or operator. The server portion includes network coding (including self-organizing network coding and / or auto-configuration) that may configure a network coding model based on feedback measures, network conditions, or the like for highly efficient transport of large amounts of data across the network between the data collection system and the cloud. The network coding may provide a wide range of functionality for intelligence, analytics, remote control, remote operation, remote optimization, various storage configurations, etc., as depicted in FIG. 1. The various storage configurations may include distributed ledger storage to support transaction data or other elements of the system.

[0287] 5 depicts a programmatic data marketplace 70, which may be a self-organized marketplace, such as for making available data collected in an industrial environment, such as from data collectors, data pools, distributed ledgers, and other elements disclosed herein. Additional details regarding the various components and subcomponents of FIGS. 1-5 are provided throughout this disclosure.

[0288] 6, one embodiment of a platform 100 may include a local data collection system 102 that may be located in an environment 104, such as an industrial environment as shown in FIG. 3, to collect data from or about elements of the environment, such as machines, components, systems, subsystems, ambient conditions, states, workflows, processes, and other elements. The platform 100 may be connected to or include part of the industrial IoT data collection, monitoring, and control system 10 depicted in FIGS. 1-5. The platform 100 may include a network data transport system 108 for transporting data to and from the local data collection system 102 over a network 110, for example, one located in a cloud computing environment or on an enterprise premises, or to a host processing system 112 comprised of distributed components that interact with each other to process the data collected by the local data collection system 102. The host processing system 112, referred to in some cases for convenience as the host processing system 112, may include various systems, components, methods, processes, equipment, etc., for enabling automation of data or processing in support of automation, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements within the local environment 104 or network 110. The platform 100 may include one or more local autonomous systems, for example, for enabling autonomous behavior reflecting artificial intelligence or machine-based intelligence, or for enabling automated behavior based on the application of a set of rules or models based on input data from the local data collection system 102, or based on input data from one or more input sources 116, or may include information feeds and inputs from a wide array of sources, including information feeds and inputs within the local environment 104, within the network 110, within the host system 112, or from one or more external systems, databases, or the like.Platform 100 may include one or more intelligent systems 118 that may be located within, integrated with, or serve as input to one or more components of platform 100. Details of these and other components of platform 100 are provided throughout this disclosure.

[0289] The intelligent system 118 may include a cognitive system 120 that allows some degree of cognitive operation as a result of the cooperation of processing elements, such as in a mesh, peer-to-peer, ring, serial, or other architecture where one or more node elements cooperate with other node elements to provide collective, coordinated operation to assist with processing, communication, data collection, etc. The MANET 20 depicted in FIG. 2 may also use cognitive radio technologies, including those that constitute equivalents to IP protocols, such as routers 42, MAC 44, and physical layer technologies 46. In one example, the cognitive system technology stack may include that disclosed in U.S. Patent No. 8,060,017 to Schlicht et al., issued November 15, 2011, and incorporated by reference herein as if fully set forth herein.

[0290] The intelligent system may include a machine learning system 122, such as for learning on one or more datasets. The one or more datasets may include information collected using the local data collection system 102 or other information from input sources 116, such as information that may be used to recognize states, objects, events, patterns, conditions, or the like, which may in turn be used for processing by the host system 112 as input to components of the platform 100 and the industrial IoT data collection, monitoring, and control system 10, or the like. Learning may be human-supervised, such as using one or more input sources 116 to provide datasets with information about the items to be learned, or may be fully automated. Machine learning may use one or more models, rules, semantic understanding, workflows, or other structured or semi-structured understanding of the world, such as for automated optimization of control of a system or process based on feedback or feedforward to a behavioral model for the system or process. One such machine learning technique for semantic and contextual understanding, workflow, or other structured or semi-structured understanding is disclosed in U.S. Patent No. 8,200,775 to Moore, issued June 12, 2012, and incorporated herein by reference as if fully set forth herein. Machine learning may be used to improve the above by adjusting one or more weights, structures, rules, or the like (such as changing functions within the model) based on feedback (such as regarding the success of the model in a given situation) or based on iteration (such as in a recursive process). Machine learning may also be performed in the absence of an underlying model, in cases where a sufficient understanding of the system's underlying structure or behavior is not known, insufficient data is available, or in other cases where it is preferable for various reasons.That is, input sources may be weighted, structured, or the like within a machine learning facility without a priori understanding of the structure, and results (e.g., based on measures of success in achieving various desired objectives) may be continuously fed to the machine learning system to enable it to learn how to achieve the targeted objective. For example, a system may learn to recognize failures, recognize patterns, develop models or functions, develop rules, optimize performance, minimize failure rates, optimize profits, optimize resource utilization, optimize flow (e.g., traffic flow), or optimize many other parameters that may be associated with successful outcomes (e.g., performance across a wide range of environments). Machine learning may use genetic programming techniques to promote or demote one or more input sources, structures, data types, objects, weights, nodes, links, or other elements based on feedback (such that successful elements emerge over a series of generations). For example, alternatively available sensor inputs for data collection system 102 may be arranged in alternative configurations and permutations such that the system, using general programming techniques over a series of data collection events, determines what permutation provides a successful outcome based on various conditions (e.g., conditions of the components of platform 100, conditions of network 110, conditions of data collection system 102, conditions of environment 104, etc.). In embodiments, local machine learning may sequentially turn one or more sensors in multi-sensor data collection system 102 on or off over time while tracking successful outcomes such as contributing to successful failure prediction, contributing to performance metrics (efficiency, effectiveness, return on investment, yield, etc.), contributing to optimization of one or more parameters, identifying patterns (e.g., relating to threats, failure modes, success modes, or the like). For example, the system may learn which set of sensors should be turned on or off under given conditions to achieve the highest utilization of data collector 102.In embodiments, similar techniques may be used to handle optimization of transport of data within platform 100 (e.g., within network 110) using general purpose programming or other machine learning techniques to learn to configure network elements (e.g., configuring network transport paths, configuring network coding types and architectures, configuring network security elements, etc.).

[0291] In an embodiment, the local data collection system 102 may include a high-performance, multi-sensor data collector with numerous novel features for collecting and processing analog and other sensor data. In an embodiment, the local data collection system 102 may be deployed in the industrial facility depicted in FIG. 3. The local data collection system 1002 may also be deployed to monitor other machines, such as machine 2300 in FIGS. 9 and 10, machines 2400, 2600, 2800, 2950, ​​and 3000 depicted in FIG. 12, and machines 3202 and 3204 depicted in FIG. 13. The data collection system 1002 may include an on-board intelligent system 118 (e.g., for learning to optimize the configuration and operation of the data collector, such as configuring permutations and combinations of sensors based on context and conditions). In one example, the data collection system 102 includes a crosspoint switch 130 or other analog switch. The automated, intelligent configuration of the local data collection system 102 may be based on various types of information from various input sources, such as information based on available power, power requirements of sensors, values ​​of collected data (e.g., based on feedback information from other elements of the platform 100), relative values ​​of information (e.g., values ​​based on the availability of other sources of the same or similar information), availability of power (e.g., to power sensors), network conditions, ambient conditions, operating states, operating contexts, operating events, etc.

[0292] FIG. 7 illustrates elements and subcomponents of a data collection and analysis system 1100 for sensor data (e.g., analog sensor data) collected in an industrial environment. As depicted in FIG. 7, embodiments of the methods and systems disclosed herein may include hardware with multiple different modules, beginning with a multiplexer (“MUX”) main board 1104. In embodiments, there may be a MUX option board 1108. The MUX main board 1104 is where sensors connect to the system. These connections are on top to allow for ease of installation. There are then multiple settings on the underside of this board and on the MUX option board 1108, which attaches to the MUX main board 1104 via two headers on either end of the board. In embodiments, the Mux option board has a male header that mates with a female header on the Mux main board. This allows them to be stacked on top of each other, taking up less space.

[0293] In an embodiment, the main MUX board and / or MUX option board are then connected via cables to mother (e.g., having four simultaneous channels) and daughter (e.g., having four additional channels for a total of eight channels) analog boards 1110, where some of the signal conditioning (such as hardware integration) occurs. The signal then travels from the analog board 1110 to an anti-aliasing board (not shown), where some of the potential aliasing is removed. The remaining portion of the aliasing removal occurs on the delta-sigma board 1112, which, along with other signal conditioning and digitization, provides more aliasing protection. The data then travels to the Jennnic board 1114 for further digitization and communication with a computer via USB or Ethernet. In an embodiment, the Jennnic board 1114 can be replaced with a Pic board 1118 for more advanced and efficient data collection and communication. Once the data is in the computer software 1102, the computer software 1102 can manipulate the data to display trends, spectra, waveforms, statistics, and analysis.

[0294] In embodiments, the system is intended to capture any type of data, from volts to 4-20 mA signals. In embodiments, open formats for data storage and communication may be used. In some embodiments, certain portions of the system may be proprietary, particularly portions of the research and data related to analysis and reporting. In embodiments, smart band analysis is a way to break down data into easily analyzed parts that can be combined with other smart bands to create new, more simplified, yet sophisticated analyses. In embodiments, this unique information is captured and graphics are used to depict conditions, as pictorial depictions are more useful to users. In embodiments, complex programs and user interfaces are simplified to allow any user to manipulate data like an expert.

[0295] In an embodiment, the system essentially operates in a big loop. The system starts with software that has a general user interface ("GUI") 1124. In an embodiment, rapid route creation may utilize hierarchical templates. In an embodiment, the GUI is created so that any general user can enter the information themselves in a simple template. Once the template is created, the user can copy and paste whatever they need. Additionally, users can develop their own templates for future ease of use and to institutionalize knowledge. Once the user has entered all of their information and connected all of their sensors, they can begin acquiring data for the system.

[0296] Embodiments of the methods and systems disclosed herein may include inherent electrostatic protection for trigger and vibration inputs. Many critical industrial environments, such as rotating machinery or slow-speed balancing using large belts, where large electrostatic forces that can harm electrical equipment can build up, require appropriate transducer and trigger input protection. Embodiments describe a low-cost yet efficient method for such protection without the need for external auxiliary equipment.

[0297] Typically, vibration data collectors are not designed to handle large input voltages due to their expense and often not required. As technology improves and monitoring costs plummet, there is a need for these data collectors to capture many different types of rotational speed data. In embodiments, the method uses established OptoMOS technology, which allows for high-voltage signal switching upfront, rather than using a more traditional reed relay approach. Many historical concerns about nonlinear zero-crossing or other nonlinear solid-state behavior are eliminated with the passage of weakly buffered analog signals. Furthermore, in embodiments, the printed circuit board wiring topology places all of the individual channel input circuitry as close as possible to the input connector. In embodiments, unique electrostatic protection for trigger and vibration inputs may be pre-installed on the Mux and DAQ hardware to dissipate accumulated charge as the signal passes from the sensor to the hardware. In embodiments, the Mux and analog boards include wider traces and solid-state relays for the upfront circuitry. The design topology may be used to support high-amperage inputs.

[0298] In some systems, the multiplexer is considered redundant and the quality of the signal coming out of the multiplexer is not taken into consideration. A poor quality multiplexer can result in a loss of signal quality of 30 dB or more. Thus, using a 24-bit DAQ with a 110 dB signal-to-noise ratio can result in substantial signal quality loss, and if the signal-to-noise ratio drops to 80 dB at the mux, it may not be much better than a 16-bit system from 20 years ago. In embodiments of this system, a key part of the front of the mux is upfront signal conditioning on the mux for improved signal-to-noise ratio. In embodiments, signal conditioning (range / gain control, integration, filtering, etc.) may be performed upfront on the mux, not just on vibration but also on other signal inputs, before mux switching to achieve the best signal-to-noise ratio.

[0299] In addition to providing a better signal, in embodiments, the multiplexer may also provide continuous monitoring alarm functionality. True continuous systems monitor all sensors constantly, but tend to be expensive. Typical multiplexer systems only monitor a set number of channels at a time, switching from bank to bank of a larger set of sensors. As a result, sensors not currently being sampled are not being monitored. In embodiments, a multiplexer can have continuous monitoring alarm functionality by placing circuitry in the multiplexer that can measure input channel levels for known alarm conditions, even when the data acquisition ("DAQ") is not monitoring the input. Continuous Monitoring Mux Bypass provides a mechanism for channels not currently sampled by the Mux system to continuously monitor for critical alarm conditions via several trigger conditions that are, in turn, passed to the unit in an expedited manner using a hardware interrupt or other means, using a filtered peak-hold circuit or similar. This essentially results in a continuous monitoring of the system, although without the ability to instantly capture the data in question as in a true continuous system. In an embodiment, the adaptive scheduling technique for continuous monitoring and software of the continuous monitoring system adapts and adjusts the data collection sequence based on statistics, analytics, data alarms, and dynamic analytics, allowing the system to collect dynamic spectral data on the alarm sensor immediately after the alarm sounds.

[0300] Another limitation of typical multiplexers is their potentially limited channel count. In one embodiment, the use of a distributed complex programmable logic device (CPLD) chip with dedicated buses for logic control of multiple muxes and data acquisition sections allows the CPLD to control multiple muxes and DAQs, so the system can handle an unlimited number of channels. Interfacing with multiple types of predictive maintenance and vibration transducers requires a lot of switching. This includes AC / DC coupling, 4-20 interfaces, integrated electronic piezoelectric transducers, channel power-down (to save op-amp power), and single-ended or differential grounding options. Also required are digital pots for range and gain control, switches for hardware integration, and AA filtering and triggering. This logic is performed by a series of CPLD chips strategically placed to match the task at hand. A single, large CPLD would require long, densely packed circuit paths. Distributed CPLDs address these issues while also providing greater flexibility. A bus is created, with each CPLD having its own device address, each with its own fixed assignment. In embodiments, multiplexers and DAQs can be stacked together to provide additional input and output channels to the system. For multiple boards (e.g., multiple Mux boards), jumpers are provided to set multiple addresses. Another example allows for up to eight 3-bit jumper-configurable boards. In embodiments, a bus protocol is defined such that each CPLD on the bus can be addressed individually or as a group.

[0301] Typical multiplexers may be limited to collecting only sensors within the same bank. This can be limiting, as detailed analysis can reveal significant value in being able to simultaneously view data from sensors on the same machine. Current systems using traditional fixed-bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) assigned to specific groups during installation. The only way to achieve flexibility is to overlap channels or build significant redundancy into the system, both of which are expensive (and in some cases, cost increases exponentially relative to flexibility). The simplest mux design selects one of many inputs and routes it to a single output line. A bank design consists of a group of these simple building blocks, each handling a fixed group of inputs and routing them to their own outputs. Since inputs generally do not overlap, it is not possible to route inputs from one mux group to another mux. Unlike traditional mux chips, which switch a fixed selection of channels from a fixed group or bank to a single output (e.g., groups of 2, 4, 8, etc.), crosspoint muxes allow any input to be assigned to any output. Previously, crosspoint multiplexers were used for specialized applications like RGB digital video applications and were too noisy to be practical for analog applications like vibration analysis. Another advantage of crosspoint muxes is that their outputs can be disabled by placing them in a high-impedance state. This makes them ideal for output buses, allowing multiple mux cards to be stacked and output buses to be connected without the need for bus switches.

[0302] In an embodiment, this can be addressed by using analog crosspoint switches to collect variable groups of vibration input channels and provide matrix circuitry so the system can access any set of eight channels from the total number of input sensors.

[0303] In embodiments, the ability to control multiple multiplexers using a distributed CPLD chip with dedicated buses for logic control of the multiple Muxes and data acquisition sections is enhanced by a hierarchical Mux, which allows multiple DAQs to collect data from multiple Muxes. The hierarchical Muxes may allow for modular output of more channels, such as 16, 24, or more, onto multiples of an 8-channel card set. In embodiments, this allows for faster data acquisition as well as simultaneous data acquisition of more channels for more complex analysis. In embodiments, the Muxes may be slightly configured to make them portable and use the data acquisition parking feature, turning the SV3X DAQ into a protected system embodiment.

[0304] In embodiments, once the signal leaves the multiplexer and hierarchical mux, it travels to the analog board where other expansions occur. In embodiments, power-saving techniques may be used: powering down analog channels when not in use, powering down component boards, powering down analog signal processing op-amps for unselected channels, and powering down channels on mother and daughter analog boards. The ability to power down component boards and other hardware in the DAQ system's low-level firmware makes high-level application control over power-saving features relatively easy. Explicit control of the hardware is always possible but not required by default. In embodiments, this power-saving benefit may be valuable in protected systems, especially if they are battery- or solar-powered.

[0305] In some embodiments, to maximize the signal-to-noise ratio and provide the best possible data, the peak detector for autoscaling is routed to a separate A / D converter, which provides the highest peak in each set of data and allows the system to quickly scale the data to that peak. For vibration analysis purposes, the A / D converters built into many microprocessors can be inadequate in terms of bit count, channel count, and sampling frequency without significantly slowing down the microprocessor. Despite these limitations, using them for autoscaling purposes is useful. In some embodiments, a separate, less expensive A / D converter with reduced functionality may be used. For each channel of input, after the signal is buffered (usually with appropriate coupling: AC or DC), but before it is conditioned, it is fed directly to the microprocessor or low-cost A / D. Unlike the conditioned signal, which has range, gain, and filter switches, these switches do not change. This allows for simultaneous sampling of the autoscaling data while the input data is processed and fed to a more robust external A / D, and directed to onboard memory using a direct memory access (DMA) method that allows memory access without the need for a CPU. This greatly simplifies the autoscaling process because there is no need to throw switches or allow for settling times, which significantly slows the autoscaling process. Furthermore, data can be collected simultaneously, ensuring the best signal-to-noise ratio. The reduced bit count and other features are typically more than sufficient for autoscaling purposes. In embodiments, the improved integration using both analog and digital methods also creates an innovative hybrid integration that improves or maintains the best possible signal-to-noise ratio.

[0306] In embodiments, sections of the analog board may allow for the routing of raw or buffered trigger channels to other analog channels. This may allow users to route triggers to any channel for analysis and troubleshooting. Systems may have trigger channels for purposes of determining the relative phase between various input data sets or for acquiring meaningful data without unnecessary repetition of unwanted inputs. In embodiments, digitally controlled relays may be used to switch either raw or buffered trigger signals to any of the input channels. It may be desirable to inspect the quality of the trigger pulse, as it may be corrupted for a variety of reasons, including improper placement of the trigger sensor, wiring issues, or, if using optical sensors, poor setup issues such as dirty reflective tape. The ability to view either the raw or buffered signal provides excellent diagnostic and debugging capabilities. Additionally, utilizing recorded data signals for various signal processing techniques, such as variable-rate filtering algorithms, can provide improved phase analysis capabilities.

[0307] In this embodiment, once the signal leaves the analog board, it travels to the delta-sigma board, where a precise voltage reference for the A / D zero reference provides more accurate DC sensor data. The high speed of delta-sigma also allows for higher input oversampling for the delta-sigma A / D to be used for lower sampling rate outputs to minimize anti-aliasing filter requirements. Lower oversampling rates can be used for higher sampling rates. For example, a third-order AA filter set for a minimum sampling requirement of 256 Hz (Fmax 100 Hz) is appropriate for Fmax ranges of 200 Hz and 500 Hz. Then, for Fmax ranges of 1 kHz and above, another high-cutoff AA filter can be used (a second-order filter kicks in at 2.56 times the maximum sampling rate of 128 kHz). In this embodiment, a CPLD can be used as a clock divider for the delta-sigma A / D to achieve lower sampling rates without the need for digital resampling. Specifically, by using a CPLD as a programmable clock divider, a high-frequency crystal reference can be divided down to a lower frequency that is more accurate than the original source over a longer time period. This also minimizes the need for resampling by a delta-sigma A / D.

[0308] Specifically, data travels from the Delta Sigma board to the Jennic board, where an onboard timer can be used to digitally derive the relative phase of the input and trigger channels. In embodiments, the Jennic board also provides the ability to store calibration data and system maintenance repair history data on an onboard card set. In embodiments, the Jennic board allows for the acquisition of long blocks of data at a high sampling rate, as opposed to multiple data sets acquired at different sampling rates, allowing for streaming data in the future and acquiring long blocks of data for advanced analysis.

[0309] In an embodiment, the signal passes through the Jennic board and is then sent to a computer. In an embodiment, computer software is used to add intelligence to the system, starting with the expert system GUI. The GUI provides a graphical expert system with a simplified user interface for defining smart bands and diagnostics, making complex analysis easy for anyone. In an embodiment, this user interface may revolve around the smart band, a simplified approach to complex yet flexible analytics for the average user. In an embodiment, the smart band may be combined with a self-learning neural network for more advanced analytical approaches. In an embodiment, the system may use the machine hierarchy for additional analytical insights. One key part of predictive maintenance is the ability to learn from known information during repairs or inspections. In an embodiment, a graphical approach for back-calculation may improve the smart band and correlation based on known faults or problems.

[0310] In embodiments, there is a smart route to adapt which sensors are collected simultaneously to gain additional correlated intelligence. In embodiments, a smart operations data store ("ODS") allows the system to select data to collect to perform operational deflection shape analysis to further examine the condition of the machine. In embodiments, adaptive scheduling techniques allow the system to modify scheduled data collected for full spectrum analysis across a number of correlated channels (e.g., eight). In embodiments, the system may provide data to enable expanded statistical capabilities for continuous monitoring, as well as ambient and local vibration for combined analysis of changes in ambient and local temperature and vibration levels to identify machine problems.

[0311] In embodiments, the data collector may be controlled by a personal computer (PC) to implement desired data collection commands. In embodiments, the DAQ box may be self-contained and may acquire, process, analyze, and monitor data independently of external PC control. In embodiments, it may include Secure Digital (SD) card storage. In embodiments, utilizing an SD card may provide significant additional storage capabilities. This may prove important for monitoring applications where important data may be permanently stored. Also, in the event of a power outage, the most recent data may be preserved even if it has not been offloaded to another system.

[0312] The current trend is for DAQ systems to communicate with the outside world as much as possible through a network, including wirelessly. In the past, it was common to use either a microprocessor or a microcontroller / microprocessor paired with a PC to control the DAQ system using a dedicated bus. In some embodiments, a DAQ system may consist of one or more microprocessors / microcontrollers, specialized microcontrollers / microprocessors, or dedicated processors focused primarily on the communication aspects with the outside world. These include USB, Ethernet, and wireless, with the ability to provide an IP address or address to host a web page. All communication with the outside world is done using simple text-based menus. The usual array of commands (over 100 in fact) is provided, such as InitializeCard, AcquireData, StopAcquisition, and RetrieveCalibration Info.

[0313] In embodiments, intense signal processing activities, including resampling, weighting, filtering, and spectral processing, may be performed by a dedicated processor, such as a field programmable gate array ("FPGA"), digital signal processor ("DSP"), microprocessor, microcontroller, or combinations thereof. In embodiments, this subsystem may communicate with a communications processing unit via a dedicated hardware bus, which may be facilitated by dual-port memory, semaphore logic, or the like. This embodiment not only provides significant improvements in efficiency, but can also significantly improve processing capabilities, including streaming data as well as other high-end analytical techniques. This negates the need to constantly interrupt key processes, including controlling signal conditioning circuitry, triggering, acquiring raw data using an A / D, directing the A / D output to appropriate onboard memory, and processing that data.

[0314] Embodiments may include sensor overload identification. There is a need for a monitoring system to identify when a sensor is overloaded. There may be situations involving high frequency inputs that saturate standard 100 mv / g sensors (most commonly used in industry), and having the ability to sense an overload improves data quality for better analysis. While a monitoring system can identify when a system is overloaded, in embodiments, the system can look at the sensor voltage to determine if the overload is coming from the sensor and allow the user to obtain another sensor that is more appropriate for the situation or collect the data again.

[0315] In embodiments, radio frequency identification ("RFID") and an inclinometer or accelerometer on the sensor can indicate which machine / bearing the sensor is attached to and what orientation it is in, allowing the software to automatically store the data without user input. In embodiments, a user can place the system on any machine or machinery and the system will automatically configure itself and be ready for data collection within seconds.

[0316] Embodiments may include providing ultrasonic online monitoring by placing ultrasonic sensors inside transformers, motor control centers, breakers, etc., and continuously searching through the sound spectrum for patterns that identify arcing, corona, and other electrical issues that indicate faults or problems. Embodiments may include providing continuous ultrasonic monitoring of rotating elements and bearings of energy production equipment. In embodiments, an analytics engine may be used in ultrasonic online monitoring, as well as to identify other faults by combining the ultrasonic data with other parameters such as vibration, temperature, pressure, heat flux, magnetic field, electric field, current, voltage, capacitance, inductance, and combinations thereof (e.g., simple ratios).

[0317] Embodiments of the methods and systems disclosed herein may include the use of analog crosspoint switches to collect variable groups of vibration input channels. For vibration analysis, it is useful to simultaneously acquire multiple channels from vibration transducers mounted in multiple directions on different parts of a machine (or machines). For example, by acquiring measurements simultaneously, the relative phase of the inputs can be compared for the purpose of diagnosing various mechanical faults. Other types of cross-channel analysis, such as cross-correlation, transfer function, and operational deflection shape ("ODS"), may also be performed.

[0318] Embodiments of the methods and systems disclosed herein may include a precise voltage reference for the A / D zero reference. Some A / D chips provide their own internal zero voltage reference, which is used as a midscale value for external signal conditioning circuitry to ensure that both the A / D and the external op amp use the same reference. While this sounds reasonable in principle, it presents complex problems in practice. Often, these references are essentially based on the power supply voltage using a resistive voltage divider. In many current systems, especially those powered by a PC via a bus such as USB, the power supply voltage often varies significantly with load, resulting in an unreliable reference. This is especially true for delta-sigma A / D chips, which require increased signal processing. Offsets can drift with load, creating problems when digitally calibrating measurements. To compensate for DC drift, it is common to digitally correct the voltage offset, expressed in counts from the A / D. However, in this case, once the appropriate calibration offset is determined for one set of load conditions, it does not apply to other conditions. The absolute DC offset, expressed in counts, no longer applies. As a result, calibration must be performed for all load conditions, which becomes complex, unreliable, and ultimately unmanageable.In an embodiment, an external voltage reference that is simply independent of the supply voltage is used as the zero offset.

[0319] In an embodiment, the system provides a phase-locked loop bandpass tracking filter method for remote balancing of low-speed machinery, such as paper mills, to obtain low-speed rotational speed and phase for balancing purposes, and provides additional analysis from that data. For balancing purposes, balancing at very low speeds may be necessary. A typical tracking filter may be built based on a phase-locked loop or PLL design, with stability and speed range being paramount concerns. In an embodiment, a number of digitally controlled switches are used to select appropriate RC and damping constants. The switches may be performed automatically after measuring the frequency of the incoming tacho signal. Embodiments of the method and system disclosed herein may include digital derivation of the relative phase for the input and trigger channels using onboard timers. In an embodiment, the digital phase derivation uses a digital timer to ensure a precise delay from the trigger event to the precise start of data acquisition. This delay or offset is then refined using interpolation methods to obtain an even more precise offset, and this offset is then applied to the analytically determined phase of the acquired data so that the phase is "essentially" absolute and has precise mechanical meaning, useful especially for one-shot balancing, alignment analysis, etc.

[0320] Embodiments of the methods and systems disclosed herein may include signal processing firmware / hardware. In embodiments, long blocks of data may be acquired at a high sampling rate, as opposed to multiple sets of data taken at different sampling rates. Typically, modern route collection for vibration analysis practices involve collecting data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary from route point to route point based on the specific mechanical analysis requirements at hand. For example, a motor may require a relatively low sampling rate and high resolution to distinguish between running speed harmonics and line frequency harmonics. However, the practical tradeoff here is that more collection time is required to achieve this improved resolution. In contrast, some high-speed compressors or gearsets may not require as precise resolution, but require a much higher sampling rate to measure the amplitude of relatively high-frequency data. Ideally, however, it would be better to collect data with very long sample lengths at a very high sampling rate. When digital acquisition devices first became widespread in the early 1980s, A / D sampling, digital storage, and computing power were not what they are today, so compromises were made between the time required for data acquisition and the desired resolution and accuracy. Because of this limitation, some field analysts refused to abandon analog tape recording systems, which did not suffer from the same drawbacks of digitalization. Some hybrid systems were employed, digitizing and playing back recorded analog data at multiple sampling rates and lengths, but these systems were not automated. As mentioned previously, a more common approach, balancing data acquisition time with analytical capabilities, is to digitally acquire blocks of data at multiple sampling rates and lengths and then digitally store these blocks separately.In embodiments, long data lengths may be collected and stored at the highest practical sampling rate (e.g., 102.4 kHz; corresponding to an Fmax of 40 kHz). This long data block may be acquired in the same time as a shorter length at a lower sampling rate utilized by the a priori method, so that there is no significant delay added to sampling at measurement points, which is always a concern in path collection. In embodiments, analog tape recording of data is digitally simulated with such accuracy that it can be considered substantially continuous or "analog" for many purposes, including those of embodiments of the present disclosure, unless the context dictates otherwise.

[0321] Embodiments of the methods and systems disclosed herein may include storing calibration data and maintenance history on an onboard card set. Many data collectors that rely on a PC interface to function store their calibration coefficients on the PC. This is especially true for complex data collectors with many signal paths and therefore where calibration tables can be very large. In embodiments, the calibration coefficients are stored in flash memory, and this data and other important information are, for all practical purposes, permanently stored. This information may include nameplate information such as individual component serial numbers, firmware or software version numbers, maintenance history, and calibration tables. In embodiments, the DAQ box remains calibrated and retains all of this important information, regardless of which computer the box is ultimately connected to. A PC or external device can poll this information at any time for portability or information exchange purposes.

[0322] Embodiments of the methods and systems disclosed herein may include rapid route creation using hierarchical templates. In the field of vibration monitoring, similar to general parametric monitoring, it is necessary to establish the existence of data monitoring points in a database or functional equivalent. These points are associated with various attributes, such as transducer attributes, data acquisition settings, machine parameters, and operational parameters. Transducer attributes include probe type, probe mounting type, and probe mounting orientation or axial direction. Measurement-related data acquisition attributes include sampling rate, data length, power and coupling requirements for electronic piezoelectric probes, hardware integration requirements, 4-20 or voltage interface, range and gain settings (if applicable), and filter requirements. Machine parametric requirements associated with a particular point include operating speed, bearing type, and bearing parametric data, including pitch diameter, number of balls, inner race, and outer race diameter for rolling element bearings. For tilting pad bearings, this includes the number of pads. Required parameters for measurement points on equipment such as gearboxes include, for example, the number of gear teeth on each gear. For induction motors, this includes the number of rotor bars and poles; for compressors, the number of blades and vanes; and for fans, the number of blades. For belt / pulley systems, the number of belts and the associated belt-pass frequency can be calculated from the pulley dimensions and center-to-center distance. For measurements near the coupling, the coupling type and number of teeth on geared couplings may be required. Operating parametric data includes operating load expressed in megawatts, flow rate (either air or fluid), percentage, horsepower, feet per minute, etc. Ambient temperature, operating temperature, pressure, humidity, etc. may also be relevant. As can be seen, the setup information required for each individual measurement point can be significant. It is also crucial for legitimate analysis of the data. Specific information about the machine, equipment, and bearings is essential for identifying failure frequencies and predicting the various types of specific failures that may be expected.Transducer attributes and data collection parameters are essential for proper interpretation of the data and also provide a window into the types of analysis techniques that can be used. Traditional methods of entering this data are manual and tedious, typically at the lowest hierarchical level (e.g., bearing level for machine parameters and transducer level for data collection configuration information). However, the importance of the hierarchical relationships required to organize data cannot be emphasized enough, not only for data storage and transfer, but also for analysis and interpretation purposes. This discussion focuses primarily on data storage and transfer. While the aforementioned configuration information is, by its nature, highly redundant at the lowest hierarchical level, it can be stored quite efficiently in this form due to its strong hierarchical nature. In embodiments, the hierarchy can be utilized when copying data in the form of templates. As an example, a hierarchical storage structure suitable for many purposes is defined from general to specific: company, plant or site, unit or process, machine, equipment, shaft element, bearing, and transducer. Copying data associated with a specific machine, equipment, shaft element, or bearing is much easier than copying only at the lowest transducer level. In embodiments, the system not only stores data in this hierarchical manner, but also uses these hierarchical templates to robustly support rapid copying of data. The similarity of elements at specific hierarchical levels lends itself to effective data storage in a hierarchical format. For example, many machines share common elements such as motors, gearboxes, compressors, belts, and fans. More specifically, many motors can be easily categorized as either induction, DC, fixed, or variable speed. Many gearboxes can be categorized into general groups such as input / output, input pinion / intermediate pinion / output pinion, four-prong, etc. Within factories and companies, similar types of equipment are purchased and standardized in large quantities for cost and maintenance reasons. This results in a significant amount of overlap between similar types of equipment, creating a great opportunity to leverage a hierarchical template approach.

[0323] Embodiments of the methods and systems disclosed herein may include a smart band. A smart band refers to any processed signal characteristics derived from any dynamic input or group of inputs for the purpose of analyzing the data and achieving a correct diagnosis. Additionally, a smart band may include mini-diagnostics or relatively simple diagnostics for the purpose of achieving more robust and complex diagnostics. Historically, in the field of mechanical vibration analysis, alarm bands have been used to define spectral frequency bands of interest for the purpose of analyzing and / or trending significant vibration patterns. Alarm bands typically consist of spectral (amplitude plotted against frequency) regions defined between low- and high-frequency boundaries. The amplitudes between these boundaries are summed in the same way to calculate overall amplitude. Smart bands are more flexible in that they can refer not only to specific frequency bands but also to groups of spectral peaks such as harmonics of a single peak, true peak levels or crest factors derived from time waveforms, wholes derived from vibration envelope spectra or other specialized signal analysis techniques, or logical combinations (e.g., AND, OR, XOR) of these signal attributes. Additionally, a myriad of other parametric data, including system load, motor voltage and phase information, bearing temperature, flow rate, etc., can similarly be used as the basis for forming additional Smart Bands. In embodiments, Smart Band symptoms can be used as components of an expert system that utilizes these inputs to derive engine diagnoses. Some of these mini-diagnoses may then be used as Smart Band symptoms (the Smart Band may include diagnoses) for more general diagnoses.

[0324] Embodiments of the methods and systems disclosed herein may include a neural net expert system using a smart band. Typical vibration analysis engines are rule-based (i.e., they use a list of expert rules that, when satisfied, trigger a specific diagnosis). In contrast, a neural approach utilizes weighted triggering of multiple input stimuli to guide smaller analysis engines, or neurons, which then provide simplified weighted outputs to other neurons. The outputs of these neurons are classified as smart bands and fed to other neurons. This creates a more multi-layered approach to expert diagnosis, as opposed to the one-off approach of rule-based systems. In embodiments, the expert system utilizes this neural approach using a smart band, but this does not preclude rule-based diagnoses from being reclassified as smart bands as additional stimuli utilized by the expert system. From this perspective, it can be viewed as a hybrid approach, although at the highest level it is essentially neural.

[0325] Embodiments of the methods and systems disclosed herein may include the use of a database hierarchy in analyzing smart band symptoms and diagnoses, which may be assigned to various hierarchical database levels. For example, a smart band may call "loose" at the bearing level, trigger "loose" at the equipment level, and trigger "loose" at the machine level. Another example would be to run a smart band diagnostic called "horizontal plane phase reversal" across a coupling, which would generate a smart band diagnostic for "vertical coupling misalignment" at the machine level.

[0326] Embodiments of the methods and systems disclosed herein may include an expert system GUI. In embodiments, the system employs a graphical approach for defining the expert system's smart bands and diagnostics. Entering symptoms, rules, or more general smart bands to create specific machine diagnoses can be tedious and time-consuming. One way to make this process faster and more efficient is to provide a graphical means for wiring. The proposed graphical interface consists of four main components: a symptom parts bin, a diagnostics bin, a tool bin, and a graphical wiring area ("GWA"). In embodiments, the symptom parts bin contains various spectral, waveform, envelope, and any type of signal processing feature or grouping of features, such as spectral peaks, spectral harmonics, waveform true peaks, waveform crest factors, spectral alarm bands, etc. Additional features may be assigned to each part. For example, a part of a spectral peak may be assigned the frequency or order (multiple) of road speed. Some parts may be predefined or user defined, such as 1x, 2x, 3x running speed, 1x, 2x, 3x gear mesh, 1x, 2x, 3x blade path, number of motor rotor bars x running speed, etc.

[0327] In embodiments, the diagnostic bin contains various predefined diagnoses as well as user-defined diagnoses such as misalignment, unbalance, looseness, and bearing defects. Like parts, diagnoses may be used as parts for the purpose of building more complex diagnoses. In embodiments, the tool bin contains logical operations such as AND, OR, and XOR, or other methods of combining the various parts described above, such as Find Max, Find Min, interpolation, averaging, and other statistical operations. In embodiments, the graphical wiring area contains parts from the parts bin or diagnoses from the diagnostic bin, which may be combined using tools to create a diagnosis. The various parts, tools, and diagnoses are simply represented by icons that are graphically wired together in a desired manner.

[0328] Embodiments of the methods and systems disclosed herein may include a graphical approach for back-calculation definition. In embodiments, the expert system also provides an opportunity for the system to learn. If it is already known that a unique set of stimuli or smart band corresponds to a particular disorder or diagnosis, it is possible to back-calculate a set of coefficients that, when applied to a future set of similar stimuli, will arrive at the same diagnosis. In embodiments, a best-fit approach can be used when there are multiple sets of data. Unlike the smart band GUI, this embodiment self-generates the wiring diagram. In embodiments, the user may adjust settings for the back-propagation approach and use a database browser to match a particular set of data to the desired diagnosis. In embodiments, the desired diagnosis may be created or customized using the smart band GUI. In embodiments, the user may then press the GENERATE button, and the dynamic wiring of symptoms to diagnoses may be displayed on the screen as it works through the algorithm to achieve the best fit. In embodiments, once the mapping process is complete, various statistics are presented detailing how well the mapping process progressed. In some cases, mapping may not be achieved, for example, if the input data is all zeros or if the input data is incorrect (misassigned). Embodiments of the methods and systems disclosed herein may include bearing analysis methods, which may be used in combination with computer-aided design ("CAD"), predictive deconvolution, minimum variance distortionless response ("MVDR"), and spectral sum of harmonics.

[0329] In recent years, there has been a strong trend toward energy conservation, resulting in an influx of variable frequency drives and variable speed machines. In an embodiment, a bearing analysis method is provided. In an embodiment, torsional vibration detection and analysis using transient signal analysis is provided to provide advanced torsional vibration analysis for a more comprehensive method of diagnosing torsional force-related machines (e.g., machines with rotating parts). Due primarily to the declining cost of motor speed control systems and increased awareness of energy consumption, exploiting the enormous energy savings potential of load control has become economically justified. Unfortunately, vibration is an often overlooked design issue. If a machine is designed to operate at only one speed, it is much easier to design the physical structure accordingly to avoid structural and torsional mechanical resonances that dramatically reduce the machine's mechanical integrity. This includes structural characteristics such as the type of material used, its weight, stiffening requirements and placement, bearing type, bearing location, and base support constraints. Even when a machine operates at one speed, designing the structure to minimize vibration is a daunting task that may require computer modeling, finite element analysis, and field testing. In many cases, the mix of variable speeds makes it impossible to design for all desired speeds. The problem then becomes one of minimization, for example, through speed avoidance. This is why many modern motor controllers are typically programmed to skip or quickly pass through certain speed ranges or bands. In some embodiments, identifying the speed ranges may be included in a vibration monitoring system. Non-torsional structural resonances are typically fairly easy to detect using traditional vibration analysis techniques. However, this is not the case for torsional resonances. One particular area of ​​current attention is the increasing incidence of torsional resonance problems, apparently due to increased torsional stresses with speed changes and / or operation of equipment at torsional resonance speeds. Unlike non-torsional structural resonances, which typically manifest their effect in dramatically increased casing or external vibrations, torsional resonances generally do not exhibit such effects.In the case of shaft torsional resonance, the torsional motion induced by the resonance may only be identifiable by looking for changes in speed and / or phase. Current standard methods for analyzing torsional vibration involve the use of specialized instrumentation. The methods and systems disclosed herein allow for the analysis of torsional vibration without the use of such specialized instrumentation. This may consist of stopping the machine and employing strain gauges and / or other specialized fixtures, such as speed encoder plates and / or gears. Friction wheels are another option, but typically require manual implementation and specialized analysts. These techniques are generally prohibitively expensive or inconvenient. Due to declining costs and increased convenience (e.g., remote access), continuous vibration monitoring systems are becoming increasingly popular. In embodiments, the vibration signal alone is capable of identifying torsional speed and / or phase changes. In embodiments, transient analysis techniques may be utilized to distinguish torsional-induced vibration from mere speed changes due to process control. In embodiments, factors for identification may focus on one or more of the following aspects: The rate of speed changes from variable speed motor controls tends to be relatively slow, sustained, and deliberate, while torsional speed changes tend to be short, impulsive, and less sustained. Additionally, the small magnitude of torsional-related speed changes relative to shaft rotational speed suggests that monitoring phase behavior will show quick or transient speed bursts, as opposed to the slow phase changes (represented by Bode or Nyquist plots) that speed the machine up and down.

[0330] Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When signals are digitally integrated using software, the spectral low-end frequency data is essentially multiplied by a function that explodes as soon as its amplitude approaches zero, creating what is known in the industry as the "ski slope" effect. The ski slope amplitude is essentially the noise floor of the instrument. A simple solution to this is a traditional hardware integrator, which can run at a signal-to-noise ratio much greater than that of the already digitized signal. Also, the amplification factor can be limited to an appropriate level, so multiplication by very large numbers is essentially prohibited. However, at increasingly higher frequencies, the original amplitude, which may have been well above the noise floor, is multiplied by a very small number (1 / f), dropping it well below the noise floor. Hardware integrators have a fixed noise floor, and although the floor is low, it does not scale down for low-amplitude high-frequency data. In contrast, the s...

Claims

1. sampling the signal at a streaming sample rate, thereby generating a plurality of samples of the signal; using a signal routing circuit to assign a first portion of a plurality of samples of the signal to a first signal analyzing circuit, the selected portion being assigned to the first signal analyzing circuit based on a first signal analysis sampling rate being less than the streaming sample rate; using the signal routing circuitry to assign a second portion of the plurality of samples of the signal to a second signal analysis circuit, the selected portion being assigned to the second signal analysis circuit based on a second signal analysis sampling rate being less than the streaming sample rate; and storing a plurality of samples of the signal, an output of the first signal analyzing circuit, and an output of the second signal analyzing circuit; wherein the allocated first portion of the stored plurality of samples and the allocated second portion of the stored plurality of samples are tagged with an index that references the corresponding stored signal analysis output.

2. The method of claim 1 , wherein allocating with the signal routing circuitry includes integrating a plurality of samples based on a ratio of the signal analysis sampling rate to the streaming sample rate.

3. The method of claim 1 , wherein allocating with the signal routing circuitry comprises selecting samples of the signal based on a ratio of the signal analysis sampling rate to the streaming sample rate.

4. The method of claim 1 , wherein the streaming sample rate is at least twice as fast as the dominant frequency of the signal.

5. The method of claim 1 , wherein the ratio of the signal analysis sampling rate to the streaming sample rate determines the number of complementary binary bits of data at the outputs of the first signal analysis circuit and the second signal analysis circuit.

6. 6. The method of claim 5, wherein the number of supplemental binary bits includes 1 when the streaming sample rate is at least two times but less than four times the signal analysis sampling rate.

7. The method of claim 5, wherein the number of supplemental binary bits includes 2 when the streaming sample rate is at least four times but less than eight times the signal analysis sampling rate.

8. The method of claim 1 , further comprising receiving a signal from a sensor that detects a condition of an industrial machine, the signal corresponding to the condition of the industrial machine.

9. determining a severity of a condition of the industrial machine based on at least one of a plurality of samples of the signal, an output of the first signal analysis circuit, or an output of the second signal analysis circuit; determining an industrial machine service recommendation for the condition of the industrial machine based on the severity; and generating a signal indicative of the industrial machine service recommendation; The method of claim 8 further comprising:

10. 10. The method of claim 9, wherein determining the industrial machine service recommendation includes utilizing an intelligent system to apply a machine fault detection or classification algorithm to one or more of: (i) the plurality of samples of the signal, (ii) the output of the first signal analysis circuit, (iii) the output of the second signal analysis circuit, or (iv) the severity.

11. 1. A system including one or more processors and a non-transitory computer-readable storage medium having a plurality of instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations, comprising: The operation is sampling the signal at a streaming sample rate, thereby generating a plurality of samples of the signal; using a signal routing circuit to assign a first portion of a plurality of samples of the signal to a first signal analyzing circuit, the selected portion being assigned to the first signal analyzing circuit based on a first signal analysis sampling rate being less than the streaming sample rate; using the signal routing circuitry to assign a second portion of the plurality of samples of the signal to a second signal analysis circuit, the selected portion being assigned to the second signal analysis circuit based on a second signal analysis sampling rate being less than the streaming sample rate; and storing a plurality of samples of the signal, an output of the first signal analyzing circuit, and an output of the second signal analyzing circuit; the allocated first portion of the stored plurality of samples and the allocated second portion of the stored plurality of samples are tagged with an index that references the corresponding stored signal analysis output.

12. The system of claim 11 , wherein allocating with the signal routing circuitry includes integrating a plurality of samples based on a ratio of the signal analysis sampling rate to the streaming sample rate.

13. The system of claim 11 , wherein allocating with the signal routing circuitry comprises selecting samples of the signal based on a ratio of the signal analysis sampling rate to the streaming sample rate.

14. The system of claim 11 , wherein the streaming sample rate is at least twice as fast as the dominant frequency of the signal.

15. 12. The system of claim 11, wherein the ratio of the signal analysis sampling rate to the streaming sample rate determines the number of complementary binary bits of data in the outputs of the first signal analysis circuit and the second signal analysis circuit.

16. 16. The system of claim 15, wherein the number of supplemental binary bits includes 1 when the streaming sample rate is at least two times but less than four times the signal analysis sampling rate.

17. The system of claim 15, wherein the number of supplemental binary bits includes 2 when the streaming sample rate is at least four times but less than eight times the signal analysis sampling rate.

18. The system of claim 15 , further comprising receiving a signal from a sensor that detects a condition of an industrial machine, the signal corresponding to the condition of the industrial machine.

19. determining a severity of a condition of the industrial machine based on at least one of a plurality of samples of the signal, an output of the first signal analysis circuit, or an output of the second signal analysis circuit; determining an industrial machine service recommendation for the condition of the industrial machine based on the severity; and generating a signal indicative of the industrial machine service recommendation; The system of claim 18 further comprising:

20. 20. The system of claim 19, wherein determining the industrial machine service recommendation includes utilizing an intelligent system to apply a machine fault detection or classification algorithm to one or more of: (i) the plurality of samples of the signal, (ii) the output of the first signal analysis circuit, (iii) the output of the second signal analysis circuit, or (iv) the severity.

Images