Systems, methods, devices, and platforms for industrial internet of things

Abstract

In example embodiments, an industrial technology stack for an industrial environment includes a set of computational resources and a set of layers executed by the set of computational resources, the set of layers including a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer. In example embodiments, the industrial technology stack may include one or more artificial intelligence models for implementing one or more components of one or more layers of the set of layers.

Description

SYSTEMS, METHODS, DEVICES, AND PLATFORMS FOR INDUSTRIAL INTERNET OF THINGSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to the following U.S. Patent Applications: Serial No. 63 / 625,594, filed January 26, 2024; Serial No. 63 / 638,596, filed April 25, 2024; and Serial No. 63 / 639,909, filed April 29, 2024. Each of the above applications is hereby incorporated by reference in its entirety as if fully set forth herein.BACKGROUNDFIELD

[0002] The present disclosure relates to the field of enterprise management platforms, more particularly involving data management, artificial intelligence, network connectivity and digital twins.DESCRIPTION OF THE RELATED ART

[0003] Industrial environments, such as environments for large scale manufacturing (such as manufacturing of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Industrial environments are widely populated with large, complex, heavy machines that are designed to have very relatively long working lifetimes and have ongoing service requirements, including requirements for scheduled maintenance and for often unanticipated repairs. Many of the large industrial machines that require ongoing maintenance, service and repairs are involved in high stakes production processes and other processes, such as energy production, manufacturing, mining, drilling, and transportation, that preferably involve minimal or no interruption. An unanticipated problem, or an extended delay in a service operation that requires a shutdown of a machine that is critical to such a process can cost thousands, or even millions of dollars per day.

[0004] Historically, data has been collected in industrial environments by human beings using dedicated data collectors, often recording batches of specific sensor data on media, such as tape or a hard drive, for later analysis. Batches of data have historically been returned to a central office for analysis, such as undertaking signal processing or other analysis on the data collected by various sensors, after which analysis can be used as a basis for diagnosing problems in an environment and / or suggesting ways to improve operations. This work has historically taken place on a time scale of weeks or months, and has been directed to limited data sets.

[0005] The emergence of the Internet of Things (loT) has made it possible to connect continuously to, and among, a much wider range of devices. Most such devices are consumer devices, such as lights, thermostats, and the like. With the proliferation of vibration sensors and other Industrial1Interet of Things (IIoT) sensors, there are vast amounts of data available relating to industrial environments. This data is useful in predicting the need for maintenance and far classifying potential issues in the industrial environments. There are, however, many unexplored uses for vibration sensor data and other IIoT sensor data that can improve the operation and uptime of the industrial environments and provide industrial entities with agility in responding to problems before the problems become catastrophic.

[0006] More complex industrial environments remain more difficult, as the range of available data is often limited, and the complexity of dealing with data from multiple sensors makes it much more difficult to produce “smart” solutions that are effective for the industrial sector. For example, in spite of availability of all such data, industrial experts still struggle to properly process all this data because of its sheer size, and thus may not be able to determine faults in the industrial environment when required. 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.

[0007] Conventional machine vision systems are made of a combination of optics, lighting, sensors and software and aim to replicate the function of the human eye. Such systems typically create an image of an object by capturing and processing the reflected light from the object. An optical lens system typically directs the reflected light to an image sensor device, such as a charge coupled device (CCD) or complementary metal oxide semiconductor (CMOS) device, among others. Such image sensor devices contain arrangements, such as matrices or arrays, of small, accurately spaced photo sensitive elements fabricated using integrated circuit technology. The sensor device converts the light falling on it, through the lens system, into analog electrical signals corresponding to light intensity. The object image is thus broken down into an array of individual picture elements, or pixels. An analog-to-digital converter is used to convert analog voltage outputs of respective, elements into digital values. If the voltage level for each pixel is given either 0 or 1 value depending on whether the analog voltage exceeds some threshold intensity measure, it is called a binary system. In contrast, a gray scale system assigns cardinal values (e.g., in a range of zero to 256), depending on the analog intensity, to each pixel. Thus, in addition to black and white, many different shades of gray can be distinguished. A gray-scale image may be seen to have one channel, represented by a 2-D matrix of pixels having pixel values in the range of, for example, 0 to 255. A color image on the other hand represents the brightness and color of the pixels in an image by the three primary color values: R (red), G (green), and B (blue). Thus, color images have red, green, and blue (RGB) channels, each representing RGB components of the image. This raw data captured by the image sensor is then sent to an image processing system for analysis. The image processing system then processes the raw data to extract useful information to analyze the image and make decisions on such analysis. The image processing system may include a pre-processing function to enhance the image quality. For example, such processing may involve image scaling, noise reduction, color adjustment, brightness adjustment, white balance adjustment, sharpness, adjustment, contrast adjustment and the like. Further the image may be analyzed using machine2learning or other algorithms to identify one or more obj ects in the image and determine the position and orientation of such objects.

[0008] While vision technology has improved significantly in the past few years, most of the improvements relate to processing of the image data captured by vision sensors and may be attributed to the use of big data, sophisticated machine learning algorithms like convolutional neural networks (CNNs) and graphical processing units (GPUs) for processing of the image data. The conventional vision technology, however, has significant limitations, specifically with respect to capturing of the raw data about an object or a scene. For example, the optical lenses in conventional vision systems attempt to enable extraction of information by relying on focusing techniques that produce images that have good clarity to the human eye. However, the attempt to get an object into focus in fact, results in discarding a large amount of relevant information that could otherwise be used in a system, including relevant optical properties.BRIEF DESCRIPTION OF THE FIGURES

[0009] FIG. 1 through FIG. 5 are diagrammatic views that depict portions of an overall view of an industrial Internet of Things (loT) data collection, monitoring and control system in accordance with the present disclosure.

[0010] FIG. 6 is a diagrammatic view of a platform including a local data collection system disposed in an industrial environment for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements in accordance with the present disclosure.

[0011] FIG. 7 is a diagrammatic view that depicts elements of an industrial data collection system for collecting analog sensor data in an industrial environment in accordance with the present disclosure.

[0012] FIG. 8 is a diagrammatic view of a rotating or oscillating machine having a data acquisition module that is configured to collect waveform data in accordance with the present disclosure.

[0013] FIG. 9 is a diagrammatic view of an exemplary tri -axial sensor mounted to a motor bearing of an exemplary rotating machine in accordance with the present disclosure.

[0014] FIG. 10 is a diagrammatic view of components and interactions of a data collection architecture involving application of cognitive and machine learning systems to data collection and processing in accordance with the present disclosure.

[0015] FIG. 11 is a diagrammatic view of components and interactions of a data collection architecture involving application of a platform having a cognitive data marketplace in accordance with the present disclosure.

[0016] FIG. 12 is a diagrammatic view of components and interactions of a data collection architecture involving application of a self-organizing swarm of data collectors in accordance with the present disclosure.

[0017] FIG. 13 is a diagrammatic view of components and interactions of a data collection architecture involving application of a haptic user interface in accordance with the present disclosure.3

[0018] FIG. 14 is a diagrammatic view of a multi-format streaming data collection system in accordance with the present disclosure.

[0019] FIG. 15 is a diagrammatic view of combining legacy and streaming data collection and storage in accordance with the present disclosure.

[0020] FIG. 16 is a diagrammatic view of industrial machine sensing using both legacy and updated streamed sensor data processing in accordance with the present disclosure.

[0021] FIG. 17 is a diagrammatic view of an industrial machine sensed data processing system that facilitates portal algorithm use and alignment of legacy and streamed sensor data in accordance with the present disclosure.

[0022] FIG. 18 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0023] FIG. 19 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument having an alarms module, expert analysis module, and a driver API to facilitate communication with a cloud network facility in accordance with the present disclosure.

[0024] FIG. 20 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument and first in, first out memory architecture to provide a real time operating sy stem in accordance with the present disclosure.

[0025] FIG. 21 is a diagrammatic view of components and interactions of a data collection architecture involving a multiple streaming data acquisition instrument receiving analog sensor signals and digitizing those signals to be obtained by a streaming hub server in accordance with the present disclosure.

[0026] FIG. 22 is a diagrammatic view of components and interactions of a data collection architecture involving a master raw data server that processes new streaming data and data already- extracted and processed in accordance with the present disclosure.

[0027] FIG. 23, FIG. 24, and FIG. 25 are diagrammatic views of components and interactions of a data collection architecture involving a processing, analysis, report, and archiving server that processes new streaming data and data already extracted and processed in accordance with the present disclosure.

[0028] FIG. 26 is a diagrammatic view of components and interactions of a data collection architecture involving a relation database server and data archives and their connectivity with a cloud network facility in accordance with the present disclosure.

[0029] FIG. 27 through FIG. 32 are diagrammatic views of components and interactions of a data collection architecture involving a virtual streaming data acquisition instrument receiving analog sensor signals from an industrial environment connected to a cloud network facility- in accordance with the present disclosure.4

[0030] FIG. 33 through FIG. 40 are diagrammatic views of components and interactions of a data collection architecture involving data channel methods and systems for data collection of industrial machines in accordance with the present disclosure.

[0031] FIG. 41 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0032] FIG. 42 and FIG. 43 are diagramm atic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0033] FIG. 44 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0034] FIGS. 45 and 46 are diagrammatic views that depict an embodiment of a system for data collection in accordance with the present disclosure.

[0035] FIGS. 47 and 48 are diagrammatic views that depict, an embodiment of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0036] FIG. 49 depicts an embodiment of a data monitoring device incorporating sensors in accordance with the present disclosure.

[0037] FIGS. 50 and 51 are diagrammatic views that depict embodiments of a data monitoring device in communication with external sensors in accordance with the present disclosure.

[0038] FIG. 52 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0039] FIG. 53 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0040] FIG. 54 is a diagrammatic view that depicts embodiments of a data monitoring de vice with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0041] FIG. 55 is a diagrammatic view that depicts embodiments of a system for data collection in accordance with the present disclosure.

[0042] FIG. 56 is a diagrammatic view that depicts embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0043] FIG. 57 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0044] FIGS. 58 and 59 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0045] FIGS. 60 and 61 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0046] FIGS. 62 and 63 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0047] FIGS. 64 and 65 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.5

[0048] FIG. 66 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0049] FIGS. 67 and 68 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0050] FIG. 69 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0051] FIG. 70 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0052] FIGS. 71 and 72 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0053] FIGS. 73 and 74 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0054] FIG. 75 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0055] FIGS. 76 and 77 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0056] FIG. 78 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0057] FIGS. 79 and 80 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0058] FIGS. 81 and 82 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0059] FIG. 83 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0060] FIGS. 84 and 85 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0061] FIG. 86 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0062] FIGS. 87 and 88 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0063] FIGS. 89 and 90 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0064] FIG. 91 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0065] FIGS. 92 and 93 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.6

[0066] FIG. 94 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0067] FIGS. 95 and 96 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0068] FIGS. 97 and 98 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0069] FIGS. 99, 100, and 101 are diagrammatic views of components and interactions of a data collection architecture involving a collector of route templates and the routing of data collectors in an industrial environment in accordance with the present disclosure.

[0070] FIG. 102 is a diagrammatic view that depicts a monitoring sy stem that employs data collection bands in accordance with the present disclosure.

[0071] FIG. 103 is a diagrammatic view that depicts a system that employs vibration and other noise in predicting states and outcomes in accordance with the present disclosure.

[0072] FIG. 104 is a diagrammatic view that depicts a system for data collection in an industrial environment in accordance with the present disclosure.

[0073] FIG. 105 is a diagrammatic view that depicts an apparatus for data collection in an industrial environment in accordance with the present disclosure.

[0074] FIG. 106 is a schematic flow diagram of a procedure for data collection in an industrial environment in accordance with the present disclosure.

[0075] FIG. 107 is a diagrammatic view that depicts a system for data collection in an industrial environment in accordance with the present disclosure.

[0076] FIG. 108 is a diagrammatic view that depicts an apparatus for data collection in an industrial environment in accordance with the present disclosure.

[0077] FIG. 109 is a schematic flow diagram of a procedure for data collection in an industrial environment in accordance with the present disclosure.

[0078] FIG. 110 is a diagrammatic view that depicts industry-specific feedback in an industrial environment in accordance with the present disclosure.

[0079] FIG. I ll is a diagrammatic view that depicts an exemplary user interface for smart band configuration of a system for data collection in an industrial environment in accordance with the present disclosure.

[0080] FIG. 112 is a diagrammatic view that depicts a graphical approach 11300 for back- calculation in accordance with the present disclosure.

[0081] FIG. 1 13 is a diagrammatic view that depicts a wearable haptic user interface device for providing haptic stimuli to a user that is responsive to data collected in an industrial environment by a system adapted to collect data in the industrial environment in accordance with the present disclosure.

[0082] FIG. 114 is a diagrammatic view that depicts an augmented reality display of heat maps based on data collected in an industrial environment by a system adapted to collect data in the environment in accordance with the present disclosure.7

[0083] FIG. 115 is a diagrammatic view that depicts an augmented reality display including real time data overlaying a view of an industrial environment in accordance with the present disclosure.

[0084] FIG. 116 is a diagrammatic view that depicts a user interface display and components of a neural net in a graphical user interface in accordance with the present disclosure.

[0085] FIG. 117 is a diagrammatic view of components and interactions of a data collection architecture involving swarming data collectors and sensor mesh protocol in an industrial environment in accordance with the present disclosure.

[0086] FIG. 118 is a diagrammatic view that depicts a data collection system according to some aspects of the present disclosure.

[0087] FIG. 119 is a diagrammatic view that depicts a system for self-organized, network-sensitive data collection in an industrial environment in accordance with the present disclosure.

[0088] FIG. 120 is a diagrammatic view that depicts an apparatus for self-organized, networksensitive data collection in an industrial environment in accordance with the present disclosure.

[0089] FIG. 121 is a diagrammatic view that depicts an apparatus for self-organized, networksensitive data collection in an industrial environment in accordance with the present disclosure.

[0090] FIG. 122 is a diagrammatic view that depicts an apparatus for self-organized, network- sensitive data collection in an industrial environment in accordance with the present disclosure.

[0091] FIG. 123 and FIG. 124 are diagrammatic views that depict embodiments of transmission conditions in accordance with the present disclosure.

[0092] FIG. 125 is a diagrammatic view that depicts embodiments of a sensor data transmission protocol in accordance with the present disclosure.

[0093] FIG. 126 and FIG. 127 are diagrammatic views that depict embodiments of benchmarking data in accordance with the present disclosure.

[0094] FIG. 128 is a diagrammatic view that depicts embodiments of a system for data collection and storage in an industrial environment in accordance with the present disclosure.

[0095] FIG. 129 is a diagrammatic view that depicts embodiments of an apparatus for selforganizing storage for data collection for an industrial system in accordance with the present disclosure.

[0096] FIG. 130 is a diagrammatic view that depicts embodiments of a storage time definition in accordance with the present disclosure.

[0097] FIG. 131 is a diagrammatic view that depicts embodiments of a data resolution description in accordance with the present disclosure.

[0098] FIG. 132 and FIG. 133 diagrammatic views of an apparatus for self-organizing network coding for data collection for an industrial system in accordance with the present disclosure.

[0099] FIG. 134 and FIG. 135 diagrammatic views of data marketplace interacting with data collection in an industrial system in accordance with the present disclosure.

[0100] FIG. 136 is a diagrammatic view that depicts a smart heating system as an element in a netw ork for an industrial Internet of Things ecosystem in accordance with the present disclosure.

[0101] FIG. 137 is a diagrammatic view that depicts an architecture, its components and functional relationships for an industrial Internet of Things solution in accordance with the present disclosure.8

[0102] FIG. 138 is a schematic illustrating an example of a sensor kit deployed in an industrial setting according to some embodiments of the present disclosure.

[0103] FIG. 139 is a schematic illustrating an example of a sensor kit network having a star network topology according to some embodiments of the present disclosure.

[0104] FIG. 140 is a schematic illustrating an example of a sensor kit network having a mesh network topology according to some embodiments of the present disclosure.

[0105] FIG. 141 is a schematic illustrating an example of a sensor kit network having a hierarchical network topology according to some embodiments of the present disclosure.

[0106] FIG. 142 is a schematic illustrating an example of a sensor according to some embodiments of the present disclosure.

[0107] FIG. 143 is a schematic illustrating an example schema of a reporting packet according to some embodiments of the present disclosure.

[0108] FIG. 144 is a schematic illustrating an example of an edge device of a sensor kit according to some embodiments of the present disclosure.

[0109] FIG. 145 is a schematic illustrating an example of a backend system that receives sensor data from sensor kits deployed in industrial settings according to some embodiments of the present disclosure.

[0110] FIG. 146 is a flow chart illustrating an example set of operations of a method for encoding sensor data captured by a sensor kit according to some embodiments of the present disclosure.

[0111] FIG. 147 is a flow chart illustrating an example set of operations of a method for decoding sensor data provided to a backend system by a sensor kit according to some embodiments of the present disclosure.

[0112] FIG. 148 is a flow chart illustrating an example set of operations of a method for encoding sensor data captured by a sensor kit using a media codec according to some embodiments of the present disclosure.

[0113] FIG. 149 is a flow chart illustrating an example set of operations of a method for decoding sensor data provided to a backend system by a sensor kit using a media codec according to some embodiments of the present disclosure.

[0114] FIG. 150 is a flow chart illustrating an example set of operations of a method for determining a transmission strategy and / or a storage strategy for sensor data collected by a sensor kit based on the sensor data, according to some embodiments of the present disclosure.

[0115] Figures 151-155 are schematics illustrating different configurations of sensor kits according to some embodiments of the present disclosure.

[0116] FIG. 156 is a flowchart illustrating an example set of operations of a method for monitoring industrial settings using an automatically configured backend system, according to some embodiments of the present disclosure.

[0117] FIG. 157 is a plan view of a manufacturing facility illustrating an exemplary implementation of a sensor kit including an edge device, according to some embodiments of the present disclosure.9

[0118] FIG. 158 is a plan view of a surface portion of an underwater industrial facility illustrating an exemplary implementation of a sensor kit including an edge device, according to some embodiments of the present disclosure.

[0119] FIG. 159 is a plan view of an indoor agricultural facility illustrating an exemplary implem entation of a sensor kit including an edge device, according to some em bodiments of the present disclosure.

[0120] FIG. 160 is a schematic illustrating an example of a sensor kit in communication with a data handling platform according to some embodiments of the present disclosure.

[0121] FIGS. 161-164 are diagrammatic views that depict embodiments of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure.

[0122] FIGS. 165, 166, and 167 are diagrammatic views that depict embodiments of a system for using one or more mobile robots and / or mobile vehicles for mobile data collection in accordance with the present disclosure.

[0123] FIGS. 168-171 are diagrammatic views that depict embodiments of a system for using one or more handheld devices for mobile data collection in accordance with the present disclosure.

[0124] FIGS. 172, 173, and 174 are diagrammatic views that depict embodiments of a computer vision system in accordance with the present disclosure.

[0125] FIGS. 175 and 176 are diagrammatic views that depict embodiments of a deep learning system for training a computer vision system in accordance with the present disclosure.

[0126] FIG. 177 depicts a predictive maintenance eco system network architecture.

[0127] FIG. 178 depicts finding sendee workers using machine learning for the predictive maintenance eco-system of FIG. 177.

[0128] FIG. 179 depicts ordering parts and service in a predictive maintenance eco-system.

[0129] FIG. 180 depicts deployment of smart RFID elements in an industrial machine environment.

[0130] FIG. 181 depicts a generalized data structure for machine information in a smart RFID.

[0131] FIG. 182 depicts a block level diagram of the storage structure of a smart RFID.

[0132] FIG. 183 depicts an example of data stored in a smart RFID.

[0133] FIG. 184 depicts a flow7diagram of a method for collecting information from a machine.

[0134] FIG. 185 depicts a flow diagram of a method for collecting data from a production environment.

[0135] FIG. 186 depicts an on-line maintenance management system with interfaces for data sources updating information in the on-line maintenance management system data storage.

[0136] FIG. 187 depicts a distributed ledger for predictive maintenance information with rolespecific access thereof.

[0137] FIG. 188 depicts a process for capturing images of portions of an industrial machine.

[0138] FIG. 189 depicts a process that uses machine learning on images to recognize a likely- internal structure of an industrial machine.

[0139] FIG. 190 depicts a knowledge graph of the predictive m aintenance gathering information.10

[0140] FIG. 191 depicts an artificial intelligence system generating service recommendations and the like based on predictive maintenance analysis.

[0141] FIG. 192 depicts a predictive maintenance timeline superimposed on a preventive maintenance timeline.

[0142] FIG. 193 depicts a block diagram of potential sources of diagnostic information.

[0143] FIG. 194 depicts a diagram of a process for rating vendors.

[0144] FIG. 195 depicts a diagram of a process for rating procedures.

[0145] FIG. 196 depicts a diagram of Blockchain applied to transactions of a predictive maintenance eco-system.

[0146] FIG. 197 depicts a transfer function that facilitates converting vibration data into severity units.

[0147] FIG. 198 depicts a table that facilitates mapping vibration data to severity' units.

[0148] FIG. 199 depicts a composite frequency graph for conventional vibration assessment and severity unit-based assessment.

[0149] FIG. 200 depicts a rendering of a portion of an industrial machine for use in an electronic user interface for depicting and discovering severity units and related information about a rotating component of the industrial machine.

[0150] FIG. 201 depicts a data table of rotating component design parameters for use in predicting maintenance events.

[0151] FIG. 202 is a flow chart of predicting maintenance of at least one of a gear, motor and roller bearing based on severity unit and actuator count, such as count of teeth in a gear.

[0152] FIG. 203 is a schematic diagram of an example platform for facilitating development of intelligence in an Industrial Internet of Things (HoT) system according to some aspects of the present disclosure.

[0153] FIG. 204 is a schematic diagram showing additional details, components, sub-systems, and other elements of an optional implementation of the example platform of FIG. 203;

[0154] FIG. 205 is a schematic diagram showing a robotic process automation (“RPA”) system of the example platform of FIG. 203;

[0155] FIG. 206 is a schematic diagram showing an opportunity mining system and an adaptive intelligence layer of the example platform of FIG. 203;

[0156] FIG. 207 is a schematic diagram showing optional elements of the adaptive intelligent systems layer that facilitate improved edge intelligence of the example platform of FIG. 203;

[0157] FIG. 208 is a schematic diagram showing optional elements of an industrial entity-oriented data storage systems layer of the example platform of FIG. 203;

[0158] FIG. 209 is a schematic diagram showing an example Robotic Process Automation system of the example platform of FIG. 203;

[0159] FIG. 210 is a schematic diagram of an example system for data processing in an industrial environment that utilizes protocol adaptors according to some aspects of the present disclosure;

[0160] FIG. 211 is another schematic diagram illustrating further components and elements of the example system of FIG. 210; and11

[0161] FIG. 212 illustrates an example connect attempt of the example system of FIG. 210 according to some aspects of the present disclosure.

[0162] FIG. 213 is a schematic illustrating examples of architecture of a digital twin system according to embodiments of the present disclosure.

[0163] FIG. 214 is a schematic illustrating exemplary components of a digital twin management system according to embodiments of the present disclosure.

[0164] FIG. 215 is a schematic illustrating examples of a digital twin I / O system that interfaces with an environment, the digital twin system, and / or components thereof to provide bi-directional transfer of data between coupled components according to embodiments of the present disclosure.

[0165] FIG. 216 is a schematic illustrating examples of sets of identified states related to industrial environments that the digital twin system may identify and / or store for access by intelligent systems (e.g., a cognitive intelligence system) or users of the digital twin system according to embodiments of the present disclosure.

[0166] FIG. 217 is a schematic illustrating example embodiments of methods for updating a set of properties of a digital twin of the present disclosure on behalf of a client application and / or one or more embedded digital twins according to embodiments of the present disclosure.

[0167] FIG. 218 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to the dryer centrifuge according to embodiments of the present disclosure.

[0168] FIG. 219 is a schematic illustrating exampl e embodiments of methods for updating a set of vibration fault level states of machine components such as bearings in the digital twin of an industrial machine, on behalf of a client application according to embodiments of the present disclosure.

[0169] FIG. 220 is a schematic illustrating example embodiments of me thods for updating a set of vibration severity unit values of machine components such as bearings in the digital twin of a m achine on behalf of a client application according to embodiments of the present disclosure.

[0170] FIG. 221 is a schematic illustrating example embodiments of a method for updating a set of probability of failure values in the digital twins of machine components on behalf of a client application according to embodiments of the present disclosure.

[0171] FIG. 222 is a schematic illustrating example embodiments of methods for updating a set of probability of downtime values of machines in the digital twin of a manufacturing facility on behalf of a client application according to embodiments of the present disclosure.

[0172] FIG. 223 is a schematic illustrating example embodiments of methods for updating a set of probability of shutdown values of manufacturing facilities in the digital twin of an enterprise on behalf of a client application according to embodiments of the present disclosure.

[0173] FIG. 224 is a schematic illustrating exampl e embodiments of methods for updating a set of cost of downtime values of machines in the digital twin of a manufacturing facility according to embodiments of the present disclosure.12

[0174] FIG. 225 is a schematic illustrating example embodiments of methods for updating one or more manufacturing KPI values in a digital twin of a manufacturing facility, on behalf of a client application according to embodiments of the present disclosure.

[0175] FIG. 226 is a view of a display illustrating further example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to its drive components according to embodiments of the present disclosure.

[0176] FIG. 227 is a view of a display illustrating further example embodiments of a display interface of the present disclosure that provides a digital twin showing components of vibration according to embodiments of the present disclosure.

[0177] FIG. 228 is a view of a display illustrating further example embodiments of a display interface of the present disclosure that provides selections of digital twins showing various components experiencing faults according to embodiments of the present disclosure.

[0178] FIG. 229 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin whose view incorporates connected machines each having drive bearings according to embodiments of the present disclosure.

[0179] FIG. 230 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin whose view incorporates connected machines each having drive bearings showing motion outside of nominal according to embodiments of the present disclosure.

[0180] FIG. 231 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin showing drive bearings corrected to nominal motion according to embodiments of the present disclosure.

[0181] FIG. 232 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin whose view incorporates connected machines such as a motor and mill each having drive bearings showing motion outside of nominal according to embodiments of the present disclosure.

[0182] FIG. 233 is a view of a display illustrating example embodiments of a display interface of the present disclosure that renders a digital twin showing drive bearings corrected to nominal motion according to embodiments of the present disclosure.

[0183] FIG. 234 is a schematic illustrating an example of a portion of an information technology system for manufacturing artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[0184] FIG. 235 is a schematic illustrating an example environment of the enterprise and industrial control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0185] FIG. 236 is a schematic illustrating an example implementation of the enterprise and industrial control tower and management platform according to some embodiments of the present disclosure.

[0186] FIG. 237 is a schematic illustrating an example set of components of the enterprise control tower and management platform according to some embodiments of the present disclosure.13

[0187] FIG. 238 is a schematic illustrating an example of an enterprise data model according to some embodiments of the disclosure.

[0188] FIG. 239 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of the enterprise digital twin firamew-ork according to some embodiments of the present disclosure.

[0189] FIG. 240 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin.

[0190] FIG. 241 is a schematic illustrating example embodiments of systems for fault diagnosis in an industrial environment having components according to embodiments of the disclosure.

[0191] FIG. 242 is a schematic illustrating example embodiments of methods for fault diagnosis in an industrial environment having components according to embodiments of the disclosure.

[0192] FIGS. 243-248 are views depicting implementations of the systems and the methods of the disclosure for fault diagnosis in an industrial environment having components according to example embodiments of the disclosure.

[0193] FIGS. 249-252 are schematics illustrating example embodiments of architectures for implementation of the systems and the methods of the disclosure for fault diagnosis in an industrial environment having components according to embodiments of the disclosure.

[0194] FIG. 253 is a schematic illustrating an example of a portion of an information technology system for manufacturing artificial intelligence leveraging digital twins according to some embodiments of the disclosure.

[0195] FIG. 254 is a schematic view of an exemplar)- embodiment of the quantum computing service according to some embodiments of the present disclosure.

[0196] FIG. 255 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[0197] FIG. 256 is a diagrammatic view that illustrates embodiments of the biology-based industrial internet of thi ngs system in accord ance with the present disclosure.

[0198] FIG. 257 is a diagrammatic view of the thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0199] FIG. 258 is a diagrammatic view of a dual process artificial neural network system in accordance with the present disclosure.

[0200] FIG. 259 is a diagrammatic view illustrating an example implementation of a conventional computer vision system for creating an image of an object of interest.

[0201] FIG. 260 is a schematic illustrating an example implementation of a dynamic vision system for dynamically learning an object concept about an object, of interest according to some embodiments of the present disclosure.

[0202] FIG. 261 is a schematic illustrating an example architecture of a dynamic vision system according to some embodiments of the present disclosure.

[0203] FIG. 262 is a flow diagram illustrating a method for object recogni tion by a dynamic vision system according to some embodiments of the present disclosure.14

[0204] FIG. 263 is a schematic illustrating an example implementation of a dynamic vision system for modelling, simulating and optimizing various optical, mechanical, design and lighting parameters of the dynamic vision system according to some embodiments of the present disclosure.

[0205] FIG. 264 is a schematic illustrating an example artificial neural network used to provide real-time, adaptive control of a dynamic vision system according to some embodiments of the present disclosure.

[0206] FIG. 265 is a diagrammatic view illustrating an example implementation of a dynamic vision system using a convolutional neural network (CNN) to provide classification of an object of interest according to some embodiments of the present disclosure.

[0207] FIG. 266 is a diagrammatic view illustrating an example implementation of a dynamic vision system using a transformer network to provide classification of an object of interest according to some embodiments of the present disclosure.

[0208] FIG. 267 is a schematic view illustrating an example implementation of a dynamic vision system depicting detailed view of various components along with integration of the dynamic vision system with one or more third party systems according to some embodiments of the present disclosure.

[0209] FIG. 268 is a block diagram depicting exempl ary features, capabilities, and interfaces of a robust generative artificial intelligence platform.

[0210] FIG. 269 is a diagram illustrating a determination of attention by a machine learning model.

[0211] FIG. 270 is a diagram of a transformer model.

[0212] FIG. 271 is a schematic view of an example Al convergence system of systems.

[0213] FIG. 272 is a schematic view of an example offering layer.

[0214] FIG. 273 is a schematic view of an example transactions layer.

[0215] FIG. 274 is a schematic view of an example operations layer.

[0216] FIG. 275 is a schematic view of an example network layer.

[0217] FIG. 276 is a schematic view of an example data layer.

[0218] FIG. 277 is a schematic view of an example data layer.

[0219] FIG. 278 is a schematic view of an example intelligent data layer architecture.

[0220] FIG. 279 is a schematic view of an example network layer.

[0221] FIG. 280 is a schematic view of an example Al subsystem integrator system.

[0222] FIG. 281 is a schematic view of an example multiplatform attention management.

[0223] FIG. 282 is a diagram of an industrial technology stack in accordance with some example embodiments.

[0224] FIG. 283 is a diagram of a set of examples of industrial modules of the industrial technology stack in accordance with some example embodiments.DETAILED DESCRIPTION

[0225] Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative15basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.ARTIFICIAL INTELLIGENCE SYSTEMS AND TECHNIQUES FOR INDUSTRIAL.. INTERNET OF THINGSComputer Vision and Neural Network Systems for Industrial Internet of Things

[0226] In example embodiments, the disclosure provides a computer-implemented method for fault diagnosis in an industrial environment having a plurality of components. The computer- implemented method includes providing a plurality of sensors to the industrial environment, each of the plurality of sensors may be operatively coupled to at least one of the plurality of components and configured to generate a plurality of sensor data values in response to one or more sensed parameters. The plurality of sensor data values may be processed to determine a recognized pattern therefrom. At least one industrial-environment digital twin corresponding to the industrial environment may be retrieved. The at least one industrial-environment digital twin may include a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment. The at least one industrial-environment digital twin and the plurality of component digital twins may be visual digital twins that may be configured to be rendered in a visual maimer. The at least one industrialenvironment digital twin and at least one respective component digital twin of the plurality of component digital twins may be updated based on the plurality of sensor data values, at least in part, in response to the determination of the recognized pattern for th e corresponding component. A request may be received from a client application to check an operational condition of a particular component from the plurality of components in the industrial environment. The at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application may be rendered in response to the received request and based on the operational condition of the particular component.

[0227] In example embodiments, the computer-implemented method may further include determining if the recognized pattern relates to at least one system characteris tic including at least one of: a fault operation for a given component of the plurality of components, an off-nominal operation for the given component of the plurality of components, or an exceedance value for the given component of the plurality of components. Such the at least one system characteristic maybe generally indicative of some fault in the corresponding component in the industrial environment and thus may be useful for the purposes of the disclosure.

[0228] In example embodiments, the computer-implemented method may further include generating a notification in tire client application in response to a determination that the recognized pattern relates to the at least one system characteristic for the given component. In example embodiments, the computer-implemented method may further comprise configuring the client application to allow7selection of the notification. The rendering the at least one industrialenvironment digital twin and the at least one respective component digital twin corresponding to the given component may be in response to the selection of the notification. Such client application may be installed on a client device and allows the client to conveniently access information related to any fault determination in the industrial environment.16

[0229] In example embodiments, the rendering may further comprise executing a simulation for tire at least one industrial-environment digital twin and the at least one respective component digital twin based on the recognized pattern. In example embodiments, the simulation may simulate an effect of the recognized pattern on an operation of the corresponding component. In example embodiments, the rendering may further comprise executing another second simulation for the at least one industrial -environment digital twin and the at least one respective component digital twin based on a normal operation of the corresponding component. With such a rendering, the client may be provided with sufficient visual information to diagnose the fault in the industrial environment.

[0230] In example embodiments, the rendering the at least one industrial-environment digital twin and the at least one respective component digital twin to the client application may be via a display device of a user device. In example embodiments, the rendering the at least one industrialenvironment digital twin and the at least one respective component digital twin to the client application may be via an augmented reality-enabled device. In example embodiments, the rendering the at least one industrial-environment digital twin and the at least one respective component digital twin to the client application may be via a virtual reality headset.

[0231] In example embodiments, the plurality’ of sensors may comprise at least one vibration measurement sensor coupled to a motor of the corresponding component. The one or more sensed parameters may comprise vibration parameters related to a wobble in the motor of the corresponding component. In example embodiments, the recognized pattern may comprise at least one of: a broken bearing in the motor, broken or cracked rotor bars in the motor, a misalignment in the motor, an imbalance in the motor, or a material build-up in the motor. In example embodiments, the one or more sensed parameters may include at least one of: a set of temperature parameters, pressure parameters, humidity parameters, wind parameters, rainfall parameters, tide parameters, storm surge parameters, cloud cover parameters, snowfall parameters, visibility parameters, radiation parameters, audio parameters, video parameters, image parameters, water level parameters, quantum parameters, flow rate parameters, signal power parameters, signal frequency parameters, motion parameters, velocity’ parameters, acceleration parameters, lighting level parameters, analyte concentration parameters, biological compound concentration parameters, metal concentration parameters, or organic compound concentration parameters.

[0232] In example embodiments, the plurality of component digital twins may be generated based on properties of the corresponding component imported from at least one of: respective manufacturers of the components, onboard libraries, crowdsourced material, or subscription marketplaces.

[0233] In example embodiments, the computer-implemented method may further comprise providing an executive digital twin configured to provide forecasted financial information for the given component based, at least in part, on the at least one system characteristic determined to be related to the recognized pattern. In example embodiments, the computer-implemented method may further comprise providing an operator digital twin configured to provide workflow17information for performing maintenance for the given component based, at least in part, on the at least one system characteristic determined to be related to the recognized pattern.

[0234] In example embodiments, the rendering the at least one industrial-environment digital twin may include rendering the at least one industrial-environment digital twin as a digital representation of a real world element. In example embodiments, the rendering the at least one industrial-environment digital twin may include at least one of mimicking, copying, or modeling behaviors of the real world element in response to at least one of inputs, outputs, or conditions of an environment. In example embodiments, the rendering the at least one respective component digital twin corresponding to the particular component may include rendering the at least one respective component digital twins as a set of discrete component digital twins embedded within the at least one industrial-environment digital twin. In example embodiments, the rendering the set of discrete component digital twins may include rendering the set of discrete component digital twins based on imported properties of the particular component and on historical behavior of the particular component for implementation in the industrial environment.

[0235] In example embodiments, the method may further include providing an operator digital twin configured to generate visual cues indicating potential problems with an identified component of the plurality of components. In example embodiments, the providing the operator digital twin may further include generating a selector for selection by a user to direct maintenance on the identified component and the method may further include directing the maintenance on the identified component in response to selection of the selector.

[0236] In example embodiments, the method may further include generating at least one of a picture or a video of a component in response to an instruction from a user and further including detecting wobble induced by bad poles based on the at least one of the picture or the video. In example embodiments, the rendering the at least one industrial-environment digital twin and the at least one respective component digital twin may be in response to selection of a received request.

[0237] In example embodiments, the rendering the at least one industrial-environment digital twin and the at. least one respective component digital twin may include rendering the at least one industrial-environment digital twin and the at least one respective component digital twin in a visual manner. The method may further include drilling down on a particular element to view additional information regarding the particular element in response to a selection by a user on a display corresponding to the at least one industrial-environment digital twin and the at least one respective component digital twin as rendered in the visual manner.

[0238] In example embodiments, the disclosure provides a computing system for fault diagnosis in an industrial environment having a plurality of components. The computing system may comprise a plurality of sensors associated with the industrial environment, with each of the plurality of sensors operatively coupled to at least one of the plurality of components. The plurality of sensors may be configured to generate a plurality of sensor data values in response to one or more sensed parameters. At least one industrial-environment digital twin may correspond to the industrial environment. The at least one industrial-environment digital twin may include a plurality of component digital twins, with each of the plurality’ of component digi tal twins corresponding to18one of the plurality of components in the industrial environment. The at least one industrialenvironment digital twin and the plurality of component digital twins may be visual digital tw ins that may be configured to be rendered in a visual manner. One or more processors may be configured to: process the plurality of sensor data values to determine a recognized pattern therefrom; update the at least one industrial-environment digital twin and at least one respective component digital twin of the plurality' of component digital twins based on the plurality' of sensor data values, at least in part, in response to the determination of the recognized pattern for the corresponding component; receive a request from a client application to check an operational condition of a particular component from the plurality of components in the industrial environment; and render the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.

[0239] In example embodiments, the system may further comprise an executive digital twin configured to provide forecasted financial information for a given component based, at least in part, on at least one system characteristic determined to be related to the recognized pattern. In example embodiments, the system may further comprise an operator digital twin configured to provide workflow information for performing maintenance for a given component based, at least in part, on at least one system characteristic determined to be related to the recognized pattern.

[0240] In example embodiments, the one or more processors may be further configured to determine if the recognized pattern relates to at least one system characteristic including at least one of: a fault operation for a given component of the plurality of components, an off-nominal operation for the given component of the plurality of componen ts, or an exceedance value for the given component of the plurality of components. In example embodiments, the one or more processors may be further configured to generate a notification in the client application in response to the determination that the recognized pattern may relate to the at least one system characteristic for the given component. In example embodiments, the one or more processors may be further configured to configure the client application to allow selection of the notification, and where the rendering the at least one industrial-environment digital twin and the at least one respective component digital twin may correspond to the given component is in response to the selection of the notification. In example embodiments, the plurality' of sensors may be configured to generate tire plurality of sensor data values to include a stream of phase-based data for at least one of temperature, humidity, or load. In example embodiments, the plurality of sensors may be configured to generate at least one of a continuous stream of data over time, a nearly continuous stream of data over time, periodic readings, event-driven readings, or readings according to a selected schedule. In example embodiments, the plurality of sensor data values may include vibration parameters related to a wobble in a motor of the at least one of the plurality' of components, and where the one or more processors may be further configured to generate maintenance indications based on the vibration parameters related to the wobble. In example embodiments, the one or more processors may be further configured to at least one of: predict a19bearing life for the motor, identify a bearing health parameter, identify a bearing performance parameter, identify wear on a bearing, identify presence of foreign matter in bearings, identify air gaps in bearings, identify a loss of fluid in fluid coated bearings, identify stress and strain of flexure bearings, or identify behavior at a selected operation frequency for the plurality of componen ts.

[0241] In example embodiments, the disclosure may proride a non-transitory computer readable storage medium having a plurality of instructions stored thereon which, w7hen executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising: proriding a plurality of sensors to an industrial environment having a plurality of components, where each of the plurality of sensors operatively may be coupled to at least one of the plurality of components and configured to generate a plurality of sensor data values in response to one or more sensed parameters; processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, where the at least one industrial-environment digital twin may include a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and where the at least one industrial-environment digital twin and the plurality of component digital twins may be visual digital twins that may be configured to be rendered in a visual manner; updating the at least one industrial-environment digi tal twin and at least one respective component digital twin of the plurality of component digital twins based on the plurality of sensor data values, at least in part, in response to determination of the recognized pattern for the corresponding component; receiving a request from a client application to check an operational condition of a particular component from the plurality of components in the industrial environment; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.

[0242] In exampl e embodiments, a maintenance system for an indu strial environment may include a plurality of industrial machines, a predictive maintenance system, and a maintenance notification system. The plurality of industrial machines may collectively include a plurality of motors, the plurality of motors collectively including a predefined number of rotor bars. The predictive maintenance system may be programmed to generate a maintenance schedule for the plurality of industrial machines based on the predefined number of rotor bars and a rotor bar failure rate formula. The maintenance notification system may be programmed to generate maintenance alerts to indicate that maintenance should be performed on the plurality of industrial machines based on the maintenance schedule. In example embodiments, the rotor bar failure rate formula may be based on rotor bar weakening. In example embodiments, each of the plurality of motors may have a cycle rate and an age, and the predictive maintenance system may be further programmed to generate the maintenance schedule based on the cycle rate and the age of each of the plurality of motors.20

[0243] One aspect of the current disclosure relates to a method for transmitting a predictive model of a data stream from a first device to a second device. The method may include receiving, by a first device, a plurality’ of data values of a data stream. The data values may comprise sensor data collected from one or more sensor devices. The method may include generating, by the first device, a predictive model for predicting future data values of the data stream based on the received plurality of data values. Generating the predictive model may include detennining a plurality of model parameters. The method may include transmitting, by the first device, the plurality of model parameters to the second device. The method may include receiving, by the second device, the plurality of model parameters. The method may include parameterizing, by the second device, a predictive model using the plurality of model parameters. The method may include predicting, by the second device, the future data values of the data stream using the parameterized predictive model. In example embodiments, the parameters comprise a vector. In example embodiments, the vector is a motion vector associated with a robot. In example embodiments, the future data values of the data stream comprise one or more future predicted locations of the robot. In example embodiments, the predictive model predicts stock levels of items, the method further including detecting, based on the future data values, an upcoming supply shortage of an item. The method may further include taking action to avoid running out of the item. In example embodiments, the predictive model is a behavior analysis model. In example embodiments, the future data values indicate a predicted behavior of an entity. In example embodiments, the predictive model is an augmentation model, wherein the future data values correspond to an inoperative sensor. In example embodiments, the predictive model is a classification model. In example embodiments, the future data values indicate a predicted future state of a system comprising the one or more sensor devices. In example embodiments, the sensors are security cameras. In example embodiments, the data stream comprises motion vectors extracted from video data captured by the security cameras. In example embodiments, the sensors are vibration sensors measuring vibrations generated by machines. In example embodiments, the future data values indicate a potential need for maintenance of the machines. The method may further include receiving, by the first device, additional data values of the data stream . The method may include refining, by the first device, the predictive model using the additional data values. In example embodiments, refining the predictive model adjusts the model parameters. The method may include transmitting the adjusted model parameters to the second device. The method may further include receiving, by the second device, the adjusted model parameters. The method may include re-parameterizing the predictive model using the adjusted model parameters. The method may include generating additional future data values using the re-parameterized predictive model.

[0244] Another aspect, of the current disclosure relates to a method for prioritizing predictive model data streams. The method may include receiving, by a first device, a plurality of predicti ve model data streams. In example embodiments, each predictive model data streams comprises a set of model parameters for a corresponding predictive model. In example embodiments, each predictive model is trained to predict future data values of a data source. The method may include prioritizing, by the first device, priorities to each of the plurality of predictive model data streams.21The method may include selecting at least one of the predictive model data streams based on a corresponding priority. The method may include parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model stream. The method may include predicting, by the first device, future data values of the data source using the parameterized predictive model. In example embodiments, the selected at least one predictive model data stream is associated with a high priority. In example embodiments, the selecting comprises suppressing the predictive model data streams that were not selected based on the priorities associated with each non-selected predictive model data stream. In example embodiments, assigning priorities to each of the plurality of predictive model data streams comprises determining whether each set of model parameters is unusual. In example embodiments, assigning priorities to each of the plurality of predictive model data streams comprises determining whether each set of model parameters has changed from a previous value. In example embodiments, the set of model parameters comprise at least one vector. In example embodiments, the at least one vector comprises a motion vector associated with a robot. In example embodiments, the future data values comprise one or more future predicted locations of the robot. In example embodiments, the predictive model is a behavior analysis model. In example embodiments, future data values indicate a predicted behavior of an entity. In example embodiments, the predictive model is an augmentation model. In example embodiments, the future data values correspond to an inoperative sensor. In example embodiments, the predictive model is a classification model. In example embodiments, the future data values indicate a predicted future state of a system comprising the one or more sensor devices. In example embodiments, the sensors are security cameras. In example embodiments, the data stream comprises motion vectors extracted from video data captured by the security cameras. In example embodiments, the sensors are vibration sensors measuring vibrations generated by machines. In example embodiments, the future data values indicate a potential need for main tenance of the machines.

[0245] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate with existing data collection, processing, and storage systems while preserving access to existing format / frequency range / resolution compatible data. While the industrial machine sensor data streaming facilities described herein may collect a greater volume of data (e.g., longer duration of data collection) from sensors at a wider range of frequencies and with greater resolution than existing data collection systems, methods and systems may be employed to provide access to data from the stream of data that represents one or more ranges of frequency and / or one or more lines of resolution that are purposely compatible with existing systems. Further, a portion of the streamed data may be identified, extracted, stored, and / or forwarded to existing data processing systems to facilitate operation of exi sting data processing systems that substantively matches operation of existing data processing systems using existing collection-based data. In this way, a newly deployed system for sensing aspects of industrial machines, such as aspects of moving parts of industrial machines, may facilitate continued use of existing sensed data processing facilities, algorithms, models, pattern recognizers, user interfaces, and the like.22

[0246] Through identification of existing frequency ranges, formats, and / or resolution, such as byaccessing a data structure that defines these aspects of existing data, higher resolution streamed data may be configured to represent a specific frequency, frequency range, format, and / or resolution. Uris configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data. One approach to adapting streamed data for compatibility with existing sensed data may include aligning the streamed data with existing data so that portions of the streamed data that align with the existing data can be extracted, stored, and made available for processing with existing data processing methods. Alternatively, data processing methods may be configured to process portions of the streamed data that correspond, such as through alignment, to the existing data, with methods that implement functions substantially similar to the methods used to process existing data, such as methods that process data that contain a particular frequency range or a particular resolution and the like.

[0247] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range. This method can thusly be at least partially identifiable by these characteristics of the data being processed. Therefore, given a set of conditions, such as moving device being sensed, industrial machine type, frequency of data being sensed, and the like, a data processing system may select an appropriate method. Also, given such a set of conditions, an industrial machine data sensing and processing facility may configure elements, such as data filters, routers, processors, and the like, to handle data meeting the conditions.

[0248] Figures 1 through 5 depict portions of an overall view of an industrial Internet of Tilings (loT) data collection, monitoring and control system 10. Figure 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. Tliis allows the industrial environment to use the benefits of networking and control technologies, while also providing security, such as preventing cyber-attacks. The MANET 20 may use cognitive radio technologies 40, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In embodiments, the system depicted in Figures 1 through 5 provides network-sensitive or network-aware transport of data over the network to and from a data collection device or a heavy industrial machine.

[0249] Figures 3-4 depict intelligent data collection technologies deployed locally, at the edge of an loT deployment, where heavy industrial machines are located. This includes various sensors 52, loT devices 54, data storage capabilities (e.g., data pools 60, or distributed ledger 62) (including intelligent, self-organizing storage), sensor fusion (including self-organizing sensor fusion), and the like. Interfaces for data collection, including multi -sensory interfaces, tablets, smartphones 58,23and the like are shown. Figure 3 also shows data pools 60 that may collect data published bymachines or sensors that detect conditions of machines, such as for later consumption by local or remote intelligence. A distributed ledger system 62 may distribute storage across the local storage of various elements of the environment, or more broadly throughout the system. Figure 4 also shows on-device sensor fusion 80, such as for storing on a device data from multiple analog sensors 82, which may be analyzed locally or in the cloud, such as by machine learning 84, including by training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success) when operating the methods and systems disclosed herein.

[0250] Figure 1 depicts a server based portion of an industrial loT system that may be deployed in the cloud or on an enterprise owner’s or operator’s premises. The server portion includes network coding (including self-organizing network coding and / or automated configuration) that may configure a network coding model based on feedback measures, network conditions, or the like, for highly efficient transport of large amounts of data across the network to and from data collection systems and the cloud. Network coding may provide a wide range of capabilities for intelligence, analytics, remote control, remote operation, remote optimization, various storage configurations and the like, as depicted in Figure 1. The various storage configurations may include distributed ledger storage for supporting transactional data or other elements of the system.

[0251] Figure 5 depicts a programmatic data marketplace 70, which may be a self-organizing marketplace, such as for making available data that is collected in industrial environments, such as from data collectors, data pools, distributed ledgers, and other elements disclosed herein. Additional detail on the various components and sub-components of Figures 1 through 5 is provided throughout this disclosure.

[0252] With reference to Figure 6, an embodiment of platform 100 may include a local data collection system 102, which may be disposed in an environment 104, such as an industrial environment similar to that shown in Figure 3, for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements. The platform 100 may connect to or include portions of the industrial loT data collection, monitoring and control system 10 depicted in Figures 1-5. The platform 100 may include a network data transport system 108, such as fortransporting data to and from the local data collection system 102 over a network 110, such as to a host processing system 112, such as one that is disposed in a cloud computing environment or on the premises of an enterprise, or that consists of distributed components that interact with each other to process data collected by the local data collection system 102. The host processing system 1 12, referred to for convenience in some cases as the host system 112, may include various systems, components, methods, processes, facilities, and the like for enabling automated, or automation-assisted processing of the data, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements in a local environment 104 or in a network 110. The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated24action based on the applications of a set of rales or models upon input data from the local data collection system 102 or from one or more input sources 116, which may comprise information feeds and inputs from a wide array of sources, including those in the local environment 104, in a network 110, in the host system 112, or in one or more external systems, databases, or the like. The platform 100 may include one or more intelligent systems 118, which may be disposed in, integrated with, or acting as inputs to one or more components of the platform 100. Details of these and other components of the platform 100 are provided throughout this disclosure.

[0253] Intelligent systems 118 may include cognitive systems 120, such as enabling a degree of cognitive behavior as a result of the coordination of processing elements, such as mesh, peer-to- peer, ring, serial, and other architectures, where one or more node elements is coordinated with other node elements to provide collective, coordinated behavior to assist in processing, communication, data collection, or the like. The MANET 20 depicted in Figure 2 may also use cognitive radio technologies, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In one example, the cognitive system technology stack can include examples disclosed in U.S. Patent Number 8,060,017 to Schlicht et al., issued 15 November 2011 and hereby incorporated by reference as if fully set forth herein.

[0254] Intelligent systems may include machine learning systems 122, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like that may, in turn, be used for processing by the host system 112 as inputs to components of the platform 100 and portions of the industrial loT data collection, monitoring and control system 10, or the like. Learning may be human-supervised or fully-automated, such as using one or more input sources 116 to provide a data set, along with information about the item to be learned. Machine learning may use one or more models, rales, semantic understandings, 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 feed forward to an operating model for the system or process. One such machine learning technique for semantic and contextual understandings, w7orkflow7s, or other structured or semi-structured understandings is disclosed in U.S. Patent Number 8,200,775 to Moore, issued 12 June 2012, and hereby incorporated by reference as if fully set forth herein. Machine learning may be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, or the like (such as changing a function within a model) based on feedback (such as regarding the success of a model in a given situation) or based on iteration (such as in a recursive process). Where sufficient understanding of the underlying structure or behavior of a system is not known, insufficient data is not available, or in other cases where preferred for various reasons, machine learning may also be undertaken in the absence of an underlying model; that is, input sources may be weighted, structured, or the like within a machine learning facility without regard to any a priori understanding of structure, and outcomes (such as those based on measures of success at accomplishing various desired objectives) can be serially fed to the machine learning system to allow it to learn how7to achieve the targeted objectives. For example, the system may learn to25recognize faults, to recognize patterns, to develop models or functions, to develop rules, to optimize performance, to minimize failure rates, to optimize profits, to optimize resource utilization, to optimize flow (such as flow of traffic), or to optimize many other parameters that may be relevant to successful outcomes (such as outcomes in a wide range of environments). Machine learning may use genetic programming techniques, such as promoting or demoting one or more input sources, structures, data types, objects, weights, nodes, links, or other factors based on feedback (such that successful elements emerge over a series of generations). For example, alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditi ons (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like. In example embodiments, local machine learning may turn on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters, identification of a pattern (such as relating to a threat, a failure mode, a success mode, or the like) or the like. For example, a system may learn what sets of sensors should be turned on or off under given conditions to achieve the highest value utilization of a data col lector 102. In example embodiments, similar techniques may be used to handle optimization of transport of data in the platform 100 (such as in the network 110) by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring network security elements), and the like.

[0255] In example embodiments, the local data collection system 102 may include a high- performance, multi-sensor data collector having a number of novel features for collection and processing of analog and other sensor data. In example embodiments, a local data collection system 102 may be deployed to the industrial facilities depicted in Figure 3. A. local data collection system 102 may also be deployed to monitor other machines such as the machine 2200. The data collection system 102 may have on-board intelligent systems 118 (such as for learning to optimize the configuration and operation of the data collector, such as configuring permutations and combinations of sensors based on contexts and conditions). In one example, the data collection system 102 includes a crosspoint switch 130 or other analog switches. Automated, intelligent configuration of the local data collection system 102 may be based on a variety of types of information, such as information from various input sources, including those based on available power, power requirements of sensors, the value of the data collected (such as based on feedback information from other elements of the platform 100), the relative value of information (such as values based on the availability of other sources of the same or similar information), power availability (such as for powering sensors), network conditions, ambient conditions, operating states, operating contexts, operating events, and many others.26

[0256] Figure 7 shows elements and sub-components of a data collection and analysis system 1100 for sensor data (such as analog sensor data) collected in industrial environments. As depicted in Figure 7, embodiments of the methods and systems disclosed herein may include hardware that has several different modules starting with tire multiplexer (“MUX”) main board 1104. In example embodiments, there may be a MUX option board 1108. The MUX 114 main board is where the sensors connect to the system. These connections are on top to enable ease of installation. Then there are numerous settings on the tmderside of this board as well as on the Mux option board 1108, which attaches to the MUX main board 1104 via two headers one at either end of the board. In example embodiments, the Mux option board lias the male headers, which mesh together with the female header on the main Mux board. This enables them to be stacked on top of each other thus taking up less real estate.

[0257] In example embodiments, the main Mux board and / or the MUX option board then connects to the mother (e.g., with 4 simultaneous channels) and daughter (e.g., with 4 additional channels for 8 total channels) analog boards 1110 via cables where some of the signal conditioning (such as hardware integration) occurs. The signals then move from the analog boards 1110 to an antialiasing board (not shown) where some of the potential aliasing is removed. The rest of the aliasing removal is done on the delta sigma board 1112. The delta sigma board 1112 provides more aliasing protection along with oilier conditioning and digitizing of the signal. Next, the data moves to the Jennie™ board 1114 for more digitizing as well as communication to a computer via USB or Ethernet. In example embodiments, the Jennie™ board 1114 may be replaced with a pic board 1118 for more advanced and efficient data collection as well as communication. Once the data moves to the computer software 1102, the computer software 1102 can manipulate the data to show trending, spectra, waveform, statistics, and analytics.

[0258] In example embodiments, the system is meant to take in all types of data from volts to 4- 20 mA signals. In example embodiments, open formats of data storage and communication may be used. In some instances, certain portions of the system may be proprietary especially some of research and data associated with the analytics and reporting. In example embodiments, smart band analysis is a way to break data down into easily analyzed parts that can be combined with other smart bands to make new more simplified yet sophisticated analytics, hi example embodiments, this unique information is taken and graphics are used to depict the conditions because picture depictions are more helpful to the user. In example embodiments, complicated programs and user interlaces are simplified so that any user can manipulate the data like an expert.

[0259] In example embodiments, the system in essence, works in a big loop. The system starts in software with a general user interface (“GUI”) 1124. In example embodiments, rapid route creation may take advantage of hierarchical templates. In example embodiments, a GUI is created so any general user can populate the information itself with simple templates. Once the templates are created the user can copy and paste whatever the user needs, hi addition, users can develop their own templates for future ease of use and to institutionalize the knowledge. When the user has entered all of the user’s information and connected all of the user’s sensors, the user can then start the system acquiring data.27

[0260] Embodiments of the methods and systems disclosed herein may include unique electrostatic protection for trigger and vibration inputs. In many critical industrial environments where large electrostatic forces, which can harm electrical equipment, may build up, for example rotating machinery' or low-speed balancing using large belts, proper transducer and trigger input protection is required. In example embodiments, a low-cost but efficient method is described for such protection without the need for external supplemental devices.

[0261] Typically, vibration data collectors are not designed to handle large input voltages due to the expense and the fact that, more often than not, it is not needed. A need exists for these data collectors to acquire many varied types of RPM data as technology improves and monitoring costs plummet. In example embodiments, a method is using the already established OptoMOS™ technology' which permits the switching up front of high voltage signals rather than using more conventional reed-relay approaches. Many historic concerns regarding non-linear zero crossing or other non-linear solid-state behaviors have been eliminated with regard to the passing through of weakly buffered analog signals. In addition, in example embodiments, printed circuit board routing topologies place all of the individual channel input circuitry as close to the input connector as possible. In example embodiments, a unique electrostatic protection for trigger and vibration inputs may be placed upfront on the Mux and DAQ hardware in order to dissipate the built up electric charge as the signal passed from the sensor to the hardware. In example embodiments, the Mux and analog board may support high-amperage input using a design topology comprising wider traces and solid state relays for upfront, circuitry'.

[0262] In some systems multiplexers are afterthoughts and the quality of the signal coming from the multiplexer is not considered. As a result of a poor quality- multiplexer, the quality' of the signal can drop as much as 30 dB or more. Thus, substantial signal quality may be lost using a 24-bit DAQ that has a signal to noise ratio of 110 dB and if the signal to noise ratio drops to 80 dB in the Mux, it may not be much better than a 16-bit system from 20 years ago. In embodiments of this system, an important part at the front of the Mux is upfront signal conditioning on Mux for improved signal -to-noise ratio. Embodiments may perform signal conditioning (such as range / gain control, integration, filtering, etc.) on vibration as well as other signal inputs up front before Mux switching to achieve the highest signal -to-noise ratio.

[0263] In example embodiments, in addition to providing a better signal, the multiplexer may provide a continuous monitor alarming feature. Truly continuous systems monitor every sensor all the time but tend to be expensive. Typical multiplexer systems only monitor a set number of channels at one time and switch from bank to bank of a larger set of sensors. As a result, the sensors not being currently collected are not being m onitored; if a level increases the user m ay never know. In example embodiments, a multiplexer may have a continuous monitor alarming feature by placing circuitry on the multiplexer that can measure input channel levels against known alarm conditions even when the data acquisition (“DAQ”) is not monitoring the input. In example embodiments, continuous monitoring Mux by-pass offers a mechanism whereby channels not being currently sampled by the Mux system may be continuously monitored for significant alarm conditions via a number of trigger conditions using filtered peak-hold circuits or functionally28similar that are in turn passed on to the monitoring system in an expedient manner using hardware intermpts or other means. This, in essence, makes the system continuously monitor, although without the ability to instantly capture data on the problem like a true continuous system. In example embodiments, coupling this capability to alarm with adaptive scheduling techniques for continuous monitoring and the continuous monitoring system’s software adapting and adjusting the data collection sequence based on statistics, analytics, data alarms and dynamic analysis may allow the system to quickly collect dynamic spectral data on the alarming sensor very soon after the alarm sounds.|0264j Another restriction of typical multiplexers is that they may have a limited number of channels. In example embodiments, use of distributed complex programmable logic device (“CPLD”) chips with dedicated bus for logic control of multiple Mux an d data acquisition sections enables a CPLD to control multiple mux and DAQs so that there is no limit to the number of channels a system can handle. Interfacing to multiple types of predictive maintenance and vibration transducers requires a great deal of switching. This includes AC / DC coupling, 4-20 interfacing, integrated electronic piezoelectric transducer, channel power-down (for conserving op-amp power), single-ended or differential grounding options, and so on. Also required is the control of digital pots for range and gain control, switches for hardware integration, AA filtering and triggering. This logic can be performed by a series of CPLD chips strategically l ocated for the tasks they control. A single giant CPLD requires long circuit routes with a great deal of density at the single giant CPLD. In example embodiments, distributed CPLDs not only address these concerns but offer a great deal of flexibility. A bus is created where each CPLD that has a fixed assignment has its own unique device address. In example embodiments, multiplexers and DAQs can stack together offering additional input and output channels to the system. For multiple boards (e.g., for multiple Mux boards), jumpers are provided for setting multiple addresses. In another example, three bits permit up to 8 boards that are jumper configurable. In example embodiments, a bus protocol is defined such that each CPLD on the bus can either be addressed individually or as a group.

[0265] Typical multiplexers may be limited to collecting only sensors in the same bank. For detailed analysis, this may be limiting as there is tremendous value in being able to simultaneously review data from sensors on the same machine. Current systems using conventional fixed bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) that were assigned to a particular group at the time of installation. The only way to provide some flexibility is to either overlap channels or incorporate lots of redundancies in the system, both of which can add considerable expense (in some cases an exponential increase in cost versus flexibility). The simplest Mux design selects one of many inputs and routes it into a single output line. A banked design would consist of a group of these simple buil ding blocks, each handling a fixed group of inputs and routing to its respective output . Typically, the inputs are not overlapping so that the input of one Mux grouping cannot be routed into another. Unlike conventional Mux chips which typically swi tch a fixed group or banks of a fixed selection of channels into a single output (e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user to assign any input to any29output. Previously, crosspoint multiplexers were used for specialized purposes such as RGB digital video applications and were as a practical matter too noisy for analog applications such as vibration analysis; however more recent advances in the technology now make it feasible. Another advantage of the crosspoint Mux is the ability to disable outputs by putting them into a high impedance state. Ulis is ideal for an output bus so that multiple Mux cards may be stacked, and their output buses joined together without the need for bus switches.

[0266] In example embodiments, this may be addressed by use of an analog crosspoint switch for collecting variable groups of vibration input channels and providing a matrix circuit so the system may access any set of eight channels from the total number of input sensors.

[0267] In example embodiments, the ability to control multiple multiplexers with use of distributed CPLD chips with dedicated bus for logic control of multiple Mux and data acquisition sections is enhanced with a hierarchical multiplexer which allows for multiple DAQ to collect data from multiple multiplexers. A hierarchical Mux may allow modular output of more channels, such as 16, 24 or more to multiple of eight channel card sets. In example embodiments, this allows for faster data collection as well as more channels of simultaneous data collection for more complex analysis. In example embodiments, the Mux may be configured slightly to make it portable and use data acquisition parking features, which turns SV3X DAQ into a protected system embodiment.

[0268] In example embodiments, once the signals leave the multiplexer and hierarchical Mux they move to the analog board w'here there are other enhancements. In example embodiments, power saving techniques may be used such as: power-down of analog channels when not in use; powering down of component boards; power-down of analog signal processing op-amps for non-selected channels; powering down channels on the mother and the daughter analog boards. The ability to power down component boards and other hardware by the low-level firmware for the DAQ system makes high-level application control with respect to power-saving capabilities relatively easy. Explicit control of the hardware is always possible but not required by default. In example embodiments, this power saving benefit may be of value to a protected system, especially if it is battery operated or solar powered.

[0269] In example embodiments, in order to maximize the signal to noise ratio and provide the best data, a peak-detector for auto-scaling routed into a separate A / D will provide the system the highest peak in each set of data so it can rapidly scale the data to that peak. For vibration analysis purposes, the built-in A / D converters in many microprocessors may be inadequate with regards to number of bits, number of channels or sampling frequency versus not slowing the microprocessor down significantly. Despite these limitations, it is useful to use them for the purposes of autoscaling. In example embodiments, a separate A / D may be used that has reduced functionality and is cheaper. For each channel of input, after the signal is buffered (usually with the appropriate coupling: AC or DC) but before it is signal conditioned, the signal is fed directly into the microprocessor or low-cost A / D. Unlike the conditioned signal for which range, gain and filter switches are thrown, no switches are varied. This permits the simultaneous sampling of the autoscaling data while the input data is signal conditioned, fed into a more robust external A / D, and30directed into on-board memory using direct memory access (DMA) methods where memory is accessed without requiring a CPU. This significantly simplifies the auto-scaling process by not having to throw switches and then allow for settling time, which greatly slows down the autoscaling process. Furthermore, the data may be collected simultaneously, which assures the best signal -to -noise ratio. The reduced number of bits and other features is usually more than adequate for auto-scaling purposes. In example embodiments, improved integration using both analog and digital methods create an innovative hybrid integration which also improves or maintains the highest possible signal to noise ratio.

[0270] In example embodiments, a section of the analog board may allow routing of a trigger channel, either raw or buffered, into other analog channels. This may allow a user to route the trigger to any of the channels for analysis and trouble shooting . Systems may have trigger channels for the purposes of determining relative phase between various input data sets or for acquiring significant data without the needless repetition of unwanted input. In example embodiments, digitally controlled relays may be used to switch either the raw or buffered trigger signal into one of the input channels. It may be desirable to examine the quality of the triggering pulse because it may be corrupted for a variety of reasons including inadequate placement of the trigger sensor, wiring issues, faulty setup issues such as a dirty piece of reflective tape if using an optical sensor, and so on. The ability to look at either the raw or buffered signal may offer an excellent diagnostic or debugging vehicle. It also can offer some improved phase analysis capability by making use of the recorded data signal for various signal processing techniques such as variable speed filtering algorithms.

[0271] In example embodiments, once the signals leave the analog board, the signals move into tire delta-sigma board where precise voltage reference for A / D zero reference offers more accurate direct current sensor data. The delta sigma’s high speeds also provide for using higher input oversampling for delta-sigma A / D for lower sampling rate outputs to minimize antialiasing filter requirements. Lower oversampling rates can be used for higher sampling rates. For example, a 3rdorder AA filter set for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoff AA. filter can then be used for Fmax ranges from 1 kHz and higher (with a secondary filter kicking in at 2.56x the highest sampling rate of 128 kHz). In example embodiments, a CPLD may be used as a clock-divider for a delta-sigma A / D to achieve lower sampling rates without the need for digital resampling. In example embodiments, a high-frequency crystal reference can be divided down to lower frequencies by employing a CPLD as a programmable clock divider. The accuracy of the divided down lower frequencies is even more accurate than the original source relative to their longer time periods. This also minimizes or removes the need for resampling processing by the delta-sigma A / D.

[0272] In example embodiments, the data then moves from the delta-sigma board to the Jennie ™ board where phase relative to input and trigger channels using on-board timers may be digitally derived. In example embodiments, the Jennie™ board also has the ability to store calibration data and system maintenance repair history data in an on-board card set. In example embodiments, the31Jennie™ board will enable acquiring long blocks of data at high-sampling rate as opposed to multiple sets of data taken at different sampling rates so it can stream data and acquire long blocks of data for advanced analysis in the future.

[0273] In example embodiments, after the signal moves through the Jennie™ board it may then be transmitted to the computer. In example embodiments, the computer software will be used to add intelligence to the system starting with an expert, system GUI. The GUI will offer a graphical expert system with simplified user interface for defining smart bands and diagnoses which facilitate anyone to develop complex analytics. In example embodiments, this user interface may revolve around smart bands, which are a simplified approach to complex yet flexible analytics for the general user. In example embodiments, the smart bands may pair with a self-learning neural network for an even more advanced analytical approach. In example embodiments, this system may use the machine’s hierarchy for additional analytical insight. One critical part of predictive maintenance is the ability to learn from known information during repairs or inspections. In example embodiments, graphical approaches for back calculations may improve the smart bands and correlations based on a known fault or problem.

[0274] In example embodiments, there is a smart route which adapts which sensors it collects simultaneously in order to gain additional correlative intelligence. In example embodiments, smart operational data store (“ODS”) allows the system to elect to gather data to perform operational deflection shape analysis in order to further examine the machinery condition. In example embodiments, adaptive scheduling techniques allow7the system to change the scheduled data collected for full spectral analysis across a number (e.g., eight), of correlative channels. In example embodiments, the system may provide data to enable extended statistics capabilities for continuous monitoring as well as ambient local vibration for analysis that combines ambient temperature and local temperature and vibration levels changes for identifying machinery issues.

[0275] In example embodiments, a data acquisition device may be controlled by a personal computer (PC) to implemen t the desired data acquisition commands. In example embodiments, the DAQ box may be self-sufficient, and can acquire, process, analyze and monitor independent of external PC control. Embodiments may include secure digital (SD) card storage. In example embodiments, significant additional storage capability may be provided by utilizing an SD card. Tliis may prove critical for monitoring applications where critical data may be stored permanently. Also, if a power failure should occur, the most recent data may be stored despite the fact that it was not off-loaded to another system.

[0276] A current trend has been to make DAQ systems as communicative as possible with the outside world usually in the form of networks including wireless. In the past it was common to use a dedicated bus to control a DAQ system with either a microprocessor or microcontroller / microprocessor paired with a PC. In example embodiments, a DAQ system may comprise one or more microprocessor / microcontrollers, specialized microcontrollers / microprocessors, or dedicated processors focused primarily on the communication aspects with the outside world. These include USB, Ethernet and wireless with the ability to provide an IP address or addresses in order to host a webpage. All communications with32the outside world are then accomplished using a simple text based menu. The usual array of commands (in practice more than a hundred) such as InitializeCard, AcquireData, StopAcquisition, RetrieveCalibration Info, and so on, would be provided.

[0277] In example embodiments, intense signal processing activities including resampling, weighting, filtering, and spectrum processing may be performed by dedicated processors such as field-programmable gate array (“FPGAs”), digital signal processor (“DSP”), microprocessors, micro-controllers, or a combination thereof. In example embodiments, this subsystem may communicate via a specialized hardware bus with the commimication processing section. It will be facilitated with dual-port memory, semaphore logic, and so on. This embodiment will not only provide a marked improvement in efficiency but can significantly improve the processing capability, including the streaming of the data as well as other high-end analytical techniques. This negates the need for constantly interrupting the main processes which include the control of the signal conditioning circuits, triggering, raw data acquisition using the A / D, directing the A / D output to the appropriate on-board memory and processing that data.

[0278] Embodiments may include sensor overload identification. A need exists for monitoring systems to identify when the sensor is overloading. There may be situations involving high- frequency inputs that will saturate a standard 100 mv / g sensor (which is most commonly used in the industry) and having the ability to sense the overload improves data quality for better analysis. A monitoring system may identify when their system is overloading, but in example embodiments, the system may look at the voltage of the sensor to determine if the overload is from the sensor, enabling the user to get another sensor better suited to the situation, or gather the data again.

[0279] Embodiments may include radio frequency identification (“RFID”) and an inclinometer or accelerometer on a sensor so the sensor can indicate what machine / bearing it is attached to and what direction such that the software can automatically store the data without the user input. In example embodiments, users could put the system on any machine or machines and the system would automatically set itsel f up and be ready for data collection in seconds.

[0280] Embodiments may include ultrasonic online monitoring by placing ultrasonic sensors inside transformers, motor control centers, breakers and the like and monitoring, via a sound spectrum, continuously looking for patterns that identify arcing, corona and other electrical issues indicating a break down or issue. Embodiments may include providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. In example embodiments, an analysis engine may be used in ultrasonic online monitoring as well as identifying other faults by combining the ultrasonic data with other parameters such as vibration, temperature, pressure, heat flux, magnetic fields, electrical fields, currents, voltage, capacitance, inductance, and combinations (e.g., simple ratios) of the same, among many others.

[0281] Embodiments of the methods and systems disclosed herein may include use of an analog crosspoint switch for collecting variable groups of vibration input channels. For vibration analysis, it is useful to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or machines) in multiple directions. By obtaining the readings at the same time, for example, the relative phases of the inputs may be compared for the purpose of33diagnosing various mechanical faults. Other types of cross channel analyses such as crosscorrelation, transfer functions, Operating Deflection Shape (“ODS”) may also be performed.

[0282] Embodiments of the methods and systems disclosed herein may include precise voltage reference for A / D zero reference. Some A / D chips provide their own internal zero voltage reference to be used as a mid-scale value for external signal conditioning circuitry to ensure that both the A / D and external op-amps use the same reference. Although this sounds reasonable in principle, there are practical complications. In many cases these references are inherently based on a supply voltage using a resistor-divider. For many current systems, especially those whose power is derived from a PC via USB or similar bus, this provides for an unreliable reference, as the supply voltage will often vary quite significantly with load. This is especially true for delta-sigma A / D chips which necessitate increased signal processing. Although the offsets may drift together with load, a problem arises if one wants to calibrate the readings digitally. It is typical to modify the voltage offset expressed as counts coming from the A / D digitally to compensate for the DC drift. However, for this case, if the proper calibration offset is determined for one set of loading conditions, they will not apply for other conditions. An absolute DC offset expressed in counts will no longer be applicable. As a result, it becomes necessary to calibrate for all loading conditions which becomes complex, unreliable, and ultimately unmanageable. In example embodiments, an external voltage reference is used which is simply independent of the supply voltage to use as the zero offset.

[0283] In example embodiments, the system provides a phase-lock-loop band pass tracking filter method for obtaining slow-speed RPMs and phase for balancing purposes to remotely balance slow speed machinery, such as in paper mills, as well as offering additional analysis from its data. For balancing purposes, it is sometimes necessary- to balance at very- slow speeds. A typical tracking filter may be constructed based on a phase-lock loop or PEL design; however, stability and speed range are overriding concerns. In example embodiments, a number of digitally controlled switches are used for selecting the appropriate RC and damping constants. The switching can be done all automatically after measuring the frequency of the incoming tach signal. Embodiments of the methods and system s disclosed herein may include digital deri vation of phase relative to input and trigger channels using on-board timers. In example embodiments, digital phase derivation uses digital timers to ascertain an exact delay from a trigger event to the precise start of data acquisition. Tliis delay, or offset, then, is further refined using interpolation methods to obtain an even more precise offset which is then applied to the analytically determined phase of the acquired data such that the phase is “in essence” an absolute phase with precise mechanical meaning useful for among other things, one-shot balancing, alignment analysis, and so on.

[0284] Embodiments of the methods and systems disclosed herein may include signal processing firmware / hardware. In example embodiments, long blocks of data may be acquired at high- sampling rate as opposed to multiple sets of data taken at different sampling rates. Typically, in modem route collection for vibration analysis, it is customary to collect data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary from route point to point based on the specific mechanical analysis requirements at hand. For example, a motor may require a relatively low sampling rate with high resolution to distinguish running speed harmonics34from line frequency harmonics. The practical trade-off here though is that it takes more collection time to achieve this improved resolution. In contrast, some high-speed compressors or gear sets require much higher sampling rates to measure the amplitudes of relatively higher frequency data although the precise resolution may not be as necessary. Ideally, however, it would be better to collect a very long sample length of data at a very high-sampling rate. When digital acquisition devices were first popularized in the early 1980s, the A / D sampling, digital storage, and computational abilities were not close to what they are today, so compromises were made between the time required for data collection and the desired resolution and accuracy. It was because of this limitation that some analysts in the field even refused to give up their analog tape recording systems, which did not suffer as much from these same digitizing drawbacks. A few hybrid systems were empl oyed that would digitize the play back of the recorded analog data at multiple sampl ing rates and lengths desired, though these systems were admittedly less automated. The more common approach, as mentioned earlier, is to balance data collection time with analysis capability and digitally acquire the data blocks at multiple sampling rates and sampling lengths and digitally store these blocks separately. In example embodiments, a long data length of data can be collected at the highest practical sampling rate (e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This long block of data can be acquired in the same amount of time as the shorter length of the lower sampling rates utilized by a priori methods so that there is no effective delay added to the sampling at the measurement point, always a concern in route collection. In example embodiments, analog tape recording of data is digitally simulated with such a precision that it can be in effect considered continuous or “analog” for many purposes, including for purposes of embodiments of the present disclosure, except where context indicates otherwise.[02851 Embodiments of the methods and systems disclosed herein may include storage of calibration data and maintenance history on-board card sets. Many data acquisition devices which rely on interfacing to a PC to function store their calibration coefficients on the PC. This is especially true for complex data acquisition devices whose signal paths are many and therefore whose calibration tables can be quite large. In example embodiments, calibration coefficients are stored in flash memory which will remember this data or any other significant information forthat matter, for all practical purposes, permanently. This information may include nameplate information such as serial numbers of individual components, firmware or software version numbers, maintenance history, and the calibration tables. In example embodiments, no matter which computer the box is ultimately connected to, the DAQ box remains calibrated and continues to hold all of this critical information. The PC or external device may poll for this information at any time for implantation or information exchange purposes.

[0286] Embodiments of the methods and systems disclosed herein may include rapid route creation taking advantage of hierarchical templates. In the field of vibration monitoring, as well as parametric monitoring in general, it is necessary' to establish in a database or functional equivalent the existence of data monitoring points. These points are associated with a variety of attributes including the following categories: transducer attributes, data collection settings, machinery' parameters and operating parameters. The transducer attributes would include probe type, probe35mounting type and probe mounting direction or axis orientation. Data collection attributes associated with the measurement would involve a sampling rate, data length, integrated electronic piezoelectric probe power and coupling requirements, hardware integration requirements, 4-20 or voltage interfacing, range and gain settings (if applicable), filter requirements, and so on. Machinery parametric requirements relative to the specific point would include such items as operating speed, bearing type, bearing parametric data which for a rolling element bearing includes the pitch diameter, number of balls, inner race, and outer-race diameters. For a tilting pad bearing, this would include the nrnnber of pads and so on. For measurement points on a piece of equipment such as a gearbox, needed parameters would include, for example, the number of gear teeth on each of the gears. For induction motors, it would include the number of rotor bars and poles; for compressors, the number of blades and / or vanes; for fans, the number of blades. For belt / pulley systems, the number of belts as well as the relevant belt-passing frequencies may be calculated from the dimensions of the pulleys and pulley center-to-center distance. For measurements near couplings, the coupling type and nrnnber of teeth in a geared coupling may be necessary, and so on. Operating parametric data would include operating load, which may be expressed in megawatts, flow (either air or fluid), percentage, horsepower, feet-per-minute, and so on. Operating temperatures both ambient and operational, pressures, humidity’, and so on, may also be relevant. As can be seen, the setup information required for an individual measurement point can be quite large. It is also crucial to performing any legitimate analysis of the data. Machinery, equipment, and bearing specific information are essential for identifying fault frequencies as well as anticipating the various kinds of specific faults to be expected. The transducer attributes as well as data collection parameters are vital for properly interpreting the data along with providing limits for the type of analytical techniques suitable. The traditional means of entering this data has been manual and quite tedious, usually at the lowest hierarchical level (for example, at the bearing level with regards to machinery parameters), and at lire transducer level for data collection setup information. It cannot be stressed enough, however, the importance of the hierarchical relationships necessary’ to organize data — both for analytical and interpretive purposes as well as the storage and movement of data. Here, we are focusing primarily on the storage and movement of data. By its nature, the aforementioned setup information is extremely redundant at the level of the lowest hierarchies; however, because of its strong hierarchical nature, it can be stored quite efficiently in that form. In example embodiments, hierarchical nature can be utilized when copying data in the form of templates. As an example, hierarchical storage structure suitable for many purposes is defined from general to specific of company, plant or site, unit or process, machine, equipment, shaft element, bearing, and transducer. It is much easier to copy data associated with a particular machine, piece of equipment, shaft element or bearing than it is to copy only at the lowest, transducer level. In example embodiments, the system not only stores data in this hierarchical fashion, but robustly supports the rapid copying of data using these hierarchical templates. Similarity of elements at specific hierarchical levels lends itself to effective data storage in hierarchical format. For example, so many machines have common elements such as motors, gearboxes, compressors, belts, fans, and so on. More specifically, many motors can be easily36classified as induction, DC, fixed or variable speed. Many gearboxes can be grouped into commonly occurring groupings such as input / output, input pinion / intermediate pinion / output pinion, 4-posters, and so on. Within a plant or company, there are many similar types of equipment purchased and standardized on for both cost and maintenance reasons. This results in an enormous overlapping of similar types of equipment and, as a result, offers a great opportunity for taking advantage of a hierarchical template approach.

[0287] Embodiments of the methods and systems disclosed herein may include smart bands. Smart bands refer to any processed signal characteristics derived from any dynamic input or group of inputs for the purposes of analyzing the data and achieving the correct diagnoses. Furthermore, smart bands may even include mini or relatively simple diagnoses for the p urposes of achieving a more robust, and complex one. Historically, in the field of mechanical vibration analysis, Alarm Bands have been used to define spectral frequency bands of interest for the purposes of an alyzing and / or trending significant vibration patterns. The Alann Band typically consists of a spectral (amplitude plotted against frequency) region defined between a low and high frequency border. The amplitude between these borders is summed in the same manner for which an overall amplitude is calculated. A Smart Band is more flexible in that it not only refers to a specific frequency band but can also refer to a group of spectral peaks such as the harmonics of a single peak, a true-peak level or crest factor derived from a time waveform, an overall derived from a vibration envelope spectrum or other specialized signal analysis technique or a logical combination (AND, OR, XOR, etc.) of these signal attributes. In addition, a myriad assortment of other parametric data, including system load, motor voltage and phase information, bearing temperature, flow rates, and the like, can likewise be used as the basis for forming additional smart bands. In example embodiments, Smart Band symptoms may be used as building blocks for an expert system whose engine would utilize these inputs to derive diagnoses. Some of these mini-diagnoses may then in turn be used as Smart-Band symptoms (smart bands can include even diagnoses) for more generalized diagnoses.

[0288] Embodiments of the methods and systems disclosed herein may include a neural net expert system using smart bands. Typical vibration analysis engines are rule-based (i.e., they use a list of expert rules which, when met, trigger specific diagnoses). In contrast, a neural approach utilizes the weighted triggering of multiple input stimuli into smaller analytical engines or neurons which in turn feed a simplified weighted output to other neurons. The output of these neurons can be also classified as smart bands which in turn feed other neurons. This produces a more layered approach to expert diagnosing as opposed to the one-shot approach of a rule-based system. In example embodiments, the expert system utilizes this neural approach using smart bands; however, it does not preclude rule-based diagnoses being reclassified as smart bands as further stimuli to be utilized by the expert system. From this point-of-view, it can be overviewed as a hybrid approach, although at the highest level it is essentially neural.

[0289] Embodiments of the methods and systems disclosed herein may include use of database hierarchy in analysis smart band symptoms and diagnoses may be assigned to various hierarchical database levels. For example, a smart band may be called “Looseness” at the bearing level, trigger37“Looseness” at the equipment level, and trigger “Looseness” at the machine level. Another example would be having a smart band diagnosis called “Horizontal Plane Phase Flip” across a coupling and generate a smart band diagnosis of “Vertical Coupling Misalignment” at the machine level.

[0290] Embodiments of the methods and systems disclosed herein may include expert system GUIs. In example embodiments, the system undertakes a graphical approach to defining smart bands and diagnoses for the expert system. The entry of symptoms, rules, or more generally smart bands for creating a particular machine diagnosis, may be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring. The proposed graphical interface consists of four major components: a symptom parts bin, diagnoses bin, tools bin, and graphical wiring area (“GW A”). In example embodiments, a symptom parts bin includes various spectral, waveform, envelope and any type of signal processing characteristic or grouping of characteristics such as a spectral peak, spectral harmonic, waveform true-peak, waveform crest-factor, spectral alarm band, and so on. Each part may be assigned additional properties. For example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a lx, 2x, 3x running speed, lx, 2x, 3x gear mesh, lx, 2x, 3x blade pass, number of motor rotor bars x running speed, and so on.

[0291] In example embodiments, the diagnoses bin includes various pre-defined as well as user- defined diagnoses such as misalignment, imbalance, looseness, bearing faults, and so on. Like parts, diagnoses may also be used as parts for the pinposes of building more complex diagnoses. In example embodiments, the tools bin includes logical operations such as AND, OR, XOR, etc. or other ways of combining the various parts listed above such as Find Max, Find Min, Interpolate, Average, other Statistical Operations, etc. In example embodiments, a graphical wiring area includes parts from the parts bin or diagnoses from the diagnoses bin and may be combined using tools to create diagnoses. The various parts, tools and diagnoses will be represented with icons which are simply graphically wired together in the desired manner.

[0292] Embodiments of the methods and systems disclosed herein may include a graphical approach for back-calculation definition, hi example embodiments, the expert system also provides the opportunity for the system to learn. If one already knows that a unique set of stimuli or smart bands corresponds to a specific fault or diagnosis, then it is possible to back-calculate a set of coefficients that when applied to a future set of similar stimuli would arrive at the same diagnosis. In example embodiments, if there are multiple sets of data, a best-fit approach may be used. Unlike the smart band GUL this embodiment will self-generate a wiring diagram. In example embodiments, the user may tailor the back-propagation approach settings and use a database browser to match specific sets of data with the desired diagnoses. In example embodiments, the desired diagnoses may be created or custom tailored with a smart band GUI. In example embodiments, after that, a user may press the GENERATE button and a dynamic wiring of the symptom-to-diagnosis may appear on the screen as it works through the algorithms to achieve the best fit. In example embodiments, when complete, a variety of s tatistics are presented which de tail38how well the mapping process proceeded. In some cases, no mapping may be achieved i£, for example, the input data was all zero or the wrong data (mistakenly assigned) and so on. Embodiments of the methods and systems disclosed herein may include bearing analysis methods. In example embodiments, bearing analysi s methods may be used in conjunction with a computer aided design (“CAD”), predictive deconvolution, minimum variance distortionless response (“MVDR”) and spectrum sum-of-harmonics.

[0293] In recent years, there has been a strong drive to save power which has resulted in an influx of variable frequency drives and variable speed machinery. In example embodiments, a bearing analysis method is provided. In example embodiments, torsional vibration detection and analysis is provided utilizing transitory signal analysis to provide an advanced torsional vibration analysis for a more comprehensive way to diagnose machinery where torsional forces are relevant (such as mach inery with rotating components) . Due prim ari ly to the decrease in cost of m otor speed control systems, as well as the increased cost and consciousness of energy-usage, it has become more economically justifiable to take advantage of the potentially vast energy savings of load control. Unfortunately, one frequently overlooked design aspect of this issue is that of vibration. When a machine is designed to run at only one speed, it is far easier to design the physical structure accordingly so as to avoid mechanical resonances both structural and torsional, each of which can dramatically shorten the mechanical health of a machine. This would include such structural characteristics as the types of materials to use, their weight, stiffening member requirements and placement, bearing types, bearing location, base support constraints, etc. Even with machines running at one speed, designing a structure so as to minimize vibration can prove a daunting task, potentially requiring computer modeling, finite-element analysis, and field testing. By throwing variable speeds into the mix, in many cases, it becomes impossible to design for all desirable speeds. The problem then becomes one of minimization, e.g., by speed avoidance. This is why- many modem motor controllers are typically programmed to skip or quickly pass through specific speed ranges or bands. Embodiments may include identifying speed ranges in a vibration monitoring system. Non-torsional, structural resonances are typically fairly easy to detect using conventional vibration analysis techniques. However, this is not the case for torsion. One special area of current interest is the increased incidence of torsional resonance problems, apparently due to the increased torsional stresses of speed change as well as the operation of equipment at torsional resonance speeds. Unlike non-torsional structural resonances which generally manifest their effect with dramatically increased casing or external vibration, torsional resonances generally show no such effect. In the case of a shaft torsional resonance, the twisting motion induced by the resonance may only be discernible by looking for speed and / or phase changes. The current standard methodology for analyzing torsional vibration involves the use of specialized instrumentation. Methods and systems disclosed herein allow7analysis of torsional vibration without such specialized instrumentation. This may consist of shutting the machine down and employing the use of strain gauges and / or other special fixturing such as speed encoder plates and / or gears. Friction wheels are another alternative, but they typically require manual implementation and a specialized analyst. In general, these techniques can be prohibitively expensive and / or inconvenient. An39increasing prevalence of continuous vibration monitoring systems due to decreasing costs and increasing convenience (e.g., remote access) exists. In example embodiments, there is an ability to discern torsional speed and / or phase variations with just the vibration signal. In example embodiments, transient analysis techniques may be utilized to distinguish torsionally induced vibrations from mere speed changes due to process control . In example embodiments, factors for discernment might focus on one or more of the following aspects: the rate of speed change due to variable speed motor control would be relatively slow, sustained and deliberate: torsional speed changes would tend to be short, impulsive and not sustained; torsional speed changes would tend to be oscillatory, most likely decaying exponentially, process speed changes would not; and smaller speed changes associated with torsion relative to tire shaft’s rotational speed which suggest that monitoring phase behavior would show the quick or transient speed bursts in contrast to the slow phase changes historically associated with ramping a machine’s speed up or down (as typified with Bode or Nyquist plots).

[0294] Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When a signal is digitally integrated using software, essentially the spectral low-end frequency data has its amplitude multiplied by a function which quickly blows up as it approaches zero and creates what is known in the industry as a “ski- slope” effect. The amplitude of the ski-slope is essentially the noise floor of the instrument. The simple rem edy for this is the traditional hardware integrator, which can perform at signal-to-noise ratios much greater than that of an already digitized signal. It can also limit the amplification factor to a reasonable level so that multiplication by very large numbers is essentially prohibited. However, at high frequencies where the frequency becomes large, the original amplitude which may be well above the noise floor is multiplied by a very small number (1 / f) that plunges it well below the noise floor. The hardware integrator has a fixed noise floor that although low floor does not scale down with the now lower amplitude high-frequency data. In contrast, the same digital m ultiplication of a digitized high-frequency signal also scales down the noise floor proportionally. In example embodiments, hardware integration may be used below the point of unity gain where (at a value usually determined by units and / or desired signal to noise ratio based on gain) and software integration may be used above the value of unity gain to produce an ideal result. In example embodiments, this integration is performed in the frequency domain. In example embodiments, the resulting hybrid data can then be transformed back into a waveform which should be far superior in signal-to-noise ratio when compared to either hardware integrated or software integrated data. In example embodiments, the streng ths of hardware integration are used in conjunction with those of digital software integration to achieve the maximum signal-to-noise ratio. In example embodiments, the first order gradual hardware integrator high pass filter along with curve fitting allow some relatively low frequency data to get. through while reducing or eliminating the noise, allowing very useful analytical data that steep filters kill to be salvaged.

[0295] Embodiments of the methods and systems disclosed herein may include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is often performed with an up-front Mux whose purpose it is to select a few channels of data among many to feed the40hardware signal processing, A / D, and processing components of a DAQ system. This is done primarily out of practical cost considerations. The tradeoff is that all of the points are not monitored continuously (although they may be monitored to a lesser extent via alternative hardware methods). In example embodiments, multiple scheduling levels are provided. In example embodiments, at the lowest level, which is continuous for the most part, all of the measurement points will be cycled through in round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 points, then each point is serviced once every 15 minutes; however, if a point should alarm by whatever criteria the user selects, its priority level can be increased so that it is serviced more often. As there can be multiple grades of severity for each alarm, so can there me multiple levels of priority with regards to monitoring. In example embodiments, more severe alanns will be monitored more frequently. In example embodiments, a number of additional high-level signal processing techniques can be applied at less frequent intervals. Embodiments may take advantage of the increased processing power of a PC and the PC can temporarily suspend the round-robin route collection (with its multiple tiers of collection) process and stream the required amount of data for a point of its choosing. Embodiments may include various advanced processing techniques such as envelope processing, wavelet analysis, as well as m any other signal processing techniques. In example embodiments, after acquisition of this data, the DAQ card set will continue with its route at the point it was interrupted. In example embodiments, various PC scheduled data acquisitions will follow their own schedules which will be less frequency than the DAQ card route. They may be set up hourly, daily, by number of route cycles (for example, once every' 10 cycles) and also increased scheduling-wise based on their alarm severity priority or type of measurement (e.g., motors may be monitored differently than fans).

[0296] Embodiments of the methods and systems disclosed herein may include data acquisition parking features. In example embodiments, a data acquisition box used for route collection, real time analysis and in general as an acquisition instrument can be detached from its PC (tablet or otherwise) and powered by an external power supply or suitable battery'. In example embodiments, the data collector still retains continuous monitoring capability and its on-board firmw'are can implement dedicated monitoring functions for an extended period of time or can be controlled remotely for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical capabilities for continuous monitoring.

[0297] Embodiments of the methods and systems disclosed herein may include ambient sensing plus local sensing plus vibration for analysis. In example embodiments, ambient environmental temperature and pressure, sensed temperature and pressure may be combined with long / medium term vibration analysis for prediction of any of a range of conditions or characteristics. Varian ts may add infrared sensing, infrared thermography, ultrasound, and many other types of sensors and input types in combination with vibration or with each other. Embodiments of the methods and systems disclosed herein may include a smart route. In example embodiments, the continuous monitoring system’s software will adapt / adjust the data collection sequence based on statistics, analytics, data alarms and dynamic analysis. Typically, the route is set based on the channels the sensors are attached to. In example embodiments, with the crosspoint switch, the Mux can combine41any input Mux channels to the (e.g., eight) output channels. In example embodiments, as channels go into alarm or the system identifies key deviations, it will pause the normal route set in the software to gather specific simultaneous data, from the channels sharing key statistical changes, for more advanced analysis. Embodiments include conducting a smart ODS or smart transfer function.

[0298] Embodiments of the methods and systems disclosed herein may include smart ODS and one or more transfer functions. In example embodiments, due to a system’s multiplexer and crosspoint switch, an ODS, a transfer function, or other special tests on all the vibration sensors attached to a machine / structure can be performed and show exactly how the machine’s points are moving in relationship to each other. In example embodiments, 40-50 kHz and longer data lengths (e.g., at least one minute) may be streamed, which may reveal different information than w7hat a normal ODS or transfer function will show7. In example embodiments, the system will be able to determine, based on the data / statistics / analytics to use, the smart route feature that breaks from the standard route and conducts an ODS across a machine, structure or multiple machines and structures that might show a correlation because the conditions / data directs it. In example embodiments, for the transfer functions there may be an impact hammer used on one channel and then compared against other vibration sensors on the machine. In example embodiments, the system may use the condition changes such as load, speed, temperature or other changes in the machine or system to conduct the transfer function. In example embodiments, different transfer functions may be compared to each other over time. In example embodiments, difference transfer functions may be strung together like a movie that may show how the machinery fault changes, such as a bearing that could show7how it moves through the four stages of bearing failure and so on. Embodiments of the methods and systems disclosed herein may include a hierarchical Mux.

[0299] With reference to Figure 8, the present disclosure generally includes digitally collecting or streaming waveform data 2010 from a machine 2020 whose operational speed can vary from relatively slow rotational or oscillational speeds to much higher speeds in different situations. The waveform data 2010, at least on one machine, may include data from a single axis sensor 2030 mounted at an unchanging reference location 2040 and from a three-axis sensor 2050 mounted at. changing locations (or located at multiple locations), including location 2052. In example embodiments, the waveform data 2010 can be vibration data obtained simultaneously from each sensor 2030, 2050 in a gap-free fonnat for a duration of multiple minutes with maximum resolvable frequencies sufficiently large to capture periodic and transient impact events. By way of this example, the waveform data 2010 can include vibration data that can be used to create an operational deflecting shape. It can also be used, as needed, to diagnose vibrations from which a machine repair solution can be prescribed .

[0300] In example embodiments, the machine 2020 can further include a bousing 2100 that can contain a drive motor 2110 that can drive a shaft 2120. The shaft 2120 can be supported for rotation or oscillation by a set of bearings 2130, such as including a first bearing 2140 and a second bearing 2150. A data collection module 2160 can connect to (or be resident on) the machine 2020. In one example, the data collection module 2160 can be located and accessible through a cloud network42facility 2170, can collect the waveform data 2010 from the machine 2020, and deliver the waveform data 2010 to a remote location. A working end 2180 of the drive shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, a drill, a gear system, a drive system, or other working element, as the techniques described herein can apply to a wide range of machines, equipment, tools, or the like that include rotating or oscillating elements. In other instances, a generator can be substituted for the motor 2110, and the working end of the drive shaft 2120 can direct rotational energy to the generator to generate power, rather than consume it.

[0301] In example embodiments, the waveform data 2010 can be obtained using a predetermined route format based on the layout of the machine 2020. The waveform data 2010 may include data from the single axis sensor 2030 and the three-axis sensor 2050. The single-axis sensor 2030 can serve as a reference probe with its one channel of data and can be fixed at the unchanging location 2040 on the machine under survey. The three-axis sensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes) with its three channels of data and can be moved along a predetermined diagnostic route format from one test point to the next test point. In one example, both sensors 2030, 2050 can be mounted manually to the machine 2020 and can connect to a separate portable computer in certain service examples. The reference probe can remain at one location while the user can move the tri-axial vibration probe along the predetermined route, such as from bearing- to-bearing on a machine. In this example, the user is instructed to locate the sensors at the predetermined locations to complete the survey (or portion thereof) of the machine.

[0302] With reference to Figure 9, a portion of an exemplary m achine 2200 is shown having a tri- axial sensor 2210 mounted to a location 2220 associated with a motor bearing of the machine 2200 with an output shaft 2230 and output member 2240 in accordance with the present disclosure.

[0303] In further examples, the sensors and data acquisition modules and equipment can be integral to, or resident on, the rotating machine. By way of these examples, the machine can contain many single axis sensors and many tri-axial sensors at predetermined locations. The sensors can be originally installed equipment and provided by the original equipment manufacturer or installed at a different time in a retrofit application. The data collection module 2160, or the like, can select and use one single axis sensor and obtain data from it exclusively during the collection of waveform data 2010 while moving to each of the tri-axial sensors. The data collection module 2160 can be resident on the machine 2020 and / or connect via the cloud network facility 2170.

[0304] With reference to Figure 8, the various embodiments include collecting the waveform data 2010 by digitally recording locally, or streaming over, the cloud network facility 2170. The waveform data 2010 can be collected so as to be gap-free with no interruptions and, in some respects, can be similar to an analog recording of waveform data. The waveform data 2010 from all of the channels can be collected for one to two minutes depending on the rotating or oscillating speed of the machine being monitored. In example embodiments, the data sampling rate can be at a relatively high-sampling rate relative to the operating frequency of the machine 2020.

[0305] In example embodiments, a second reference sensor can be used, and a fifth channel of data can be collected. As such, the single-axis sensor can be the first channel and tri-axial vibration can occupy the second, the third, and the fourth data channels. This second reference sensor, like the43first, can be a single axis sensor, such as an accelerometer. In example embodiments, the second reference sensor, like the first reference sensor, can remain in the same location on the machine for the entire vibration survey on that machine. The location of the first reference sensor (i.e., the single axis sensor) may be different than the location of the second reference sensors (i.e., another single axis sensor). In certain examples, the second reference sensor can be used when the machine has two shafts with different operating speeds, with the two reference sensors being located on the two different shafts. In accordance with this example, further single-axis reference sensors can be employed at additional but different unchanging locations associated with the rotating machine.|0306j Inexample embodiments, the waveform data can be transmitted electronically in a gap-free free format at a significantly high rate of sampling for a relatively longer period of time. In one example, the period of time is 60 seconds to 120 seconds. In another example, the rate of sampling is 100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will be appreciated in light of this disclosure that the waveform data can be shown to approximate more closely some of the wealth of data available from previous instances of analog recording of waveform data.

[0307] In example embodiments, sampling, band selection, and filtering techniques can permit one or more portions of a long stream of data (i.e., one to two minutes in duration) to be under sampled or over sampled to realize varying effective sampling rates. To this end, interpolation and decimation can be used to further realize varying effective sampling rates. For example, oversampling may be applied to frequency bands that are proximal to rotational or oscillational operating speeds of th e sampled machine, or to harmonics thereof, as vibration effects may tend to be more pronounced at those frequencies across the operating range of the machine. In example embodiments, the digitally-sampled data set can be decimated to produce a lower sampling rate. It will be appreciated in light of the disclosure that decimate in this context can be the opposite of interpolate. In example embodiments, decimating the data set can include first applying a low-pass filter to the digi tally-sampled data set and then undersampling the data set.

[0308] In one example, a sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz, but the remaining nine points of that portion of the waveform are effecti vely discarded and not included in the modeling of the sample waveform. Moreover, this type of bare undersampling can create ghost frequencies due to the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample waveform.

[0309] Most hardware for analog-to-digital conversions uses a sample-and-hold circuit that can charge up a capacitor for a given amount of time such that an average value of the waveform is determined over a specific change in time. It will be appreciated in light of the disclosure that the value of the waveform over the specific change in time is not linear but more similar to a cardinal sinusoidal (“sine”) function; therefore, it can be shown that more emphasis can be placed on the waveform data at the center of the sampling interval with exponential decay of the cardinal sinusoidal signal occurring from its center.

[0310] By way of the above example, the sample waveform at 100 Hz can be hardware-sampled at 10 Hz and therefore each sampling point is averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz can have each point averaged over 10 milliseconds). In contrast to the effective44discarding of nine out of the ten data points of the sampled waveform as discussed above, the present disclosure can include weighing adjacent data. The adjacent data can refer to the sample points that were previously discarded and the one remaining point that was retained. In one example, a low pass filter can average the adjacent sample data linearly, i.e., determining the sum of every ten points and then dividing that sum by ten. In a further example, the adjacent data can be weighted with a sine function. The process of weighting the original waveform with the sine function can be referred to as an impulse function, or can be referred to in the time domain as a convolution.

[0311] The present disclosure can be applicable to not only digitizing a waveform signal based on a detected voltage, but can also be applicable to digitizing waveform signals based on current waveforms, vibration waveforms, and image processing signals including video signal rasterization. In one example, the resizing of a window on a computer screen can be decimated, albeit in at least two directions, hi these further examples, it will be appreciated that undersampling by itself can be shown to be insufficient. To that end, oversampling or upsampling by itself can similarly be shown to be insufficient, such that interpolation can be used like decimation but in lieu of only under sampling by itself.

[0312] It will be appreciated in light of the disclosure that interpolation in this context can refer to first applying a low pass filter to the digitally-sampled waveform data and then upsampling the waveform data. It will be appreciated in light of the disclosure that real -world examples can often require the use of use non-integer factors for decimation or interpolation, or both. To that end, the present disclosure includes interpolating and decimating sequentially in order to realize a noninteger factor rate for interpolating and decimating. In one example, interpolating and decimating sequentially can define applying a low-pass filter to the sample waveform, then interpolating the waveform after the low-pass filter, and then decimating the waveform after the interpolation. In example embodiments, the vibration data can be looped to purposely emulate conventional tape recorder loops, with digital filtering techniques used with the effective splice to facilitate longer analyses. It will be appreciated in light of the disclosure that the above techniques do not preclude waveform, spectrum, and other types of analyses from being processed and displayed with a GUI of the user at the time of collection. It will be appreciated in light of the disclosure that newer systems can permit this functionality to be performed in parallel to the high-performance collection of the raw waveform data.

[0313] With respect to time of collection issues, it will be appreciated that older systems using the compromised approach of improving data resolu tion, by collecting at different sampling rates and data lengths, do not in fact save as much time as expected. To that end, every' time the data acquisition hardware is stopped and started, latency issues can be created, especially when there is hardware auto-scaling performed. The same can be true with respect to data retrieval of the route information (i.e., test locations) that is often in a database format and can be exceedingly slow. The storage of the raw data in bursts to disk (whether solid state or otherwise) can also be undesirably slow.45

[0314] In contrast, the many embodiments include digitally streaming the waveform data 2010, as disclosed herein, and also enjoying the benefit of needing to load the route parameter information while setting the data acquisition hardware only once. Because the waveform data 2010 is streamed to only one file, there is no need to open and close files, or switch between loading and writing operations with the storage medium. It can be shown that the collection and storage of the waveform data 2010, as described herein, can be shown to produce relatively more meaningful data in significantly less time than the traditional batch data acquisition approach. An example of this includes an electric motor about which waveform data can be collected with a data length of 4K points (i.e., 4,096) for sufficiently high resolution in order to, among other things, distinguish electrical sideband frequencies. For fans or blowers, a reduced resolution of IK (i.e., 1,024) can be used. In certain instances, IK can be the minimum waveform data length requirement. The sampling rate can be 1,280 Hz and that equates to an Fmax of 500 Hz. It will be appreciated in light of the disclosure that oversampling by an industry standard factor of 2.56 can satisfy the necessary two-times (2x) oversampling for the Nyquist Criterion with some additional leeway that can accommodate anti-aliasing filter-rolloff. The time to acquire this waveform data would be 1,024 points at 1,280 hertz, which are 800 milliseconds.

[0315] To improve accuracy, the waveform data can be averaged. Eight averages can be used with, for example, fifty percent overlap. This would extend the time from 800 milliseconds to 3.6 seconds, which is equal to 800 msec x 8 averages x 0.5 (overlap ratio) + 0.5 x 800 msec (nonoverlapped head and tail ends). After collection at Fmax = 500 Hz waveform data, a higher sampling rate can be used. In one example, ten times (lOx) the previous sampling rate can be used and Fmax = 10 kHz. By way of this example, eight averages can be used with fifty percent (50%) overlap to collect waveform data at this higher rate that can amount to a collection time of 360 msec or 0.36 seconds. It will be appreciated in light of the disclosure that it can be necessary to read the hardware collection parameters for the higher sampling rate from the route list, as well as permit hardware auto-scaling, or the resetting of other necessary' hardware collection parameters, or both. To that end, a few seconds of latency can be added to accommodate the changes in sampling rate. In other instances, introducing latency can accommodate hardware autoscaling and changes to hardware collection parameters that can be required when using the lower sampling rate disclosed herein, hi addition to accommodating the change in sampling rate, additional time is needed for reading the route point information from the database (i.e., where to monitor and where to monitor next), displaying the route information, and processing the waveform data. Moreover, display of the waveform data and / or associated spectra can also consume significant time. In light of the above, 15 seconds to 20 seconds can elapse while obtaining waveform data at each measurement point.

[0316] In further examples, additional sampling rates can be added but this can make the total amounttime for the vibration survey even longer because time adds up from changeovertime from one sampling rate to another and from the time to obtain additional data at different sampling rate. In one example, a lower sampling rate is used, such as a sampling rate of 128 Hz where Fmax = 50 Hz. By way of this example, the vibration survey would, therefore, require an additional 3646seconds for the first set of averaged data at this sampling rate, in addition to others mentioned above, and consequently the total time spent at each measurement point increases even more dramatically. Further embodiments include using similar digital streaming of gap free waveform data as disclosed herein for use with wind turbines and other machines that can have relatively slow speed rotating or oscillating systems. In many examples, the waveform data collected can include long samples of data at a relatively high-sampling rate. In one example, the sampling rate can be 100 kHz and the sampling duration can be for two minutes on all of the channels being recorded. In many examples, one channel can be for the single axis reference sensor and three more data channels can be for the tri-axial three channel sensor. It will be appreciated in light of the disclosure that the long data length can be shown to facili tate detection of extremely low frequency phenomena. The long data length can also be shown to accommodate the inherent speed variability in wind turbine operations. Additionally, the long data length can further be shown to provide the opportunity for using numerous averages such as those discussed herein, to achieve very high spectral resolution, and to make feasible tape loops for certain spectral analyses. Many multiple advanced analytical techniques can now become available because such techniques can use the available long uninterrupted length of waveform data in accordance with the present disclosure.

[0317] It will also be appreciated in light of the disclosure that the simultaneous collection of waveform data from multiple channels can facilitate performing transfer functions between multiple channels. Moreover, the simultaneous collection of waveform data from multiple channels facilitates establishing phase relationships across the machine so that more sophisticated correlations can be utilized by relying on the fact that the waveforms from each of the channels are collected simultaneously. In other examples, more channels in the data collection can be used to reduce the time it takes to complete the overall vibration survey by allowing for simultaneous acquisition of waveform data from multiple sensors that otherwise would have to be acquired, in a subsequent fashion, moving sensor to sensor in the vibration survey.

[0318] The present disclosure includes the use of at least one of the single-axis reference probes on one of the channels to allow for acquisition of relative phase comparisons between channels. The reference probe can be an accelerometer or other type of transducer that is not moved and, therefore, fixed at an unchanging location during the vibration survey of one machine. Multiple reference probes can each be deployed at suitable locations fixed in place (i.e., at unchanging locations) throughout the acquisition of vibration data during the vibration survey. In certain examples, up to seven reference probes can be deployed depending on the capacity of the data collection module 2160 or the like. Using transfer functions or similar techniques, the relative phases of all channels may be compared with one another at all selected frequencies. By keeping the one or more reference probes fixed at their unchanging locations wfiile moving or monitoring the other tri-axial vibration sensors, it can be shown that the entire machine can be mapped with regard to amplitude and relative phase. This can be shown to be true even when there are more measurement points than channels of data collection. With this information, an operating deflection shape can be created that can show dynamic movements of the machine in 3 D, which can provide an invaluable diagnostic tool. In example embodiments, the one or more reference probes can47provide relative phase, rather than absolute phase. It will be appreciated in light of the disclosure that relative phase may not be as valuable absolute phase for some purposes, but the relative phase the information can still be shown to be very' useful.

[0319] In example embodiments, the sampling rates used during the vibration survey can be digitally synchronized to predetermined operational frequencies that can relate to pertinent parameters of the machine such as rotating or oscillating speed. Doing this, permits extracting even more information using synchronized averaging techniques. It will be appreciated in light of the disclosure that this can be done without the use of a key phasor or a reference pulse from a rotating shaft, which is usually not available for route collected data. As such, non-synchronous signals can be removed from a complex signal without the need to deploy synchronous averaging using the key phasor. This can be shown to be very powerful when analyzing a particular pinion in a gearbox or generally applied to any component within a complicated mechanical mechanism. In many instances, the key phasor or the reference pulse is rarely available with route collected data, but the techniques disclosed herein can overcome this absence. In example embodiments, there can be multiple shafts running at different speeds within the machine being analyzed. In certain instances, there can be a single-axis reference probe for each shaft. In other instances, it is possible to relate the phase of one shaft to another shaft using only one single axis reference probe on one shaft at its unchanging location. In example embodiments, variable speed equipment can be more readily analyzed with relatively longer duration of data relative to single speed equipment. The vibration survey can be conducted at several machine speeds within the same contiguous set of vibration data using the same techniques disclosed herein. These techniques can also permit the study of the change of the relationship between vibration and the change of the rate of speed that was not available before.

[0320] In example embodiments, there are numerous analytical techniques that can emerge because raw waveform data can be captured in a gap-free digital format as disclosed herein. The gap-free digital format can facili tate many paths to analyze the waveform data in many ways after the fact to identify specific problems. The vibration data collected in accordance with the techniques disclosed herein can provide the analysis of transient, semi-periodic and very low frequency phenomena. The waveform data acquired in accordance with the present disclosure can contain relatively longer streams of raw gap-free waveform data that can be conveniently played back as needed, and on which many and varied sophisticated analytical techniques can be performed. A large number of such techniques can provide for various forms of filtering to extract low amplitude modulations from transient impact data that can be included in the relatively longer stream of raw gap-free waveform data. It will be appreciated in light of the di sclosure that in past data coll ecti on practices, these types of phenom ena were typically lost by the averaging process of the spectral processing algorithms because the goal of the previous data acquisition module was purely periodic signals: or these phenomena were lost to file size reduction methodologies due to the fact that much of the content from an original raw signal was typically discarded knowing it would not be used.48

[0321] In example embodiments, there is a method of monitoring vibration of a machine having at least one shaft supported by a set of bearings. The method includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method also includes monitoring a second, third, and fourth data channel assigned to a three- axis sensor. The method further includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation; and determining a change in relative phase based on the digital waveform data. The method also includes the tri-axial sensor being located at a plurality of positions associated with the machine while obtaining the digital waveform. In example embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In example embodiments, the data is received from all of the sensors on all of their channels simultaneously.

[0322] The method also includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In example embodiments, the unchanging location of the reference sensor is a position associated with a shaft of the machine. In example embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In example embodiments, the unchanging location is a position associated with a shaft of the machine and, wherein, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine. The various embodiments include methods of sequentially monitoring vibration or similar process parameters and signals of a rotating or oscillating machine or analogous process machinery' from a number of channels simultaneously, which can be known as an ensemble. In various examples, the ensemble can include one to eight channels. In further examples, an ensemble can represent a logical measurement grouping on the equipment being monitored whether those measurement locations are temporary' for measurement, supplied by the original equipment manufacturer, retrofi t at a later date, or one or more combinations thereof.

[0323] In one example, an ensemble can monitor bearing vibration in a single direction. In a further example, an ensemble can monitor three different directions (e.g., orthogonal directions) using a tri-axial sensor. In yet further examples, an ensemble can monitor four or more channels where the first channel can monitor a single axis vibration sensor, and the second, the third, and the fourth channels can monitor each of the three directions of the tri-axial sensor. In other examples, the ensemble can be fixed to a group of adjacent bearings on the same piece of equipment or an associated shaft. The various embodiments provide methods that include strategies for collecting waveform data from various ensembles deployed in vibration studies or the like in a relatively more efficient manner. The methods also include simultaneously monitoring of a reference channel assigned to an unchanging reference location associated with the ensemble monitoring the machine. The cooperation with the reference channel can be shown to support a more complete correlation of the collected waveforms from the ensembles. The reference sensor on the reference channel can be a single axis vibration sensor, or a phase reference sensor that can be triggered by a reference location on a rotating shaft or the like. As disclosed herein, the methods can further49include recording gap-free digital waveform data simultaneously from all of the channels of each ensemble at a relatively high rate of sampling so as to include all frequencies deemed necessary for the proper analysis of the machinery' being monitored while it is in operation. The data from the ensembles can be streamed gap-free to a storage medium for subsequent processing that can be connected to a cloud network facility, a local data link, BLUETOOTH™ connectivity, cellular data connectivity', or the like.

[0324] In example embodiments, the methods disclosed herein include strategies for collecting data from the various ensembles including digital signal processing techniques that can be subsequently applied to data from the ensembles to emphasize or better isolate specific frequencies or waveform phenomena. This can be in contrast with current methods that collect multiple sets of data at different sampling rates, or with different hardware filtering configurations including integration, that provide relatively less post-processing flexibility because of the commitment to these same (known as a priori hardware configurations). These same hardware configurations can also be shown to increase time of the vibration survey due to the latency delays associated with configuring the hardware for each independent test. In example embodiments, the methods for collecting data from various ensembles include data marker technology that can be used for classifying sections of streamed data as homogenous and belonging to a specific ensemble. In one example, a classification can be defined as operating speed. In doing so, a mul titude of ensembles can be created from what conventional systems would collect as only one. The many embodiments include post-processing analytic techniqu es for comparing th e relative phases of all the frequencies of interest not only between each channel of the collected ensemble but also between all of the channels of all of the ensembles being monitored, when applicable.

[0325] The present disclosure can include markers that can be applied to a time mark or a sample length within the raw waveform data. The markers generally fall into two categories: preset or dynamic. The preset markers can correlate to preset or existing operating conditions (e.g., load, head pressure, air flow cubic feet per minute, ambient temperature, RPMs, and the like.). These preset markers can be fed into the data acquisition system directly. In certain instances, the preset markers can be collected on data channels in parallel with the waveform data (e.g., waveforms for vibration, current, voltage, etc.). Alternatively, the values for the preset markers can be entered manually.

[0326] For dynamic markers such as trending data, it can be important to compare similar data like comparing vibration amplitudes and patterns with a repeatable set of operating parameters. One example of the present disclosure includes one of the parallel channel inputs being a key phasor trigger pulse from an operating shaft that can provide RPM information at the instantaneous time of collection. In this example of dynamic markers, sections of collected waveform data can be marked with appropriate speeds or speed ranges.

[0327] The present disclosure can also include dynamic markers that can correlate to data that can be derived from post processing and analytics perfbnned on the sample waveform. In further embodiments, the dynamic markers can also correlate to post-collection derived parameters including RPMs, as well as other operationally derived metrics such as alarm conditions like a50maximum RPM. In certain examples, many modern pieces of equipment that are candidates for a vibration survey with the portable data collection systems described herein do not include tachometer information. This can be true because it is not always practical or cost-justifiable to add a tachometer even though the measurement of RPM can be of primary' importance for the vibration survey and analysis. It will be appreciated that for fixed speed machinery obtaining an accurate RPM measurement can be less important especially when the approximate speed of the machine can be ascertained before-hand; however, variable-speed drives are becoming increasingly prevalent. It will also be appreciated in light of the disclosure that various signal processing techniques can pennit the derivation of RPM from the raw data without the need for a dedicated tachometer signal.

[0328] In many embodiments, the RPM information can be used to mark segments of the raw waveform data over its collection history. Further embodiments include techniques for collecting instrument data following a prescribed route of a vibration study. The dynamic markers can enable analysis and trending software to utilize multiple segments of the collection interval indicated by the markers (e.g., two minutes) as multiple historical collection ensembles, rather than just one as done in previous systems where route collection systems would historically store data for only one RPM setting. This could, in turn, be extended to any other operational parameter such as load setting, ambient temperature, and the like, as previously described. The dynamic markers, however, that can be placed in a type of index file pointing to the raw7data stream can classify portions of the stream in homogenous entities that can be more readily compared to previously collected portions of the raw data stream.

[0329] The many embodiments include the hybrid relational metadata-binary storage approach that can use the best of pre-existing technologies for both relational and raw7data streams. In example embodiments, the hybrid relational metadata - binary storage approach can marry them together with a variety of marker linkages. The marker linkages can permi t rapid searches through the relational metadata and can allow for more efficient analyses of the raw data using conventional SQL techniques with pre-existing technology. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional database technologies do not provide.

[0330] The marker linkages can also permit rapid and efficient storage of the raw data using conventional binary storage and data compression techniques. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional raw data technologies provide such as TMDS (National Instruments), UFF (Universal File Format such as UFF58), and the like. The marker linkages can further permit using the marker technology links where a vastly richer set of data from the ensembles can be amassed in the same collection time as more conventional systems. The richer set of data from the ensembles can store data snapshots associated with predetermined collection criterion and the proposed system can derive multiple snapshots from the collected data streams utilizing the marker technology. In doing so, it can be shown that a relatively richer analysis of the collected data can be achieved. One such benefi t can include more trending points of vibration at a specific frequency or order of running51speed versus RPM, load, operating temperature, flow rates, and the like, which can be collected for a similar time relative to what is spent collecting data with a conventional system.

[0331] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines, elements of the machines and the environm ent of the machines including heavy duty' machines deployed at a local job site or at distributed job sites under common control. The heavy-duty machines may include earthmoving equipment, heavy duty' on-road industrial vehicles, heavy duty off-road industrial vehicles, industrial machines deployed in various settings such as turbines, turbomachinery, generators, pumps, pulley systems, manifold and valve systems, and the like. In example embodiments, heavy industrial machinery may also include earth-moving equipment, earthcompacting equipment, hauling equipment, hoisting equipment, conveying equipment, aggregate production equipment, equipment used in concrete construction, and piledriving equipment. In examples, earth moving equipment may include excavators, backhoes, loaders, bulldozers, skid steer loaders, trenchers, motor graders, motor scrapers, crawler loaders, and wheeled loading shovels. In examples, construction vehicles may include dumpers, tankers, tippers, and trailers. In examples, material handling equipment may include cranes, conveyors, forklift, and hoists. In examples, construction equipment may include tunnel and handling equipment, road rollers, concrete mixers, hot mix plants, road making machines (compactors), stone crashers, pavers, slurry' seal machines, spraying and plastering machines, and heavy-duty pumps. Further examples of heavy industrial equipment may include different systems such as implement traction, structure, power train, control, and information. Heavy industrial equipment may include many different powertrains and combinations thereof to provide power for locomotion and to also provide power to accessories and onboard functionality. In each of these examples, the platform 100 may deploy the local data collection system 102 into the environment 104 in which these machines, motors, pumps, and the like, operate and directly connected integrated into each of the machines, motors, pumps, and the like.

[0332] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines in operation and machines in being constructed such as turbine and generator sets like Siemens™ SGT6-5000F ™ gas turbine, an SST-900™ steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator, and the like. In example embodiments, the local data collection system 102 may be deployed to monitor steam turbines as they rotate in the currents caused by hot water vapor that may be directed through the turbine but otherwise generated from a different source such as from gas-fired burners, nuclear cores, molten salt loops and the like. In these systems, the local data collection system 102 may monitor the turbines and the water or other fluids in a closed loop cycle in which water condenses and is then heated until it evaporates again. The local data collection system 102 may monitor the steam turbines separately from the fuel source deployed to heat the water to steam. In examples, working temperatures of steam turbines may be between 500 and 650 °C. In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement.52

[0333] The local data collection system 102 may also be deployed in a gas turbines arrangement and therefore not only monitor the turbine in operation but also monitor the hot combustion gases feed into the turbine that may be in excess of 1,500 °C. Because these gases are much hotter than those in steam turbines, the blades may be cooled with air that may flow out of small openings to create a protective film or boundary’ layer between the exhaust gases and the blades. This temperature profile may be monitored by the local data collection system 102. Gas turbine engines, unlike typical steam turbines, include a compressor, a combustion chamber, and a turbine all of which are journaled for rotation with a rotating shaft. The construction and operation of each of these components may be monitored by the local data collection system 102.

[0334] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from water turbines serving as rotary’ engines that may harvest energy from moving water and are used for electric power generation. The type of water turbine or hydro-power selected for a project may be based on the height of standing water, often referred to as head, and the flow (or volume of water) at the site. In this example, a generator may be placed at the top of a shaft that connects to the water turbine. As the turbine catches the naturally moving water in its blade and rotates, the turbine sends rotational power to the generator to generate electrical energy’. In doing so, the platform 100 may monitor signals from the generators, the turbines, the local water system, flow controls such as dam windows and sluices. Moreover, the platform 100 may monitor local conditions on the electric grid including load, predicted demand, frequency response, and the like, and include such information in the monitoring and control deployed by platform 100 in these hydroelectric settings.

[0335] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy’ plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources. In example embodiments, elements in such systems may include transmission lines, heat exchangers, desulphurization scrubbers, pumps, coolers, recuperators, chillers, and the like. In example embodiments, certain implementations of turbomachinery, turbines, scroll compressors, and the like may be configured in arrayed control so as to monitor large facilities creating electricity for consumption, providing refrigeration, creating steam for local manufacture and heating, and the like, and that arrayed control platforms may be provided by the provider of the industrial equipment such as Honeywell and their Experion™ PKS platform. In example embodiments, the platform 100 may specifically communicate with and integrate the local manufacturer-specific controls and may allow equipment from one manufacturer to communicate with other equipment. Moreover, the platform 100 provides allows for the local data collection system 102 to collect information across systems from many different manufacturers. In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from marine industrial equipment.53marine diesel engines, shipbuilding, oil and gas plants, refineries, petrochemical plant, ballast water treatment solutions, marine pumps and turbines, and the like.

[0336] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from heavy industrial equipment and processes including monitoring one or more sensors. By way of this example, sensors may be devices that may be used to detect or respond to some type of input from a physical environment, such as an electrical, heat or optical signal. In example embodiments, the local data collection system 102 may include multiple sensors such as, without limitation, a temperature sensor, a pressure sensor, a torque sensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, a radiation sensor, a position sensor, an acceleration sensor, a strain sensor, a pressure cycle sensor, a pressure sensor, an air temperature sensor, and the like. The torque sensor may encompass a magnetic twist angle sensor. In one example, the torque and speed sensors in the local data collection system 102 may be similar to those discussed in U.S. Patent Number 8,352,149 to Meachem, issued 8 January 2013 and hereby incorporated by reference as if fully set forth herein. In example embodiments, one or more sensors may be provided such as a tactile sensor, a biosensor, a chemical sensor, an image sensor, a humidity sensor, an inertial sensor, and the like.

[0337] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors that may provide signals for fault detection including excessive vibration, incorrect material, incorrect, material properties, trueness to the proper size, trueness to the proper shape, proper weight, trueness to balance. Additional fault sensors include those for inventory control and for inspections such as to confirm that parts are packaged to plan, parts are to tolerance in a plan, occurrence of packaging damage or stress, and sensors that may indicate the occurrence of shock or damage in transit. Additional fault sensors may include detection of the lack of lubrication, over lubrication, the need for cleaning of the sensor detection window, the need for maintenance due to low lubrication, the need for maintenance due to blocking or reduced flow in a lubrication region, and the like.

[0338] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 that, includes aircraft operations and manufacture including monitoring signals from sensors for specialized applications such as sensors used in an aircraft’s Attitude and Heading Reference System (AHRS), such as gyroscopes, accelerometers, and magnetometers. In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from image sensors such as semiconductor charge coupled devices (CCDs), active pixel sensors, in complementary metal- oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies. In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as an infra-red (IR) sensor, an ultraviolet (UV) sensor, a touch sensor, a proximity sensor, and the like. In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for optical character recognition (OCR), reading barcodes, detecting surface acoustic waves, detecting transponders,54communicating with home automation systems, medical diagnostics, health monitoring, and the like.

[0339] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as a Micro-Electro- Mechanical Systems (MEMS) sensor, such as ST Microelectronic’s™ LSM303AH smart MEMS sensor, which may include an ultra-low-power high-performance system-in-package featuring a 3D digital linear acceleration sensor and a 3D digital magnetic sensor.

[0340] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines such as turbines, windmills, industrial vehicles, robots, and the like. These large mechanical machines include multiple components and elements providing multiple subsystems on each machine. To that end, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from individual elements such as axles, bearings, belts, buckets, gears, shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets, sleeves, valves, wheels, actuators, motors, servomotor, and the like. Many of the machines and their elements may include servomotors. The local data collection system 102 may monitor the motor, the rotary encoder, and the potentiometer of the servomechanism to provide three-dimensional detail of position, placement, and progress of industrial processes.

[0341] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from gear drives, powertrains, transfer cases, multispeed axles, transmissions, direct drives, chain drives, belt-drives, shaft-drives, magnetic drives, and similar meshing mechanical drives. In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from faul t conditions of industrial machines that may include overheating, noise, grinding gears, locked gears, excessive vibration, wobbling, under-inflation, over-inflation, and the like. Operation faults, maintenance indicators, and interactions from other machines may cause maintenance or operational issues may occur during operation, during installation, and during maintenance. The faults may occur in the mechanisms of the industrial machines but may also occur in infrastructure that supports the machine such as its wiring and local installation platforms. In example embodiments, the large industrial machines may face different types of fault conditions such as overheating, noise, grinding gears, excessive vibration of machine parts, fan vibration problems, problems with large industrial machines rotating parts.

[0342] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from industrial machinery including failures that may be caused by premature bearing failure that may occur due to contamination or loss of bearing lubricant. In another example, a mechanical defect such as misalignment of bearings may occur. Many factors may contribute to the failure such as metal fatigue, therefore, the local data collection system 102 may monitor cycles and local stresses. By way of this example, the platform 100 may monitor the incorrect operation of machine parts, lack of maintenance and55servicing of parts, corrosion of vital machine parts, such as couplings or gearboxes, misalignment of machine parts, and the like. Though the fault occurrences cannot be completely stopped, many industrial breakdowns may be mitigated to reduce operational and financial losses. The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments wherein it has been shown to present a cost-savings over regularly-scheduled maintenance processes that replace parts according to a rigid expiration of time and not actual load and wear and tear on tlie element or machine. To that end, the platform 10 may provide reminders of, or perform some, preventive measures such as adhering to operating manual and mode instructions for machines, proper lubrication, and maintenance of machine parts, minimizing or eliminating overrun of machines beyond their defined capacities, replacement of worn but still functional parts as needed, properly training the personnel for machine use, and the like.

[0343] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by a plurality of phy sical, electronic, and symbolic formats or signals. The platform 100 may employ signal processing including a plurality of mathematical, statistical, computational, heuristic, and linguistic representations and processing of signals and a plurality of operations needed for extraction of useful information from signal processing operations such as techniques for representation, modeling, analysis, synthesis, sensing, acquisition, and extraction of information from signals. In examples, signal processing may be performed using a plurality of techniques, including but not limited to transformations, spectral estimations, statistical operations, probabilistic and stochastic operations, numerical theory analysis, data mining, and the like. The processing of various types of signals forms the basis of many electrical or computational processes. As a result, signal processing applies to almost all disciplines and applications in the industrial environment such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, quality improvements such as noise reduction, image enhancement, and the like. Signal processing for images may include pattern recognition for manufacturing inspections, quality inspection, and automated operational inspection and maintenance. The platform 100 may employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations and may be used in applications such as computer vision, speech and text processing, radar processing, handwriting recognition, CAD systems, and the like. The platform 100 may employ supervised classification and unsupervised classification. The supervised learning classification algorithms may be based to create classifiers for image or pattern recognition, based on training data obtained from different object classes. The unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering. For example, some of the analysis techniques used in unsupervised learning may include K-means clustering, Gaussian mixture models. Hidden Markov models, and the like. The algorithms used in supervised and unsupervised learning methods of pattern recognition enable the use of pattern56recognition in various high precision applications. The platform 100 may use pattern recognition in face detection related applications such as security systems, tracking, sports related applications, fingerprint analysis, medical and forensic applications, navigation and guidance systems, vehicle tracking, public infrastructure systems such as transport systems, license plate monitoring, and the like.

[0344] In example embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 using machine learning to enable derivation-based learning outcomes from computers without the need to program them. The platform 100 may, therefore, learn from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data. In example embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include presenting a set of example inputs and desired outputs to the machine learning systems. Unsupervised learning may include the learning algorithm itself structuring its input by methods such as pattern detection and / or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect deci sions. In examples, machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rulebased machine learning, sparse dictionary' learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), a priori algorithms, and the like. In example embodiments, certain machine learning algorithms may be used (such as genetic algorithms defined for solving both constrained and unconstrained optimization problems that may be based on natural selection, the process that drives biological evolution). By way of this example, genetic algorithms may be deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. In an example, the genetic algorithm may be used to address problems of mixed integer programming, where some components restricted to being integer-valued. Genetic algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. By way of this example, the machine learning systems may be used to perform intelligent computing based control and be responsi ve to tasks in a wide variety of systems (such as interactive websites and portals, brain-machine interfaces, online security and fraud detection systems, medical applications57such as diagnosis and therapy assistance systems, classification of DNA sequences, and the like). In examples, machine learning systems may be used in advanced computing applications (such as online advertising, natural language processing, robotics, search engines, software engineering, speech and handwriting recognition, pattern matching, game playing, computational anatomy, bioinformatics systems and the like). In an example, machine learning may also be used in financial and marketing systems (such as for user behavior analytics, online advertising, economic estimations, financial market analysis, and the like).

[0345] Additional details are provided below in connection with the methods, systems, devices, and components depicted in connection with Figures 1 through 6. In example embodiments, methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof. A model may be created by a human who has experience with the industrial environment and may be associated with a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments. The learing machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as cl assifi cation of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like). The machine learning facility may take feedback, such as one or more inputs or measures of success, such that it may train, or improve, its initial model (such as improvements by adjusting weights, rules, parameters, or the like, based on the feedback). For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and / or consumed e.g., such as through combustion, through chemical reactions in battery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like). For example, if a resonance effect, between two machines is adversely affecting one of them, the model may account for this and automatically provide an output that results in changing the operation of one of the machines (such as to reduce the resonance, to increase fuel efficiency of one or both machines). By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for58predicting faults, optimizing operational parameters, and the like). Tins may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes.

[0346] Figure 10 illustrates components and interactions of a data collection architecture involving the application of cognitive and machine learning systems to data collection and processing. Referring to Figure 10, a data collection system 102 may be disposed in an environment (such as an industrial environment where one or more complex systems, such as electro-mechanical systems and machines are manufactured, assembled, or operated). The data collection system 102 may include onboard sensors and may take input, such as through one or more input interfaces or ports 4008, from one or more sensors (such as analog or digital sensors of any type disclosed herein) and from one or more input sources 116 (such as sources that may be available through Wi-Fi, BLUETOOTH™, NFC, or other local network connections or over the Internet). Sensors may be combined and multiplexed (such as with one or more multiplexers 4002). Data may be cached or buffered in a cache / buffer 4022 and made available to external systems, such as a remote host processing system 112 as described elsewhere in this disclosure (which may include an extensive processing architecture 4024, including any of the elements described in connection with ether embodiments described throughout this disclosure and in the Figure), though one or more output interfaces and ports 4010 (which may in embodiments be separate from or the same as the input interfaces and ports 4008). The data collection system 102 may be configured to take input from a host processing system 112, such as input from an analytic system 4018, which may operate on data from the data collection system 102 and data from other input sources 116 to provide analytic results, which in turn may be provided as a learning feedback input 4012 to the data collection system, such as to assist in configuration and operation of the data collection system 102.

[0347] Combination of inputs (including selection of what sensors or input sources to turn “on” or “off”) may be performed under the control of machine-based intelligence, such as using a local cognitive input selection system 4004, an optionally remote cognitive input selection system 4114, or a combination of the two . The cognitive input selection systems 4004, 4014 may use intelligence and machine learning capabilities described elsewhere in this disclosure, such as using detected conditions (such as conditions informed by the input sources 116 or sensors), state information (including state information determined by a machine state recognition system 4020 that may determine a state), such as relating to an operational state, an environmental state, a state within a known process or workflow, a state involving a fault or diagnostic condition, or many others. This may include optimization of input sel ection and configuration based on learning feedback from the learning feedback system 4012, which may include proriding training data (such as from the host processing system 112 or from other data collection systems 102 either directly or from the host. 112) and may include providing feedback metrics, such as success metrics calculated within the analytic system 4018 of the host processing system 112. For example, if a data stream consisting of a particular combination of sensors and inputs yields positive results in a given set of conditions (such as providing improved pattern recognition, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, or the like), then metrics59relating to such results from the analytic system 4018 can be provided via the learning feedback system 4012 to the cognitive input selection systems 4004, 4014 to help configure future data collection to select that combination in those conditions (allowing other input sources to be deselected, such as by powering down the other sensors). In example embodiments, selection and deselection of sensor combinations, under control of one or more of the cognitive input selection systems 4004, may occur with automated variation, such as using genetic programming techniques, based on learning feedback 4012, such as from the analytic system 4018, effective combinations for a given state or set of conditions are promoted, and less effective combinations are demoted, resulting in progressive optimization and adaptation of the local data collection system to each unique environment. Thus, an automatically adapting, multi-sensor data collection system is provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results, energy production or usage, and the like), analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like), and local system metrics (such as bandwidth utilization, storage utilization, power consumption, and the like). In example embodiments, the analytic system 4018, the state system 4020 and the cognitive input selection system 4114 of a host may take data from multiple data collection systems 102, such that optimi zation (including of input selection) may be undertaken through coordinated operation of multiple systems 102. For example, the cognitive input selection system 4114 may understand that if one data collection system 102 is already collecting vibration data for an X-axis, the X-axis vibration sensor for the other data collection system might be turned off, in favor of getting Y-axis data from the other data collector 102. Thus, through coordinated collection by the host cognitive input selection system 4114, the activity of multiple collectors 102, across a host of different sensors, can provide for a rich data set for tire host processing system 112, without wasting energy, bandwidth, storage space, or the like. As noted above, optimization may be based on overall system success metrics, analytic success metrics, and local system metrics, or a combination of the above.

[0348] Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. In example embodiments, machine learning may take advantage of a state machine, such as tracking states of multiple analog and / or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on his torical data about sequences of state information. For example, where a temperature state of an industrial machine exceeds a certain threshold and is followed by a fault condition, such as breaking down of a set of bearings, that temperature state may be tracked by a pattern recognizer, which may produce an output data structure indicating an anticipated bearing fault state (whenever an input state of a high temperature is recognized). A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, galvanic states, magnetic field states,60electrical field states, capacitance states, charge and discharge states, motion, position, and many others. States may comprise combined states, where a data structure includes a series of states, each of which is represented by a place in a byte-like data structure. For example, an industrial machine may be characterized by a genetic structure, such as one that provides pressure, temperature, vibration, and acoustic data, the measurement of which takes one place in the data structure, so that the combined state can be operated on as a byte-like structure, such as a structure for compactly characterizing the current combined state of the machine or environment or compactly characterizing the anticipated state. This byte-like structure can be used by a state machine for machine learning, such as pattern recognition that operates on the structure to determine patterns that reflect combined effects of multiple conditions. A wide variety of such structures can be tracked and used, such as in machine learning, representing various combinations, of various length, of the different elements that can be sensed in an industrial environment. In example embodiments, byte-like structures can be used in a genetic programming technique, such as by substituting different types of data, or data from varying sources, and tracking outcomes over time, so that one or more favorable structures emerges based on the success of those structures when used in real world situations, such as indicating successful predictions of anticipated states, or achievement of success operational outcomes, such as increased efficiency, successful routing of information, achieving increased profits, or the like. That is, by varying what data types and sources are used in byte-like structures that are used for machine optimization over time, a genetic programming-based machine learning facility can “evolve” a set of data structures, consi sting of a favorable mix of data types (e.g., pressure, temperature, and vibration), from a favorable mix of data sources (e.g., temperature is derived from sensor X, while vibration comes from sensor Y), for a given purpose. Different desired outcomes may result in different data structures that are best adapted to support effective achievement of those outcomes over time with application of machine learning and promotion of structures with favorable results for the desired outcome in question by genetic programming. The promoted data structures may provide compact, efficient data for various activities as described throughout this disclosure, including being stored in data pools (which may be optimized by storing favorable data structures that provide the best operational results for a given environment), being presented in data marketplaces (such as being presented as the most effective structures for a given purpose), and the like.

[0349] In example embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In example embodiments, the host processing system 112, such as disposed in the cloud, may include the state system 4020, which may be used to infer or calculate a current state or to determine an anticipated future state relating to the data collection system 102 or some aspect of the environment in which the data collection system 102 is disposed, such as the state of a machine, a component, a workflow, a process, an event (e.g., whether the event has occurred), an object, a person, a condition, a function, or the like. Maintaining state information allows the host processing system 112 to undertake analysis, such as in one or more analytic systems 4018, to determine contextual information, to apply semantic and conditional61logic, and perform many other functions as enabled by the processing architecture 4024 described throughout this disclosure.

[0350] In example embodiments, a platform is provided having cloud-based policy automation engine for loT, with creation, deployment, and management of loT devices. In example embodiments, the platform 100 includes (or is integrated with , or included in) the host processing system 112, such as on a cloud platform, a policy automation engine 4032 for automating creation, deployment, and management of policies to loT devices. Polices, which may include access policies, network usage policies, storage usage policies, bandwidth usage policies, device connection policies, security policies, nde-based policies, role-based polices, and others, may be required to govern the use of loT devices. For example, as loT devices may have many different network and data communications to other devices, policies may be needed to indicate to what devices a given device can connect, what data can be passed on, and what data can be received. As billions of devices with countless potential connections are expected to be deployed in the near future, it becomes impossible for humans to configure policies for loT devices on a connection- by-connection basis. Accordingly, an intelligent policy automation engine 4032 may include cognitive features for creating, configuring, and managing policies. The policy automation engine 4032 may consume information about possible policies, such as from a policy database or library, which may include one or more public sources of available policies. These may be written in one or more conventional policy languages or scripts. The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation, thereby avoiding a remote “takeover’ by a hacker. This may be accomplished in turn by automatically finding and applying security policies that bar connection of the control infrastructure of the machine to the Internet, by requiring access authentication, or the like. The policy automation engine 4032 may include cognitive features, such as varying the application of policies, the configuration of policies, and the like (such as features based on state information from the state system 4020). The policy automation engine 4032 may take feedback, as from the learning feedback system 4012, such as based on one or more analytic results from the analytic system 4018, such as based on overall system results (such as the extent of security breaches, policyviolations, and the like), local results, and analytic results. By variation and selection based on such feedback, the policy automation engine 4032 can, over time, learn to automatically create, deploy, configure, and manage policies across very large numbers of devices, such as managing policies for configuration of connections among loT devices.

[0351] Methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial loT devices, including on-device sensor fusion and data storage for an industrial loT device, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. For example, pressure and temperature data may be multiplexed into a data stream that combines pressure and temperature in a time series, such as in a byte-like structure (where time, pressure, and temperature are bytes in a data structure, so that pressure and temperature remain62linked in time, without requiring separate processing of the streams by outside systems), or byadding, dividing, multiplying, subtracting, or the like, such that the fused data can be stored on the device. Any of the sensor data types described throughout this disclosure can be fused in this manner and stored in a local data pool, in storage, or on an ToT device, such as a data collector, a component of a machine, or the like.

[0352] In example embodiments, a platform is provided having on-device sensor fusion and data storage for industrial loT devices. In example embodiments, a cognitive system is used for a selforganizing storage system 4028 for the data collection system 102. Sensor data, and in particular analog sensor data, can consume large amounts of storage capacity, in particular where a data collector 102 has multiple sensor inputs onboard or from the local environment. Simply storing all the data indefinitely is not typically a favorable option, and even transmitting all of the data may strain bandwidth limitations, exceed bandwidth permissions (such as exceeding cellular data plan capacity), or the like. Accordingly, storage strategies are needed. These typically include capturing only portions of the data (such as snapshots), storing data for limited time periods, storing portions of the data (such as intermediate or abstracted forms), and the like. With many possible selections among these and other options, determining the correct storage strategy may be highly complex. In example embodiments, the self-organizing storage system 4028 may use a cognitive system, based on learning feedback 4012, and use various metrics from the analytic system 4018 or other system of the host cognitive input selection system 4114, such as overall system metrics, analytic metrics, and local performance indicators. The self-organizing storage system 4028 may automatically vary storage parameters, such as storage locations (including local storage on the data collection system 102, storage on nearby data collection systems 102 (such as using peer-to- peer organization) and remote storage, such as network-based storage), storage amounts, storage duration, type of data stored (including individual sensors or input sources 116, as well as various combined or multiplexed data, such as selected under the cogniti ve input selection systems 4004, 4014), storage type (such as using RAM, Flash, or other short-term memory versus available hard drive space), storage organization (such as in raw form, in hierarchies, and the like), and others. Variation of the param eters may be undertaken with feed back, so that over time the data coll ection system 102 adapts its storage of data to optimize itself to the conditions of its environment, such as a particular industrial environment, in a way that results in it storing the data that is needed in the right amounts and of the right type for availability to users.

[0353] In example embodiments, the local cognitive input selection system 4004 may oiganize fusion of data for various onboard sensors, external sensors (such as in the local environment) and other input sources 1 16 to the local collection system 102 into one or more fused data streams, such as using the multiplexer 4002 to create various signals that represent combinations, pennutations, mixes, layers, abstractions, data-metadata combinations, and the like of the source analog and / or digital data that is handled by the data collection system 102. The selection of a particular fusion of sensors may be determined locally by the cognitive input selection system 4004, such as based on learning feedback from the learning feedback system 4012, such as various overall system, analytic system and local system results and metrics. In example embodiments, the63system may learn to fuse particular combinations and permutations of sensors, such as in order to best achieve correct anticipation of state, as indicated by feedback of the analytic system 4018 regarding its ability to predict future states, such as the various states handled by the state system 4020. For example, the input selection system 4004 may indicate selection of a sub-set of sensors among a larger set of available sensors, and the inputs from the selected sensors may be combined, such as by placing input from each of them into a byte of a defined, multi-bit data structure (such as a combination by taking a signal from each at a given sampling rate or time and placing the result into the byte structure, then collecting and processing the bytes over time), by multiplexing in the multiplexer 4002, such as a combination by additive mixing of continuous signals, and the like. Any of a wide range of signal processing and data processing techniques for combination and fusing may be used, including convolutional techniques, coercion techniques, transformation techniques, and the like. The particular fusion in question may be adapted to a given situation by cognitive learning, such as by having the cognitive input selection system 4004 learn, based on feedback 4012 from results (such as feedback conveyed by the analytic system 4018), such that the local data collection system 102 executes context-adaptive sensor fusion.

[0354] In example embodiments, the analytic system 4018 may apply to any of a wide range of analytic techniques, including statistical and econometric techniques (such as linear regression analysis, use similarity matrices, heat map based techniques, and the like), reasoning techniques (such as Bayesian reasoning, rule-based reasoning, inductive reasoning, and the like), iterative techniques (such as feedback, recursion, feed-forward and other techniques), signal processing techniques (such as Fourier and other transforms), pattern recognition techniques (such as Kalman and other filtering techniques), search techniques, probabilistic techniques (such as random walks, random forest algorithms, and the like), simulation techniques (such as random walks, random forest algorithms, linear optimization and the like), and others. This may include computation of various statistics or measures. In example embodiments, the analytic system 4018 maybe disposed, at least in part, on a data collection system 102, such that a local analytic system can calculate one or more measures, such as measures relating to any of the items noted throughout this disclosure. For example, measures of efficiency, power utilization, storage utilization, redundancy, entropy, and other factors may be calculated onboard, so that the data collection 102 can enable various cognitive and learning functions noted throughout this disclosure without dependence on a remote (e.g., cloud-based) analytic system.

[0355] In example embodiments, the host processing system 112, a data collection system 102, or both, may include, connect to, or integrate with, a self-organizing networking system 4020, which may comprise a cognitive system for providing machine-based, intelligent or organization of network utilization for transport of data in a data collection system, such as for handling analog and other sensor data, or other source data, such as among one or more local data collection system s 102 and a host system 112. This may include organizing network utilization for source data delivered to data collection systems, for feedback data, such as analytic data provided to or via a learning feedback system 4012, data for supporting a marketplace (such as described in connection64with other embodiments), and output data provided via output interfaces and ports 4010 from one or more data collection systems 102.

[0356] Methods and systems are disclosed herein for a self-organizing data marketplace for industrial loT data, including where avai lable data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility' with a training set and feedback from measures of marketplace success. A marketplace may be set up initially to make available data collected from one or more industrial environments, such as presenting data by type, by source, by environment by machine, by one or more patterns, or the like (such as in a menu or hierarchy). The marketplace may vary the data collected, the organization of the data, the presentation of the data (including pushing the data to external sites, providing links, configuring APIs by which the data may be accessed, and the like), the pricing of the data, or the like, such as under machine learning, which may vary different parameters of any of the foregoing. The machine learning facility' may manage all of these parameters by self-organization, such as by vary ing parameters over time (including by varying elements of the data types presented), the data sourced used to obtain each type of data, the data structures presented (such as byte-like structures, fused or multiplexed structures (such as representing multiple sensor types), and statistical structures (such as representing various mathematical products of sensor information), among others), the pricing for the data, where the data is presented, how the data is presented (such as by APIs, by links, by push messaging, and the like), how the data is stored, how the data is obtained, and the like. As parameters are varied, feedback may be obtained as to measures of success, such as number of views, yield (e.g., price paid) per access, total yield, per unit profit, aggregate profit, and many others, and the self-organizing machine learning facility may promote configurations that improve measures of success and demote configurations that do not, so that, over time, the marketplace is progressively configured to present favorable combinations of data types (e.g., those that provide robust prediction of anticipated states of particular industrial environments of a given type), from favorable sources (e.g., those that are reliable, accurate and low priced), with effective pricing (e.g., pricing that tends to provide high aggregate profit from the marketplace). The marketplace may include spiders, web crawlers, and the like to seek input data sources, such as finding data pools, connected loT devices, and the like that publish potentially relevant data. These may be trained by human users and improved by machine learning in a manner similar to that described elsewhere in this disclosure.

[0357] In example embodiments, a platform is pro vided having a self-organizing data marketplace for industrial loT data. Referring to Figure 11, in example embodiments, a platform is provided having a cognitive data marketplace 4102, referred to in some cases as a self-organizing data marketplace, for data collected by one or more data collection systems 102 or for data from other sensors or input sources 116 that are located in various data collection environments, such as industrial environments. In addition to data collection systems 102, this may include data collected, handled or exchanged by loT devices, such as cameras, monitors, embedded sensors, mobile devices, diagnostic devices and systems, instrumentation systems, telematics systems, and the like, such as for monitoring various parameters and features of machines, devices, components, parts,65operations, functions, conditions, states, events, workflows and other elements (collectively encompassed by the term “states”) of such environments. Data may also include metadata about any of the foregoing, such as describing data, indicating provenance, indicating elements relating to identity, access, roles, and permissions, providing summaries or abstractions of data, or otherwise augmenting one or more items of data to enable further processing, such as for extraction, transforming, loading, and processing data. Such data (such term including metadata except where context indicates otherwise) may be highly valuable to third parties, either as an individual element (such as the instance where data about the state of an environment can be used as a condition within a process) or in the aggregate (such as the instance where collected data, optionally over many systems and devices in different en vironments can be used to develop models of behavior, to train learning systems, or the like). As billions of loT devices are deployed, with countless connections, the amount of available data will proliferate. To enable access and utilization of data, the cognitive data marketplace 4102 enables various components, features, sendees, and processes for enabling users to supply, find, consume, and transact in packages of data, such as batches of data, streams of data (including event streams), data from various data pools 4120, and the like. In example embodiments, the cognitive data marketplace 4102 may be included in, connected to, or integrated with, one or more other components of a host processing architecture 4024 of a host processing system 112, such as a cloud-based system, as well as to various sensors, input sources 115, data collection systems 102 and the like. The cognitive data marketplace 4102 may include marketplace interfaces 4108, which may include one or more supplier interfaces by which data suppliers may make data available and one more consumer interfaces by which data may be found and acquired. The consumer interface may include an interlace to a data market search system 4118, which may include features that enable a user to indicate what types of data a user wishes to obtain, such as by entering keywords in a natural language search interface that characterize data or metadata. The search interface can use various search and filtering techniques, including keyword matching, collaborative filtering (such as using known preferences or characteristics of the consumer to m atch to similar consumers and the past outcomes of those other consumers), ranking techniques (such as ranking based on su ccess of past outcomes according to various metrics, such as those described in connection with other embodiments in this disclosure). In example embodiments, a supply interface may allow an owner or supplier of data to supply the data in one or more packages to and through the cognitive data marketplace 4102, such as packaging batches of data, streams of data, or the like . The supplier may pre-package data, such as by providing data from a single input source 116, a single sensor, and the like, or by providing combinations, permutations, and the like (such as multiplexed analog data, mixed bytes of data from multiple sources, results of extraction, loading and transformation, results of convolution, and the like), as well as by providing metadata with respect to any of the foregoing. Packaging may include pricing, such as on a per-batch basis, on a streaming basis (such as subscription to an event feed or other feed or stream), on a per item basis, on a revenue share basis, or other basis. For data involving pricing, a data transaction system 4114 may track orders, deliver) / , and utilizati on, including fulfillment of orders. The transaction system 4114 may include66rich transaction features, including digital rights management, such as by managing cryptographic keys that govern access control to purchased data, that govern usage (such as allowing data to be used for a limited time, in a limited domain, by a limited set of users or roles, or for a limited purpose). The transaction system 4114 may manage payments, such as by processing credit cards, wire transfers, debits, and other forms of consideration.

[0358] In example embodiments, a cognitive data packaging system 4012 of the marketplace 4102 may use machine-based intelligence to package data, such as by automatically configuring packages of data in batches, streams, pools, or the like. In example embodiments, packaging maybe according to one or more rules, models, or parameters, such as by packaging or aggregating data that is likely to supplement or complement an existing model. For example, operating data from a group of similar machines (such as one or more industrial machines noted throughout this disclosure) may be aggregated together, such as based on metadata indicating the type of data or by recognizing features or characteristics in the data stream that indicate the nature of the data. In example embodiments, packaging may occur using machine learning and cognitive capabilities, such as by learning what combinations, permutations, mixes, layers, and the like of input sources 116, sensors, information from data pools 4120 and information from data collection systems 102 are likely to satisfy user requirements or result in measures of success. Learning may be based on learning feedback 4012, such as learning based on measures determined in an analytic system 4018, such as system performance measures, data collection measures, analytic measures, and the like. In example embodiments, success measures may be correlated to marketplace success measures, such as viewing of packages, engagement with packages, purchase or licensing of packages, payments made for packages, and the like. Such measures may be calculated in an analytic system 4018, including associating particular feedback measures with search terms and other inputs, so that the cognitive packaging system 4110 can find and configure packages that are designed to provide increased value to consumers and increased returns for data suppliers. In example embodiments, the cognitive data packaging system 4110 can automatically vary packaging, such as using different combinations, permutations, mixes, and the like, and varying weights applied to given input sources, sensors, data pools and the like, using learning feedback 4012 to promote favorable packages and de-emphasize less favorable packages. This may occur using genetic programming and similar techniques that compare outcomes for different packages. Feedback may include state information from the state system 4020 (such as about various operating states, and the like), as well as about marketplace conditions and states, such as pricing and availability information for other data sources. Thus, an adaptive cognitive data packaging system 4110 is provided that automatically adapts to conditions to provide favorable packages of data for the marketplace 4102.

[0359] In example embodiments, a cognitive data pricing system 4112 may be provided to set pricing for data packages. In example embodiments, the data pricing system 4112 may use a set of rules, models, or the like, such as setting pricing based on supply conditions, demand conditions, pricing of various available sources, and the like. For example, pricing for a package may be configured to be set based on the sum of the prices of consti tuent elements (such as input sources,67sensor data, or the like), or to be set based on a rule-based discount to the sum of prices for constituent elements, or the like. Rules and conditional logic may be applied, such as rules that factor in cost factors (such as bandwidth and network usage, peak demand factors, scarcity factors, and the like), rules that factor in utilization parameters (such as the purpose, domain, user, role, duration, or the like for a package) and many others. In example embodiments, the cognitive data pricing system 4112 may include folly cognitive, intelligent features, such as using genetic programming including automatically varying pricing and tracking feedback on outcomes. Outcomes on which tracking feedback may be based include various financial yield metrics, utilization metrics and the like that may be provided by calculating metrics in an analytic system 4018 on data from foe data transaction system 4114.

[0360] Methods and systems are disclosed herein for self-organizing data pools which may include self-organization of data pools based on utilization and / or yield metrics, including utilization and / or yield metrics that are tracked for a plurality' of data pools. The data pools may initially comprise unstructured or loosely structured pools of data that contain data from industrial environments, such as sensor data from or about industrial machines or components. For example, a data pool might take streams of data from various machines or components in an environment, such as turbines, compressors, batteries, reactors, engines, motors, vehicles, pumps, rotors, axles, bearings, valves, and many others, with foe data streams containing analog and / or digital sensor data (of a w'ide range of types), data published about operating conditions, diagnostic and fault data, identify ing data for machines or components, asset tracking data, and many other types of data. Each stream may have an identifier in foe pool, such as indicating its source, and optionally its type. The data pool may be accessed by external systems, such as through one or more interfaces or APIs (e.g., RESTfol APIs), or by data integration elements (such as gateways, brokers, bridges, connectors, or foe like), and the data pool may use similar capabilities to get access to available data streams. A data pool may be managed by a self-organizing machine learning facility, which may configure the data pool, such as by managing what sources are used for the pool, managing what streams are available, and managing APIs or other connections into and out of foe data pool. The self-organization may take feedback such as based on measures of success that may include measures of utilization and yield. The measures of utilization and yield that may include mayaccount for foe cost of acquiring and / or storing data, as well as foe benefits of foe pool, measured either by profit or by other measures that may include user indications of usefulness, and the like. For example, a self-organizing data pool might recognize that chemical and radiation data for an energy production environment are regularly accessed and extracted, while vibration and temperature data have not been used, in which case foe data pool might automatically reorganize, such as by ceasing storage of vibration and / or temperature data, or by obtaining better sources of such data. This automated reorganization can also apply to data structures, such as promoting different data types, different data sources, different data structures, and foe like, through progressive iteration and feedback.

[0361] In example embodiments, a platform is provided having self-organization of data pools based on utilization and / or yield metrics. In example embodiments, foe data pools 4020 may be68self-organizing data pools 4020, such as being organized by cognitive capabilities as described throughout this disclosure. The data pools 4020 may self-organize in response to learning feedback 4012, such as based on feedback of measures and results, including calculated in an analytic system 4018. Organization may include determining what data or packages of data to store in a pool (such as representing particular combinations, pennutations, aggregations, and the like), the structure of such data (such as in flat, hierarchical, linked, or other structures), the duration of storage, the nature of storage media (such as hard disks, flash memory, SSDs, network-based storage, or the like), the arrangement of storage bits, and other parameters. The content and nature of storage maybe varied, such that a data pool 4020 may learn and adapt, such as based on states of the host system 112, one or more data collection systems 102, storage environment parameters (such as capacity, cost, and performance factors), data collection environment parameters, marketplace parameters, and many others. In example embodiments, pools 4020 may learn and adapt, such as by variation of the above and other parameters in response to yield metrics (such as return on investment, optimization of power utilization, optimization of revenue, and the like).

[0362] Methods and systems are disclosed herein for training Al models based on industry-specific feedback, including training an Al model based on industry-specific feedback that reflects a measure of utilizati on, yield, or impact, and where the Al model operates on sensor data from an industrial environment. As noted above, these models may include operating models for industrial environments, machines, workflows, models for anticipating states, models for predicting fault and optimizing maintenance, models for self-organizing storage (on devices, in data pools and / or in the cloud), models for optimizing data transport (such as for optimizing network coding, network- condition-sensitive routing, and the like), models for optimizing data marketplaces, and many others.

[0363] In example embodiments, a platform is provided having training Al models based on industry-specific feedback. In example embodiments, the various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources 116 (such as relating to optimization of specific machines, devices, components, processes, and the like). Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features (such as for a marketplace 4102 or for other purposes of the host processing system 112) may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application invol ving loT devices (such as an industrial environment). This may include optimization of efficiency (such as in electrical, electromechanical, magnetic, physical, thermodynamic, chemical and other processes and systems), optimization of outputs (such as for production of energy, materials, products, services and other outputs), prediction, avoidance and mitigation of faults (such as in the aforementioned systems and processes), optimization of performance measures (such as returns on investment, yields, profits, margins, revenues and the like), reduction of costs (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, and many others), optimization of benefits (such as relating to safety, satisfaction.69health), optimization of work flows (such as optimizing time and resource allocation to processes), and others.

[0364] Methods and systems are disclosed herein for a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among them selves to optimize data collection based on the capabil ities and conditions of the members of the swarm. Each member of the swarm may be configured with intelligence, and the ability to coordinate with other members. For example, a member of the swarm may track information about what data other members are handling, so that data collection activities, data storage, data processing, and data publishing can be allocated intelligently across the swarm, taking into account conditions of the environment, capabilities of the members of the swarm, operating parameters, rules (such as from a rules engi ne that governs the operati on of th e swarm), an d current conditions of the members. For example, among four collectors, one that has relatively low current power levels (such as a low battery), might be temporarily allocated the role of publishing data, because it may receive a dose of power from a reader or interrogation device (such as an RFID reader) when it needs to publish the data. A second collector with good power levels and robust processing capability might be assigned more complex functions, such as processing data, fusing data, organizing the rest of the swarm (including self-organization under machine learning, such that the swarm is optimized over time, including by adjusting operating parameters, rules, and the like based on feedback), and the like. A third collector in the swarm with robust storage capabilities might be assigned the task of collecting and storing a category of data, such as vibration sensor data, that consumes considerable bandwidth. A fourth collector in the swarm, such as one with lower storage capabilities, might be assigned the role of collecting data that can usually be discarded, such as data on current diagnostic conditions, where only data on faults needs to be maintained and passed along. Members of a swarm may connect by peer-to-peer relationships by using a member as a “master” or “hub,” or by having them connect in a series or ring, where each member passes along data (including commands) to the next, and is aware of the nature of the capabilities and commands that are suitable for the preceding and / or next member. The swarm may be used for allocation of storage across it (such as using m emory' of each memory' as an aggregate data store. In these examples, the aggregate data store may support a distributed ledger, which may store transaction data, such as for transactions involving data collected by the swarm, transactions occurring in the industrial environment, or tire like. In example embodiments, the transaction data may also include data used to manage the swarm, the environment, or a machine or components thereof. The swarm may self-organize, either by machine learning capability disposed on one or more members of the swarm , or based on instructions from an external machine learning facil ity, which may optimize storage, data collection, data processing, data presentation, data transport, and other functions based on managing parameters that are relevant to each. The machine learning facility may start with an initial configuration and vary' parameters of the swarm relevant to any of the foregoing (also including varying the membership of the swarm), such as iterating based on feedback to the machine learning facility’ regarding measures of success (such as utilization measures, efficiency measures, measures of success in prediction or anticipation of states,70productivity measures, yield measures, profit measures, and others). Over time, the swarm may be optimized to a favorable configuration to achieve the desired measure of success for an owner, operator, or host of an industrial environment or a machine, component, or process thereof.

[0365] The swarm 4202 may be organized based on a hierarchical organization (such as where a m aster data collector 102 organizes and directs activities of one or more subservient data collectors 102), a collaborative organization (such as where decision-making for the organization of the swarm 4202 is distributed among the data collectors 102 (such as using various models for decision-making, such as voting systems, points systems, least-cost routing systems, prioritization systems, and the like), and the like.) In example embodiments, one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102. Data collection systems 102 may communicate with each other and with the host processing system 112, including sharing an aggregate allocated storage space involving storage on or accessible to one or more of the collectors (which in embodiment may be treated as a unified storage space even if physically distributed, such as using virtualization capabilities). Organization may be automated based on one or more rules, models, conditions, processes, or the like (such as embodied or executed by conditional logic), and organization may be governed by policies, such as handled by the policy engine. Rules may be based on industry, application- and domain-specific objects, classes, events, workflows, processes, and systems, such as by setting up the swarm 4202 to collect selected types of data at designated places and times, such as coordinated with the foregoing. For example, the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and / or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines. In example embodiments, self-organization may be cognitive, such as where the swarm varies one or more collection parameters and adapts the selection of parameters, weights applied to the parameters, or the like, overtime. In examples, this may be in response to learning and feedback, such as from the learning feedback system 4012 that may be based on various feedback measures that may be determined by applying the analytic system 4018 (which in embodiments may reside on the swarm 4202, the host processing system 112, or a combination thereof) to data handled by the swarm 4202 or to other elements of the various embodiments disclosed herein (including marketplace elements and others). Thus, the swarm 4202 may display adaptive behavior, such as adapting to the current state 4020 or an anticipated state of its environment (accounting for marketplace behavior), behavior of various objects (such as loT devices, machines, components, and systems), processes (including events, states, workflows, and the like), and other factors at a given time. Parameters that may be varied in a process of variation (such as in a neural net, selforganizing map, or the like), selection, promotion, or the like (such as those enabled by genetic programming or other Al-based techniques). Parameters that may be managed, varied, selected and adapted by cognitive, machine learning may include storage parameters (location, type, duration, amount, structure and the like across the swarm 4202), network parameters (such as how71the swarm 4202 is organized, such as in mesh, peer-to-peer, ring, serial, hierarchical and other network configurations as well as bandwidth utilization, data routing, network protocol selection, network coding type, and other networking parameters), security parameters (such as settings for various security’ applications and sendees), location and positioning parameters (such as routing m ovement of mobile data collectors 102 to locations, positi oning and orienting collectors 102 and the like relative to points of data acquisition, relative to each other, an d relative to locations where network availability may be favorable, among others), input selection parameters (such as input selection among sensors, input sources 116 and the like for each collector 102 and for the aggregate collection), data combination parameters (such as those for sensor fusion, input combination, multiplexing, mixing, layering, convolution, and ether combinations), power parameters (such as parameters based on power levels and power availability for one or more collectors 102 or other objects, devices, or the like), states (including anticipated states and conditions of the swarm 4202, individual collection systems 102, the host processing system 112 or one or more objects in an environment), events, and many others. Feedback may be based on any of the kinds of feedback described herein, such that over time the swarm may adapt to its current and anticipated situation to achieve a wide range of desired objectives.

[0366] Methods and systems are disclosed herein for an industrial loT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial loT data. A distributed ledger may distribute storage across devices, using a secure protocol, such as those used for cryptocurrencies (such as the Blockchain™ protocol used to support the Bitcoin™ currency). A ledger or similar transaction record, which may comprise a structure where each successive member of a chain stores data for previous transactions, and a competition can be established to determine which of alternative data stored data structures is “best” (such as being most complete), can be stored across data collectors, industrial machines or components, data pools, data marketplaces, cloud computing elements, servers, and / or on the IT infrastructure of an enterprise (such as an owner, operator or host of an industrial environment or of the systems disclosed herein). The ledger or transaction may be optimized by machine learing, such as to provide storage efficiency, security, redundancy, or the like.

[0367] In example embodiments, the cognitive data marketplace 4102 may use a secure architecture for tracking and resolving transactions, such as a distributed ledger 4004, wherein transactions in data packages are tracked in a chained, distributed data structure, such as a Blockchain ™, allowing forensic analysis and validation where individual devices store a portion of the ledger representing transactions in data packages. The distributed ledger 4004 may be distributed to loT devices, to data pools 4020, to data collection systems 102, and the like, so that transaction information can be verified without reliance on a single, central repository of information. The transaction system 4114 may be configured to store data in the distributed ledger 4004 and to retrieve data from it (and from constituent devices) in order to resolve transactions. Thus, a distributed ledger 4004 for handling transactions in data, such as for packages of loT data, is provided. In example embodiments, the self-organizing storage system 4028 may be used for72optimizing storage of distributed ledger data, as well as for organizing storage of packages of data, such as loT data, that can be presented in the marketplace 4102.

[0368] Methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing and / or other network conditions. Network sensitivity can include awareness of the price of data transport (such as allowing the system to pull or push data during off-peak periods or within the available parameters of paid data plans), the quality of the network (such as to avoid periods where errors are likely ), the quality of environmental conditions (such as delaying transmission until signal quality is good, such as when a collector emerges from a shielded environment, avoiding wasting use of power when seeking a signal when shielded, such as by large metal structures typically of industrial environments), and the like.

[0369] Methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and / or conditions identified in an industrial data collection environment. For example, interfaces can recognize what sensors are available and interfaces and / or processors can be turned on to take input from such sensors, including hardware interfaces that allow the sensors to plug in to the data collector, wireless data interfaces (such as where the collector can ping the sensor, optionally providing some power via an interrogation signal), and software interfaces (such as for handling particular types of data). Thus, a collector that is capable of handling various kinds of data can be configured to adapt to the particular use in a given environment. In example embodiments, configuration may be automatic or under machine learning, which may improve configuration by optimizing parameters based on feedback measures over time.

[0370] Methods and systems are disclosed herein for self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data. Self-organizing storage may allocate storage based on application of machine learning, which may improve storage configuration based on feedback measure over time. Storage may be optimized by configuring what data types are used (e.g., byte-like structures, structures representing fused data from multiple sensors, structures representing statistics or measures calculated by applying mathematical functions on data, and the like), by configuring compression, by configuring data storage duration, by configuring write strategies (such as by striping data across multiple storage devices, using protocols where one device stores instructions for other devices in a chain, and the like), and by configuring storage hierarchies, such as by providing precalculated intermediate statistics to facilitate more rapid access to frequently accessed data items. Thus, highly intelligent storage systems may be configured and optimized, based on feedback, over time.

[0371] Methods and systems are disclosed herein for self-organizing network coding for a multisensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment. Network coding, including random linear network coding, can enable highly efficient and reliable transport of large amounts of data over various kinds of networks. Different network coding configurations can be selected,73based on machine learning, to optimize network coding and other network transport characteristics based on network conditions, environmental conditions, and other factors, such as the nature of the data being transported, environmental conditions, operating conditions, and the like (including bytraining a network coding selection model over time based on feedback of measures of success, such as any of the measures described herein).

[0372] In example embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network. A cognitive system may vary one or more parameters for networking, such as network type selection (e.g., selecting among available local, cellular, satellite, Wi-Fi, BLUETOOTH™, NFC, ZIGBEE® and other networks), network selection (such as selecting a specific network, such as one that is known to have desired security features), network coding selection (such as selecting a type of network coding for efficient transport[such as random linear network coding, fixed coding, and others]), network timing selection (such as configuring delivery based on network pricing conditions, traffic and the like), network feature selection (such as selecting cognitive features, security features, and the like), network conditions (such as network quality based on current environmental or operation conditions), network feature selection (such as enabling available authentication, permission and similar systems), network protocol selection (such as among HTTP, IP, TCP / IP, cellular, satellite, serial, packet, streaming, and many other protocols), and others. Given bandwidth constraints, price variations, sensitivity to environmental factors, security concerns, and the like, selecting the optimal network configuration can be highly complex and situation dependent. The self-organizing networking system 4030 may vary combinations and permutations of these parameters while taking input from a learning feedback system 4012 such as using information from the analytic system 4018 about various measures of outcomes. In the many examples, outcomes may include overall system measures, analytic success measures, and local performance indicators. In example embodiments, input from a learning feedback system 4012 may include information from various sensors and input sources 1 16, information from the state system 4020 about states (such as events, environmental conditions, operating conditions, and many others, or other information) or taking other inputs. By variation and selection of alternative configurations of networking parameters in different states, the selforganizing networking system may find configurations that are well-adapted to the environment that is being monitored or controlled by the host system 112, such as the instance where one or more data collection systems 102 are located and that are well-adapted to emerging network conditions. Thus, a self-organizing, network-condition-adaptive data collection system is provided.

[0373] Referring to Figure 32, a data collection system 102 may have one or more output interfaces and / or ports 4010. These may include network ports and connections, application programming interlaces, and the like. Methods and systems are disclosed herein for a haptic or multi-sensory user interface, including a wearable haptic or multi -sensory user interface for an industrial sensor data collector, with vibration, heat, electrical, and / or sound outputs. For example, an interface may, based on a data structure configured to support the interface, be set up to provide a user with input or feedback, such as based on data from sensors in the environment. For example, if a fault condition based on a vibration data (such as resulting from a bearing being worn down, an axle74being misaligned, or a resonance condition between machines) is detected, it can be presented in a haptic interface by vibration of an interface, such as shaking a wrist-worn device. Similarly, thermal data indicating overheating could be presented by warming or cooling a wearable device, such as while a worker is working on a machine and cannot necessarily look at a user interface. Similarly, electrical or magnetic data may be presented by a buzzing, and the like, such as to indicate presence of an open electrical connection or wire, etc. That is, a multi-sensory' interface can intuitively help a user (such as a user with a wearable device) get a quick indication of what is going on in an environment, with the wearable interface having various modes of interaction that do not require a user to have eyes on a graphical UI, which may be difficult or impossible in many industrial environments where a user needs to keep an eye on the environment.

[0374] In example embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and / or sound outputs. In example embodiments, a haptic user interface 4302 is provided as an output for a data collection system 102, such as a system for handling and providing information for vibration, heat electrical, and / or sound outputs, such as to one or more components of the data collection system 102 or to another system, such as a wearable device, mobile phone, or the like. A data collection system 102 may be provided in a form factor sui table for delivering haptic input to a user, such as vibration, warming or cooling, buzzing, or the like, such as input disposed in headgear, an armband, a wristband or watch, a belt, an item of clothing, a uniform, or the like. In such cases, data collection systems 102 may be integrated with gear, uniforms, equipment, or the like worn by users, such as individuals responsible for operating or monitoring an industrial environment. In example embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may trigger haptic feedback. For example, if a nearby industrial machine is overheating, the haptic interface may alert a user by wanning up, or by sending a signal to another device (such as a mobile phone) to warm up. If a system is experiencing unusual vibrations, the haptic interface may vibrate. Thus, through various forms of haptic input, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as those in an industrial environment) without requiring them to read messages or divert their visual attention away from the task at hand. The haptic interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the haptic system 4202. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed), hi example embodiments, a cognitive haptic system may be provided, where selection of inputs or triggers for haptic feedback, selection of outputs, timing, intensity levels, durations, and other parameters (or weights applied to them) may be varied in a process of variation, promotion, and selection (such as using genetic programming)75with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive haptic interface for a data collection system 102 is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.

[0375] Methods and systems are disclosed herein for a presentation layer for AR / VR industrial glasses, where heat map elements are presented based on patterns and / or parameters in collected data. Methods and systems are disclosed herein for condition-sensitive, self-organized tuning of AR / VR interfaces based on feedback metrics and / or training in industrial environments. In example embodiments, any of the data, measures, and the like described throughout this disclosure can be presented by visual elements, overlays, and the like for presentation in the AR / VR interlaces, such as in industrial glasses, on AR / VR interfaces on smart phones or tablets, on AR / VR interfaces on data collectors (which may be embodied in smart phones or tablets), on displays located on machines or components, and / or on displays located in industrial environments.

[0376] In example embodiments, a platform is provided having heat maps displaying collected data for AR / VR. In example embodiments, a platform is provided having heat maps 4204 displaying collected data from a data collection system 102 for providing input to an AR / VR interface 4208. In example embodiments, the heat map interface 4304 is provided as an output for a data collection system 102, such as for handling and providing information for visualization of various sensor data and other data (such as map data, analog sensor data, and other data), such as to one or more components of the data collection system 102 or to another system, such as a mobile device, tablet, dashboard, computer, AR / VR device, or the like. A data collection system 102 may be provided in a form factor suitable for delivering visual input to a user, such as the presentation of a map that includes indicators of levels of analog and digital sensor data (such as data indicating levels of rotation, vibration, heating or cooling, pressure, and many other conditions). In such cases, data collection systems 102 may be integrated with equipment, or the like, that are used byindividuals responsible for operating or monitoring an industrial environment. In example embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to a heat map. Coordinates may include real world location coordinates (such as geo-location or location on a map of an environment), as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for representation of analog sensor signals, digital signals, input source information, and various combinations, in a map-based visualization, such that colors may represent varying levels of input along the relevant dimensions. For example, if a nearby industrial machine is overheating, the heat map interface may alert a user by showing a machine in bright red. If a system is experiencing unusual vibrations, the heat map interface may show a different color for a visual element for the machine, or it may cause an icon or display element representing the machine to vibrate in the interface, calling attention to the element. Clicking, touching, or otherwise interacting with the map can allow a user to drill down and see underlying sensor or input data that is used as76an input to the heat map display. Thus, through various forms of display, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors, such as those in an industrial environment, without requiring them to read text-based messages or input. The heat map interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the heat map U14304. This may include rule-based or model-based feedback (such as feedback providing outputs that correspond in some logical fashion to the source data that is being conveyed). In example embodiments, a cognitive heat map system may be provided, where selection of inputs or triggers for heat map displays, selection of outputs, colors, visual representation elements, timing, intensity levels, durations and other parameters (or weights applied to them) may be varied in a process of variation, promotion and selection (such as selection using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive heat map interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 112, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.

[0377] In example embodiments, a platform is provided having automatically timed AR / VR visualization of data collected by a data collector. In example embodiments, a platform is provided having an automatically timed AR / VR visualization system 4308 for visualization of data collected by a data collection system 102, such as the case where the data collection system 102 has an AR / VR interface 4208 or provides input to an AR / VR interface 4308 (such as a mobile phone positioned in a virtual reality or AR headset, a set of AR glasses, or the like). In example embodiments, the AR / VR system 4308 is provided as an output interface of a data collection system 102, such as a system for handling and providing information for visualization of various sensor data and other data (such as map data, analog sensor data, and other data), such as to one or more components of the data collection system 102 or to another system, such as a mobile device, tablet, dashboard, computer, AR / VR device, or the like. A data collection system 102 may be provided in a form factor suitable for delivering AR or VR visual, auditory, or other sensory input to a user, such as by presenting one or more displays such as 3D-realistic visualizations, objects, maps, camera overlays, or other overlay elements, maps and the like that include or correspond to indicators of levels of analog and digital sensor data (such as data indicating levels of rotation, vibration, heating or cooling, pressure and many other conditions, to input sources 1 16, or the like) . In such cases, data collection systems 102 may be integrated with equipment, or the like, that are used by individuals responsible for operating or monitoring an industrial environment.

[0378] In example embodiments, signals from various sensors or input sources (or selective combinations, perm utations, mixes, and the like as managed by one or more of the cognitive input77selection systems 4004, 4014) may provide input data to populate, configure, modify, or otherwise determine the AR / VR element. Visual elements may include a wide range of icons, map elements, menu elements, sliders, toggles, colors, shapes, sizes, and the like, for representation of analog sensor signals, digital signals, input source information, and various combinations. In many examples, colors, shapes, and sizes of visual overlay elements may represent varying levels of input along the relevant dimensions for a sensor or combination of sensors. In further examples, if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display of a pair of AR glasses. If a system is experiencing unusual vibrations, a virtual reality interface showing visualization of the components of the machine (such as an overlay of a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion, or the like, to ensure the component stands out in a virtual reality environment being used to help a user monitor or sendee the machine. Clicking, touching, moving eyes toward, or otherwise interacting with a visual element in an AR / VR interface may allow a user to drilldown and see underly ing sensor or input data that is used as an input to the display. Thus, through various forms of display, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as in an industrial environment), without requiring them to read text-based messages or input or divert attention from the applicable environment (whether it is a real environment with AR features or a virtual environment, such as for simulation, training, or the like).

[0379] The AR / VR output interface 4208, and selection and configuration of what outputs or displays should be provided, may be handled in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that AR / VR display signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the AR / VR UI 4308. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In example embodiments, a cognitively tuned AR / VR interface control system 4308 may be provided, where selection of inputs or triggers for AR / VR display elements, selection of outputs (such as colors, visual representation elements, timing, intensity levels, durations and other parameters [or weights applied to them]) and other parameters of a VR / AR environment may be varied in a process of variation, promotion and selection (such as the use of genetic programming) with feedback based on real world responses in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive, tuned AR / VR interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 1 12, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.78

[0380] As noted above, methods and systems are disclosed herein for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. Embodiments include using continuous ultrasonic monitoring of an industrial environment as a source for a cloud-deployed pattern recognizer. Embodiments include using continuous ultrasonic monitoring to provide updated state information to a state machine that is used as an input to a cloud-deployed pattern recognizer. Embodiments include making available continuous ultrasonic monitoring information to a user based on a policy declared in a policy engine. Embodiments include storing continuous ultrasonic monitoring data with other data in a fused data structure on an industrial sensor device. Embodiments include making a stream of continuous ultrasonic monitoring data from an industrial environment available as a service from a data marketplace. Embodiments include feeding a stream of continuous ultrasonic monitoring data into a self-organizing data pool. Embodiments include training a machine learning model to monitor a continuous ultrasonic monitoring data stream where the model is based on a training set created from human analysis of such a data stream, and is improved based on data collected on performance in an industrial environment.

[0381] Embodiments include a swarm of data collectors that include at least one data collector for continuous ultrasonic monitoring of an industrial environment and at least one other type of data collector. Embodiments include using a distributed ledger to store time-series data from continuous ultrasonic monitoring across multiple devices. Embodiments include collecting a stream of continuous ultrasonic data in a self-organizing data collector, a network-sensitive data collector, a remotely organized data collector, a data collector having self-organized storage and the like. Embodiments include using self-organizing network coding to transport a stream of ultrasonic data collected from an industrial environment. Embodiments include conveying an indicator of a parameter of a continuously collected ultrasonic data stream via an interface where the interface is one of a sensory' interface of a wearable device, a heat map visual interface of a wearable device, an interface that operates with self-organized tuning of the interface layer, and the like.

[0382] As noted above, methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote analog industrial sensors. Embodiments include taking input from a plurality of analog sensors disposed in an industrial environment, multiplexing the sensors into a multiplexed data stream, feeding the data stream into a cloud-deployed machine learning facility, and training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment. Embodiments include deploying policies by a policy' engine that govern what data can be used by what users and for what purpose in cloud-based, machine learning. Embodiments include using a cloud-based platform to identify patterns in data across a plurality of data pools that contain data published from industrial sensors. Embodiments include training a model to identify preferred sensor sets to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment.79

[0383] Embodiments include a swarm of data collectors that is governed by a policy that is automatically propagated through the swarm. Embodiments include using a distributed ledger to store sensor fusion information across multiple devices. Embodiments include feeding input from a set of data collectors in to a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment. The data collectors may be self-organizing data collectors, networksensitive data collectors, remotely organized data collectors, a set of data collectors having selforganized storage, and the like. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport of data fused from multiple sensors in the environment. Embodiments include conveying information formed by fusing inputs from multiple sensors in an industrial data collection system in an interface such as a multi-sensory interface, a heat map interface, an interface that operates with self-organized tuning of the interface layer, and the like.

[0384] As noted above, methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. Embodiments include using a policy engine to determine what state information can be used for cloud-based machine analysis. Embodiments include feeding inputs from multiple devices that have fused and on-device storage of multiple sensor streams into a cloud-based pattern recognizer to determine an anticipated state of an industrial environment. Embodiments include making an output, such as anticipated state information, from a cloud-based machine pattern recognizer that analyzes fused data from remote, analog industrial sensors available as a data sendee in a data marketplace. Embodiments include using a cloud-based pattern recognizer to determine an anticipated state of an industrial environment based on data collected from data pools that contain streams of information from machines in the en vironment. Embodiments include training a model to identify preferred state information to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment. Embodiments include a swarm of data collectors that feeds a state machine that maintains current state information for an industrial environment. Embodiments include using a distributed ledger to store historical state information for fused sensor states a self-organizing data collector that feeds a state machine that maintains current state information for an industrial environment. Embodiments include a data collector that feeds a state machine that maintains current state information for an industrial environment where the data collector may be a network sensitive data collector, a remotely organized data collector, a data collector with self-organized storage, and the like. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport and maintains anticipated state information for the environment. Embodiments include conveying anticipated state information determined by machine learning in an industrial data collection system in an interface where the interface may be one or more of a multisensory interface, a heat map interface an interface that operates with self-organized tuning of the interface layer, and the like.80

[0385] As noted above, methods and systems are disclosed herein for a cloud-based policy automation engine for loT, with creation, deployment, and management of loT devices, including a cloud-based policy automation engine for loT, enabling creation, deployment and management of policies that apply to loT devices. Policies can relate to data usage to an on-device storage system that stores fused data from multiple industrial sensors, or what data can be provided to whom in a self-organizing marketplace for loT sensor data. Policies can govern how a selforganizing swarm or data collector should be organized for a particular industrial environment, how a network-sensitive data collector should use network bandwidth for a particular industrial environment, how a remotely organized data collector should collect, and make available, data relating to a specified industrial environment, or how a data collector should self-organize storage for a particular industrial environment. Policies can be deployed across a set of self-organizing pools of data that contain data streamed from industrial sensing devices to govern use of data from the pools or stored on a device that governs use of storage capabilities of the device for a distributed ledger. Embodiments include training a model to determine what policies should be deployed in an industrial data collection system. Embodiments include a system for data collection in an industrial environment with a policy engine for deploying policy within the system and, optionally, self-organizing network coding for data transport. In example embodiments, a policy applies to how data will be presented in a multi-sensory' interface, a heat map visual interface, or in an interface that operates with self-organized tuning of the interface layer.

[0386] As noted above, methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial loT devices, such as an industrial data collector, including selforganizing, remotely organized, or network-sensitive industrial data collectors, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. Embodiments include a self-organizing marketplace that presents fused sensor data that is extracted from on- device storage of loT devices. Embodiments include streaming fused sensor information from multiple industrial sensors and from an on-device data storage facility to a data pool. Embodiments include training a model to determine what data should be stored on a device in a data collection environment. Embodiments include a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection, where at least some of the data collectors have on-device storage of fused data from multiple sensors. Embodiments include storing distributed ledger information with fused sensor information on an industrial loT device. Embodiments include a system for data collection with on-device sensor fusion, such as of industrial sensor data and, optionally, self-organizing network coding for data transport, where data structures are stored to support alternative, multi-sensory' modes of presentation, visual heat map modes of presentation, and / or an interface that operates with self-organized tuning of the interface layer.

[0387] As noted above, methods and systems are disclosed herein for a self-organizing data marketplace for industrial loT data, where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. Embodiments include organizing a set of data81pools in a self-organizing data marketplace based on utilization metrics for the data pools. Embodiments include training a model to determine pricing for data in a data marketplace. The data marketplace is fed with data streams from a self-organizing swarm of industrial data collectors, a set of industrial data collectors that have self-organizing storage, or self-organizing, network-sensitive, or remotely organized industrial data collectors. Embodiments include using a distributed ledger to store transactional data for a self-organizing marketplace for industrial loT data. Embodiments include using self-organizing network coding for data transport to a marketplace for sensor data collected in industrial environments. Embodiments include providing a library of data structures suitable for presenting data in alternative, multi-sensory interface modes in a data marketplace, in heat map visualization, and / or in interfaces that operate with selforganized tuning of the interface layer.

[0388] As noted above, methods and systems are disclosed herein for self-organizing data pools such as those that self-organize based on utilization and / or yield metrics that may be tracked for a plurality of data pools. In example embodiments, the pools contain data from self-organizing data collectors. Embodiments include training a model to present the most valuable data in a data marketplace, where training is based on industry-specific measures of success. Embodiments include populating a set of self-organizing data pools with data from a self-organizing swarm of data collectors. Embodiments include using a distributed ledger to store transactional information for data that is deployed in data pools, where the distributed ledger is distributed across the data pools. Embodiments include populating a set of self-organizing data pools with data from a set of network-sensitive or remotely organized data collectors or a set of data collectors having selforganizing storage. Embodiments include a system for data collection in an industrial environment with self-organizing pools for data storage and self-organizing network coding for data transport, such as a system that includes a source data structure for supporting data presentation in a multi- sensory interface, in a heat map interface, and / or in an interface that operates with self-organized tuning of the interface layer.

[0389] As noted above, methods and systems are disclosed herein fortraining Al models based on industry-specific feedback, such as that reflects a measure of utilization, yield, or impact, where the Al model operates on sensor data from an industrial environment. Embodiments include training a swarm of data collectors, or data collectors, such as remotely organized, self-organizing, or network-sensitive data collectors, based on industry-specific feedback or network and industrial conditions in an industrial environment, such as to configure storage. Embodiments include training an Al model to identify and use available storage locations in an industrial environment for storing distributed ledger information. Embodiments include training a remote organizer for a remotely organized data collector based on industry-specific feedback measures. Embodiments include a system for data collection in an industrial environment with cloud-based training of a network coding model for organizing network coding for data transport or a facility that manages presentation of data in a multi-sensory interface, in a heat map interface, and / or in an interface that operates with self-organized tuning of the interface layer.82

[0390] As noted above, methods and systems are disclosed herein for a self-organized swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm. Embodiments include deploying distributed ledger data structures across a swarm of data. Data collectors may be network-sensitive data collectors configured for remote organization or have self-organizing storage. Systems for data collection in an industrial environment with a swarm can include a self-organizing network coding for data transport. Systems include swamis that relay information for use in a multi-sensory interface, in a heat map interface, and / or in an interface that operates with self-organized tuning of the interface layer.

[0391] As noted above, methods and systems are disclosed herein for an industrial loT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial loT data. Embodiments include a self-organizing data collector that is configured to distribute collected information to a distributed ledger. Embodiments include a network-sensitive data collector that is configured to distribute collected information to a distributed ledger based on network conditions. Embodiments include a remotely organized data collector that is configured to distribute collected information to a distributed ledger based on intelligent, remote management of the distribution. Embodiments include a data collector with selforganizing local storage that is configured to distribute collected information to a distributed ledger. Embodiments include a system for data collection in an industrial environment using a distributed ledger for data storage and self-organizing network coding for data transport, wherein data storage is of a data structure supporting a haptic interface for data presentation, a heat map interface for data presentation, and / or an interface that operates with self-organized tuning of the interlace layer.

[0392] As noted above, methods and systems are disclosed herein for a self-organizing collector, including a self-organizing, multi-sensor data collector that can optimize data collection, power and / or yield based on conditions in its environment, and is optionally responsive to remote organization. Embodiments include a self-organizing data collector that organizes at least in part based on network conditions. Embodiments include a self-organizing data collector with selforganizing storage for data collected in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with self-organizing data collection and self-organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment with a self-organizing data collector that feeds a data structure supporting a haptic or multi -sensory’ wearable interface for data presentation, a heat map interface for data presentation, and / or an interface that operates with self-organized tuning of the interface layer.

[0393] As noted above, methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing, and / or other network conditions. Embodiments include a remotely organized, network condition-sensitive universal data collector that can power up and down sensor interfaces based on need and / or conditions identified in an83industrial data collection environment, including network conditions. Embodiments include a network-condition sensitive data collector with self-organizing storage for data collected in an industrial data collection environment. Embodiments include a network-condition sensitive data collector wi th self-organizing network coding for data transport in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with a network-sensitive data collector that rel ays a data structure supporting a haptic wearable interface for data presentation, a heat map interface for data pr...

Claims

CLAIMSWhat is claimed is:

1. An industrial technology stack for an industrial environment, the industrial technology stack comprising: a set of computational resources; and a set of layers executed by the set of computational resources, the set of layers including, a governance layer, an enterprise layer, an offering layer, a transaction layer, an operations layer, a network layer, a data layer, and a resource layer.

2. The industrial technology stack of claim 1, wherein the industrial technology stack performs at least one of, enabling devices of the industrial environment to cooperatively and interoperatively discover and assign resources, orchestrating an application of computational resources to industrial resources and uses, monitoring and manage industrial processes, or maintaining at least one resource of the industrial environment.

3. The industrial technology stack of claim 1, wherein the governance layer monitors resources of the industrial environment to ensure compliance with governance policies of the industrial environment, and the governance policies include at least one of, conserving consumption of resources of the industrial environment, improving efficiency of industrial processes of the industri al environment , promoting productivity of employees of the industrial environment, reducing greenhouse gas emissions by the industrial environment, or ensuring compliance with at least one law, regulation, moral principle, ethical obligation, or organizational objective.

4. The industrial technology stack of claim 1, wherein the governance layer includes at least one of, artificial intelligence models for embedded policies and governance, policy automation, safety and compliance automation, reporting automation, or automated governance of the industrial environment.

5. The industrial technology stack of claim 1, wherein the enterprise layer performs at least one of.1115allocating resources of an industrial enterprise to industrial processes and / or equipment of the industrial environment, or selecting, analyzing, and / or adapting an organizational structure of the industrial environment to entities within the industrial environment.

6. The industrial technology stack of claim 1, wherein the enterpri se layer includes at least one, artificial intelligence models for contextual simulation and forecasting, executive digital twins,IIoT platforms, big data and analytics, or advanced operational control over the industrial environment.

7. The industrial technology stack of claim 1, wherein the offering layer performs at least. one of, selecting and / or describing external contracts or opportunities for participation by the industrial environment, formulating, analyzing, and / or releasing external offerings for participation of other enterprises, or selecting, describing, formulating, analyzing, and / or releasing internal offerings among at least, two entities of the industrial environment.

8. The industrial technology stack of claim 1, wherein the offering layer includes at least one of, expert systems, generative Al, industrial metaverse simulation of the industrial environment, system s integration of the industrial environmen t, industrial control systems of the industrial environment, or industrial automation of the industrial environment.

9. The industrial technology stack of claim 1, wherein the transaction layer formulates, negotiates, executes, fulfills, and / or reports transactions related to one or more offerings of the offering layer, and the transactions include at least one of, a purchase of one or more assets for the industrial environment, a sale of one or more assets of the industrial environment, a mortgage, lease, and / or loan of one or more resources of the industrial environment, or a conveyance, transport, and / or delivery' of a physical or digital asset of the industrial environment.

10. The industrial technology stack of claim 1, wherein the transaction layer includes at least one of, artificial intelligence models for discovery, generation, and optimization of transactions, smart contracts and distributed ledgers, a transaction -related system-of-systems,1116automated transaction orchestration, or an automated industrial marketplace.

11. The industrial technology stack of claim 1 , wherein the operations layer performs at least one of, executing steps of an industrial manufacturing, refining, materials processing, inspection, assembly, and / or delivery process, monitoring a set of resources and / or operations of the industrial environment, assessing a set of resources and / or operations of the industrial environment, diagnosing a set of resources and / or operations of the industrial environment, maintaining a set of resources and / or operations of the industrial environment, repairing a set of resources and / or operations of the industrial environment, cleaning a set of resources and / or operation s of the industrial environm ent, generating a set of resources of the industrial environment, organizing a set of resources of the industrial environment, interconnecting a set of resources of the industrial en vironment, or maintaining a set of resources of the industrial environment.

12. The industrial technology stack of claim 1 , wherein the operations layer includes at least one of, artificial intelligence models for routing, control, optimization, and / or generation of resources of the industrial environment, automated monitoring and maintenance platforms, asset and infrastructure management, robotics and process automation, or converged. Al-based IT / OT workflow orchestration.

13. The industrial technology stack of claim 1, wherein the network layer provides connectivity' and / or communicating among a set of components of the industrial environment, wherein the set of components of the industrial environment includes at least one of, an industrial machine, a computer, a server, a workstation, an industrial sensor, a personal communication device, or a software component of the industrial technology stack.

14. The industrial technology stack of claim 1, wherein the network layer includes at least one of, artificial intelligence models for adaptive networking, edge and / or cloud communication, cellular, Wi-Fi, ORAN, and / or Bluetooth communication protocols. Industrial Tntemet-of-Things (IIoT)-related communication protocols, or1117intelligence edge, advanced networking, and / or IIoT-in-a-Box architectures.

15. The industrial technology stack of claim 1, wherein the data layer performs at least one of, providing data storage capabilities of the industrial environment, m anaging data storage capabil ities of the industrial environment, storing data of the industrial environment, curating data of the industrial environment, indexing data of the industrial environment, searching data of the industrial environment, querying data of the industrial environment, structuring data of the industrial environment, providing data of the industrial environment, refreshing data of the industrial environment, compiling data of the industrial environment, transmitting data of the industrial environment, altering data of the industrial environment, monitoring data of the industrial environment, or deleting data of the industrial environment.

16. The industrial technology stack of claim 1, wherein the data layer includes at least one of, artificial intelligence models for sensor fusion, sensor and / or wearable devices of the industrial environment, mobile data collectors of the industrial environment, application programming interfaces of the industrial environment, or context-aware industrial loT and sensor fusion.

17. The industrial technology stack of claim 1, wherein the resource layer performs at least one of, acquiring a resource of the industrial environment, m anaging a resource of th e industrial environment, monitoring a resource of the industrial environment, controlling a resource of the industrial environment, transporting a resource of the industrial environment, or maintaining a resource of the industrial environment.

18. The industrial technology stack of claim 1 , wherein the resource layer includes at least one of, artificial intelligence models for resource optimization, advanced computation, switches and / or multiplexers, vibration or other sensor data collection, or machine-level data collection and resource optimization.

19. A system for visual search in a digital twin environment, comprising:1118a visual search engine embedded within a digital twin; an interface configured to receive an image-based query; and one or more processors configured to: process the image-based query’ and data structures supporting the digital twin to identify matching elements in the digital twin, present the matching elements in a visual representation of the digital twin, and provide additional data associated with the matching elements.

20. The system of claim 19, wherein the image-based query comprises at least one of: a pasted image, image metadata, a user-created drawing, or a set. of keywords describing an image.

21. The system of claim 19, wherein presenting the matching elements comprises at least one of: listing the matching elements in a menu bar, or highlighting the matching elements in the visual representation through changes in font, color, or brightness.

22. The system of claim 19, wherein the processors are further configured to utilize artificial intelligence to perform at least one of, parsing queries, recognizing visual elements, classifying visual elements, matching images with image content, correlating images with image content, ranking results based on likelihood of relevance, or optimizing a presentation of results.

23. A system for automatically constructing a digital twin environment, comprising one or more processors configured to: receive an arbitrary set of photographs taken over time, utilize artificial intelligence systems to link common elements within the photographs, manipulate image elements to provide a stitched view of the digital twin environment, and add information to the digital twin from other data sources.

24. The system of claim 23, further comprising a blockchain configured to, capture image information and other information about the environment, support a distributed ledger of transactions related to the environment, provide interfaces for controlling access to the information, and enable private and public portions of the information.

25. A system for managing decision-making in a digital twin environment, comprising: a digital twin platfonn configured to display states and activities of entities; and1119one or more processors configured to: simulate, based on data for the entities, states and activities processed by the digital twin, a set of outcomes predicted to result upon deployment of a set of human and artificial intelligence systems in decision making workflows; provi de a planning system for deployment of artifici al intelligence systems and required resources; and predict outcomes of deployment of multiple distinctive sets of artificial intelligence systems and resource configurations.

26. The system of claim 25, wherein the processors are further configured to display training metrics for at least one of, humans available to perform decision making tasks, hybrid human-AI systems available to perform decision making tasks, or Al systems available to perform decision making tasks.

27. The system of claim 25, wherein the processors are further configured to display performance metrics for at least one of, humans available to perform decision making tasks, hybrid human-AI systems available to perform decision making tasks, or Al systems available to perform decision making tasks.

28. The system of claim 25, wherein the processors are further configured to displaycomparative performance metrics among at least one of, individual humans, human-led enterprises, crowds of humans, human-AI combination systems, orAl systems.

29. The system of claim 25, further comprising an intelligent agent configured to: automatically discover available systems among human systems, combined human-AI systems and standalone artificial intelligence systems capable of performing desired functions, and recommend deployment configuration parameters based on situational factors.

30. The system of claim 25, wherein the situational factors comprise at least one of, availability factors for human and artificial intelligence resources, capability factors for human and artificial intelligence resources, resource availability factors for enabling human and artificial intelligence resources, or contextual and environmental factors.

31. The system of claim 25, further comprising an interface system configured to, designate users as trainers for creation of artificial intelligence systems represented in the digital twin, and designate users as supervisors for artificial intelligence systems represented in the digital twin.112032. The system of claim 25, wherein the processors are further configured to distribute decision making authority based on a framework including at least one of, a hierarchical framework, a rules-based framework, a simulation framework, an enterprise planning framework, an algorithmic framework, a principles-based framework, a collaborative framework, a peer-to-peer framework, or a competitive framework.

33. The system of claim 25, further comprising a smart, contract system embedded in the digital twin configured to, set terms and conditions for liability resulting from system outputs, and process input data to produce outputs reflecting transactional terms.

34. The system of claim 25, wherein the processors are further configured to enable at least one of. enabling incremental change, enabling experimentation, enabling reversible implementation, enabling gracefill migration among humans, human-AI mixes and autonomous Al systems, or enabling rapid adjustment based on performance capabilities, contextual factors and environmental factors.

35. A system for managing data in a digital twin environment, comprising: a pipeline of loT and edge devices; high QoS networks configured for adaptive networking; and one or more processors configured to, collect granular, real-time sensor data about entities in an operating environment, process the sensor data for big data analytics, and process the sensor data for machine learning.

36. The system of claim 35, further comprising data architectures configured to perform at least one of, adapting visual representations for human cognitive processing based on user parameters, or preparing and stage data for artificial intelligence systems through at least one of, data cleansing, deduplication, generation of synthetic data, entity resolution.1121normalization, or generation of embeddings.

37. The system of claim 35, further comprising computational architectures configured to adapt processing using a mix of: cloud processing systems; edge processing system s; on-device processing systems; and quantum computing systems.

38. A system for managing artificial intelligence deployment, comprising: one or more processors configured to: evaluate trust factors including: stakes involved; training data bias; governance compliance; performance metrics; data quality and availability; connectivity quality and availability; computational resource availability; and environmental parameters; and determine extent of permitted Al system autonomy based on the tru st factors.

39. The system of claim 38, wherein the processors are configured to perform at least one of, permitting Al system control during normal operations; or modifying Al system control permissions based on at least one of, environmental catastrophes, market swings, or other significant environmental shifts.

40. A system fortraining artificial intelligence in a digital twin environment, comprising: interfeces configured to receive from human experts at least one of, tagged data sets for Al classification, code, text, images, audio and video for training generative Al, preference indicators for recommendation systems, system configurations for configuration recommendations, or decision examples for similar contexts, and processors configured to train Al systems using the received data.

41. The system of claim 40, wherein the processors are configured to, designate first users to provide input data for initial Al system training, designate second users to supervise Al system outputs, and enable overlap between the first and second users.

42. A system for managing enterprise workflows, comprising: systems integration configured to integrate,1122individual activities, group activities, enterprise activities, and business ecosystem activities; data and sensor fusion configured to track outcomes using at least one of, environmental data, market data, or transaction data; andAl-driven stakeholder interfaces configured to perform at least one of, filtering information, prioritizing information, or distributing information for planning, simulation, decision making and operational action.

43. The system of claim 42, wherein the processors are configured to perform at least one of, enabling collaboration, distributing decision making to combinations of at least one of, human beings, artificial intelligence systems, or other systems, or removing fear of closing automation loops through at least one of, incremental change capabilities, experimentation capabilities, or easily reversible implementation.

44. The system of claim 42, further comprising a simulation environment configured to, use historical data, use current data, predict outcomes from deployment configurations, and provide a planning environment for resource deployment scenarios.

45. A computer-implemented system for managing operations of artificial intelligence systems, comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the system to: store a set of operations, wherein each operation of the set of operations is associated with at least one artificial intelligence system of a set of artificial intelligence systems; and initiate at least one operation of the stored set of operations with at least one artificial intelligence system of the set of artificial intelligence systems that is associated with the at least one operation.

46. The system of claim 45, wherein the instructions further cause the system to:1123generate at least one artificial intelligence system of the set of artificial intelligence systems; store the at least one generated artificial intelligence system; and deploy the at least one generated artificial intelligence system to at least one device.

47. The system of claim 45, wherein the instructions further cause the system to: generate a training data set fortraining at least one artificial intelligence system; train the at least one artificial intelligence system using the generated training data set; and verify a performance of the trained artificial intelligence system.

48. The system of claim 45, wherein the instructions further cause the system to: monitor a perform ance of at least one artificial intelligence system during execution of at least one operation; analyze the monitored performance; and update the at least one artificial intelligence system based on the analyzed performance.

49. The system of claim 45, wherein the instructions further cause the system to: orchestrate operation of at least a portion of the set of artificial intelligence systems to fulfill a set of requests; schedule invocation of artificial intelligence systems based on processing loads and throughput; and perform load-balancing of requests to invoke artificial intelligence systems.

50. The system of claim 45, wherein the instructions further cause the system to: adapt at least one artificial intelligence system for deployment to a particular device; package resources of the adapted artificial intelligence system for deployment; and transmit the packaged resources to the particular device.

51. The system of claim 45, wherein the instructions further cause the system to: aggregate two or more artificial intelligence systems into a hybrid system that combines heterogeneous processing capabilities; connect at least one output of a first artifi cial intelligence system to at. least one input of a second artificial intelligence system; and condition processing of data by the first artificial intelligence system based on processing of the data by the second artificial intelligence system.

52. The system of claim 45, wherein the instructions further cause the system to: govern operation of at least one artificial intelligence system based on legal, social, practical, or regulatory policies; m onitor invocations of the at least one artificial intelligence system to detect unauthorized uses; and monitor input to and output from the at least one artificial intelligence system to detect unauthorized content.

53. The system of claim 45, wherein the instructions further cause the system to: log activities of the artificial intelligence systems during performance of operations;1124correlate activities of various artificial intelligence systems to determine causal relationships; and store the logged activities in one or more event logs.

54. The system of claim 45, wherein the set of operations includes: operations for generating artificial intelligence models; operations for evaluating artificial intelligence models; operations for deploying artificial intelligence models; and operations for invoking artificial intelligence models to perform various operations.

55. A computer-implemented system for managing operations of artificial intelligence systems, comprising: a processor; and a memory' storing instructions that, when executed by the processor, cause the system to: generate synthetic data to augment training data sets; evaluate the synthetic data for hallmarks of synthetic data; and adapt augmentation techniques to reduce artifacts of synthetic data.

56. The system of claim 55, wherein the instructions further cause the system to: detect reduced performance of an artificial intelligence system; initiate retraining of the artificial intelligence system using the augmented training data sets; and verify that, a retrained artificial intelligence system addresses the reduced performance.

57. The sy stem of claim 55, wherein the instructions further cause the system to: generate a plurality' of generative artificial intelligence systems that produce content to be combined; generate primary content using a first artificial intelligence system; and generate supplemental content for the primary' content using a second artificial intelligence system.

58. The system of claim 55, wherein the instructions further cause the system to: invoke a first artificial intelligence system to generate content; invoke a second artificial intelligence system to review the generated content; and alter the generated content to address issues indicated in the review.

59. The system of claim 55, wherein the instructions further cause the system to: monitor social, political, economic, scientific, and technical events; extrapolate effects of current events on operation of artificial intelligence systems; and execute actions to promote value, security, and longevity of artificial intelligence systems.

60. The system of claim 55, wherein the instructions further cause the system to: monitor invocations of artificial intelligence systems to detect malicious use; detect attempts to circumvent governance mechanisms; and update policies and governance mechanisms to prevent detected circumvention attempts.

61. The system of claim 55, wherein the instructions further cause the system to: generate an artificial intelligence agent configured to monitor conditions;1125configure the artificial intelligence agent to learn policies based on experience; and configure the artificial intelligence agent to reflect on policies to identify synergies and conflicts.

62. The system of claim 55, wherein the instructions further cause the system to: determine hyperparameters of artificial neural networks; generate artificial neural networks according to determined hyperparameters; and store resources of generated artificial neural networks in a data store.

63. The system of claim 55, wherein the instructions further cause the system to: partition training data sets into training, validation and test data sets; analyze training data sets for training issues; and request collection of additional data to address identified training issues.

64. The system of claim 55, wherein the instructions further cause the system to: apply supervision to inputs of generative artificial intelligence systems; apply supervision to processing of generative artificial intelligence systems; and apply supervision to outputs of generative artificial intelligence systems based on rules, guidelines, heuristics or principles.1126