Intelligent vibration digital twin systems and methods for industrial environments

Abstract

A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

Description

INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to the following U.S. Provisional Patent Applications: Serial No. 62 / 939,769, filed November 25, 2019, entitled “METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS;” Serial No. 63 / 016,974, filed April 28, 2020, entitled “DIGITAL TWIN SYSTEMS FOR INDUSTRIAL ENVIRONMENTS” Serial No. 63 / 054,600, filed July 21, 2020, entitled “INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS” and Serial No. 63 / 069,548, filed August 24, 2020, entitled “INFORMATION TECHNOLOGY SYSTEMS AND METHODS FOR MANUFACTURING ARTIFICIAL INTELLIGENCE LEVERAGING DIGITAL TWINS.” This application also claims the benefit of priority to U.S. Patent Application Serial No. 17 / 104,964, filed November 25, 2020, entitled “INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS.” Each of the above applications is hereby incorporated by reference in its entirety as if fully set forth herein. BACKGROUND Field

[0002] The present disclosure relates to an intelligent digital twin system that creates, manages, and provides digital twins of industrial entities using vibration data and other data. Description of the Related Art

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

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

[0005] With the proliferation of vibration sensors and other Industrial Internet of Things (II0T) sensors, there are vast amounts of data available relating to industrial environments. This data is useful in predicting the need for maintenance and for classifying potential issues in the industrial environments. There are, however, many unexplored uses for vibration sensor data and other IToT 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] Industrial enterprises that rely on industrial experts struggle to capture the knowledge of these experts when they move on to another enterprise or leave the workforce. There exists a need in the art to capture industrial expertise and to use the captured industrial expertise in guiding newer workers or mobile electronic industrial entities to perform industrial-related tasks. SUMMARY

[0007] The present disclosure is directed to a platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system. The platform can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IloT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IToT system.

[0008] In embodiments, the present disclosure includes a method for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

[0009] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system. In embodiments, the digital twins are digital twins of at least one of industrial entities and industrial environments. In embodiments, the one or more dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data. In embodiments, the selected data sources include an Internet of Things connected device. In embodiments, the selected data sources include a machine vision system.

[0010] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties that are depicted in digital twins indicated by the request and a respective type of the one or more digital twins. In embodiments, the one or more dynamic models are identified using a lookup table.

[0011] In embodiments, the present disclosure includes a method including receiving imported data from one or more data sources, the imported data corresponding to an industrial environment; generating an environment digital twin representing the industrial environment based on the imported data; identifying one or more industrial entities within the industrial environment; generating a set of discrete digital twins representing the one or more industrial entities within the environment; embedding the set of discrete digital twins within the environment digital twin; establishing a connection with a sensor system of the industrial environment; receiving real-time sensor data from one or more sensors of the sensor system via the connection; and updating at least one of the environment digital twin and the set of discrete digital twins based on the real-time sensor data.

[0012] In embodiments, the connection with the sensor system is established via one of a webhook and an application programming interface (API). In embodiments, the environmental digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual manner. In embodiments, the present disclosure includes outputting the visual digital twins to a client application that displays the visual digital twins via a virtual reality headset. In embodiments, the present disclosure includes outputting the visual digital twins to a client application that displays the visual digital twins via a display device of a user device. In embodiments, the present disclosure includes outputting the visual digital twins to a client application that displays the visual digital twins via an augmented reality-enabled device. In embodiments, the present disclosure includes receiving user input relating to one or more steps performed in an industrial process relating to the industrial environment; and generating a process digital twin that defines the steps of the industrial process with respect to the industrial environment and one or more of the set of industrial entities. In embodiments, the present disclosure includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the environment digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In embodiments, each edge represents a relationship between two respective digital twins. In embodiments, embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective industrial entity represented by the respective discrete digital twin and the industrial environment. In embodiments, each edge represents a spatial relationship between two respective digital twins, and an operational relationship between two respective digital twins. In embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins, In embodiments, each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node. In embodiments, each entity node of the one or more entity nodes includes one or more behaviors of a respective properties of the respective industrial entity represented by the entity node. In embodiments, the environment node includes one or more properties of the environment. In embodiments, the environment node includes one or more behaviors of the environment.

[0013] In embodiments, the present disclosure includes executing a simulation based on the environment digital twin and the one or more discrete digital twins. In embodiments, the simulation simulates one of an operation of a machine in the industrial environment that produces an output based on a set of inputs and movement of workers in the industrial environment. In embodiments, the imported data includes a three-dimensional scan of the environment. In embodiments, the imported data includes a LIDAR scan of industrial the environment, In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment. In embodiments, generating the set of discrete digital twins includes importing a predefined digital twin of an industrial entity from a manufacturer of the industrial entity, wherein the predefined digital twin includes properties and behaviors of the industrial entity. In embodiments, generating the set of discrete digital twins includes classifying an industrial entity within the imported data of the industrial environment and generating a discrete digital twin corresponding to the classified industrial entity.

[0014] In embodiments, the present disclosure includes a system for monitoring interaction within an industrial environment. In embodiments, the system includes a digital twin datastore including data collected by a set of proximity sensors disposed within an industrial environment, the data including location data indicating respective locations of a plurality of elements within the industrial environment; and one or more processors configured to: maintain, via the digital twin datastore, an industrial-environment digital twin for the industrial environment; receive signals indicating actuation of at least one proximity sensor within the set of proximity sensors by a real-world element from the plurality of elements; collect, in response to actuation of the at least one proximity sensor, updated location data for the real-world element using the at least one proximity sensor; and update the industrial-environment digital twin within the digital twin datastore to include the updated location data.

[0015] In embodiments, each of the set of proximity sensors is configured to detect a device associated with the user. In embodiments, the device is a wearable device and an RFID device. In embodiments, each element of the plurality of elements is a mobile element. In embodiments, each element of the plurality of elements is a respective worker. In embodiments, the plurality of elements includes mobile equipment elements and workers, mobile-equipment-position data is determined using data transmitted by the respective mobile equipment element, and worker-position data is determined using data obtained by the system. In embodiments, the worker-position data is determined using information transmitted from a device associated with a respective worker. In embodiments, the actuation of the at least one proximity sensor occurs in response to interaction between the respective worker and the proximity sensor. In embodiments, the actuation of the at least one proximity sensor occurs in response to interaction between a worker and a respective at least one proximity-sensor digital twin corresponding to the at least one proximity sensor. In embodiments, the one or more processors collect updated location data for the plurality of elements using the set of proximity sensors in response to actuation of the at least one proximity sensor,

[0016] In embodiments, the present disclosure includes a system for modeling moving elements for an industrial digital twin. The system includes a digital twin datastore storing an industrial-environment digital twin corresponding to an industrial element, the industrial-environment digital twin including real-world-element digital twins embedded therein, wherein each real-world-element digital twin corresponds to a respective real- world element that is disposed within the industrial environment, the real-world-element digital twins including mobile-element digital twins that respectively correspond to a respective mobile element within the industrial environment; and one or more processors configured to: for each mobile element: determine whether the mobile element is in motion; and obtain path information from the mobile element, and model, in response to obtaining the path information for each mobile element, traffic within the industrial environment via a digital twin simulation system.

[0017] In embodiments, the path information is obtained from a navigation module of the mobile element. In embodiments, the one or more processors are further configured to obtain the path information by: detecting, using a plurality of sensors within the industrial environment, movement of the mobile element; obtaining a destination for the mobile element; calculating, using the plurality of sensors within the industrial environment, an optimized path for the mobile element; and instructing the mobile element to navigate the optimized path.

[0018] In embodiments, the optimized path includes path information for other mobile elements within the real-world elements and the optimized path minimizes interactions between mobile elements and humans within the industrial environment. In embodiments, the mobile elements include autonomous vehicles and non-autonomous vehicles and the optimized path reduces interactions of the autonomous vehicles with the non-autonomous vehicles. In embodiments, the traffic modeling includes use of a particle traffic model, a trigger-response mobile-element-following traffic model, a macroscopic traffic model, a microscopic traffic model, a submicroscopic traffic model, a mesoscopic traffic model, or a combination thereof

[0019] In embodiments, the present disclosure includes a method for updating one or more vibration fault level states of one or more digital twins including receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request, wherein the one or more dynamic models include a dynamic model that predicts when a vibration fault level occurs based on an input dataset; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more vibration fault level states of the one or more digital twins based on the output of the one or more dynamic models.

[0020] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system. In embodiments, the digital twins are digital twins of at least one of industrial entities and industrial environments. In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0021] In embodiments, the data source is selected from the set of an Internet of Things connected device, a machine vision system, an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, and a cross-point switch. In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins. In embodiments, the one or more dynamic models are identified using a lookup table.

[0022] In embodiments, the present disclosure includes a system for monitoring navigational route data through an industrial environment having real-world elements disposed therein. The system includes a digital twin datastore including an industrial environment digital twin corresponding to the industrial environment and a worker digital twin corresponding to a respective worker of a set of workers within the industrial environment; and one or more processors configured to: maintain, via the digital twin datastore, the industrial-environment digital twin to include contemporaneous positions for the set of workers within the industrial environment; monitor movement of each worker in the set of workers via a sensor array; determine, in response to detecting movement of the respective worker, navigational route data for the respective worker; and update the industrial-environment digital twin to include indicia of the navigational route data for the respective worker and to indicate movement of the worker digital twin along a route corresponding to the navigational route data. In embodiments, the one or more processors are further configured to, in response to representing movement of the respective worker, determine navigational route data for remaining workers in the set of workers. In embodiments, the navigational route data is automatically transmitted to the system by one or more individual-associated devices. In embodiments, the individual-associated device is one of a mobile device having cellular data capabilities and a wearable device associated with the worker. In embodiments, the navigational route data is determined via environment-associated sensors. In embodiments, the navigational route data is determined using historical routing data stored in the digital twin datastore. In embodiments, the historical route data is obtained from a device associated with the respective worker. In embodiments, the historical route data is obtained a device associated with another worker. In embodiments, the historical route data is associated with a current task of the worker. In embodiments. the digital twin datastore includes an industrial- environment digital twin. In embodiments, the one or more processors are further configured to: determine existence of a conflict between the navigational route data and the industrial-environment digital twin; alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array, the navigational route data for the worker; and update, in response to determining inaccuracy of the industrial-environment digital twin via the sensor array, the industrial-environment digital twin to thereby resolve the conflict.

[0023] In embodiments, the industrial-environment digital twin is updated using collected data transmitted from the worker. In embodiments, the collected data includes proximity sensor data, image data, or combinations thereof. In embodiments, the navigational route includes a route for collecting vibration measurements.

[0024] In embodiments, the present disclosure includes a method for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

[0025] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system. In embodiments, the digital twins are digital twins of at least one of industrial entities and industrial environments. In embodiments, the one or more dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data. In embodiments, the selected data sources include an Internet of Things connected device. In embodiments, the selected data sources include a machine vision system.

[0026] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties that are depicted in digital twins indicated by the request and a respective type of the one or more digital twins. In embodiments, the one or more dynamic models are identified using a lookup table.

[0027] In embodiments, the present disclosure includes a method including receiving imported data from one or more data sources, the imported data corresponding to an industrial environment; generating an environment digital twin representing the industrial environment based on the imported data; identifying one or more industrial entities within the industrial environment; generating a set of discrete digital twins representing the one or more industrial entities within the environment; embedding the set of discrete digital twins within the environment digital twin; establishing a connection with a sensor system of the industrial environment; receiving real-time sensor data from one or more sensors of the sensor system via the connection; and updating at least one of the environment digital twin and the set of discrete digital twins based on the real-time sensor data.

[0028] In embodiments, the connection with the sensor system is established via one of a webhook and an application programming interface (API). In embodiments, the environmental digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual manner. In embodiments, the present disclosure includes outputting the visual digital twins to a client application that displays the visual digital twins via a virtual reality headset. In embodiments, the present disclosure includes outputting the visual digital twins to a client application that displays the visual digital twins via a display device of a user device. In embodiments, the present disclosure includes outputting the visual digital twins to a client application that displays the visual digital twins via an augmented reality-enabled device. In embodiments, the present disclosure includes receiving user input relating to one or more steps performed in an industrial process relating to the industrial environment; and generating a process digital twin that defines the steps of the industrial process with respect to the industrial environment and one or more of the set of industrial entities. In embodiments, the present disclosure includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the environment digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In embodiments, each edge represents a relationship between two respective digital twins. In embodiments, embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective industrial entity represented by the respective discrete digital twin and the industrial environment. In embodiments, each edge represents a spatial relationship between two respective digital twins, and an operational relationship between two respective digital twins. In embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins, In embodiments, each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node. In embodiments, each entity node of the one or more entity nodes includes one or more behaviors of a respective properties of the respective industrial entity represented by the entity node. In embodiments, the environment node includes one or more properties of the environment. In embodiments, the environment node includes one or more behaviors of the environment.

[0029] In embodiments, the present disclosure includes executing a simulation based on the environment digital twin and the one or more discrete digital twins. In embodiments, the simulation simulates one of an operation of a machine in the industrial environment that produces an output based on a set of inputs and movement of workers in the industrial environment. In embodiments, the imported data includes a three-dimensional scan of the environment. In embodiments, the imported data includes a LIDAR scan of industrial the environment. In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment. In embodiments, generating the set of discrete digital twins includes importing a predefined digital twin of an industrial entity from a manufacturer of the industrial entity, wherein the predefined digital twin includes properties and behaviors of the industrial entity. In embodiments, generating the set of discrete digital twins includes classifying an industrial entity within the imported data of the industrial environment and generating a discrete digital twin corresponding to the classified industrial entity.

[0030] In embodiments, the present disclosure includes a system for monitoring interaction within an industrial environment. In embodiments, the system includes a digital twin datastore including data collected by a set of proximity sensors disposed within an industrial environment, the data including location data indicating respective locations of a plurality of elements within the industrial environment; and one or more processors configured to: maintain, via the digital twin datastore, an industrial-environment digital twin for the industrial environment; receive signals indicating actuation of at least one proximity sensor within the set of proximity sensors by a real-world element from the plurality of elements; collect, in response to actuation of the at least one proximity sensor, updated location data for the real-world element using the at least one proximity sensor; and update the industrial-environment digital twin within the digital twin datastore to include the updated location data.

[0031] In embodiments, each of the set of proximity sensors is configured to detect a device associated with the user. In embodiments, the device is a wearable device and an RFID device. In embodiments, each element of the plurality of elements is a mobile element. In embodiments, each element of the plurality of elements is a respective worker. In embodiments, the plurality of elements includes mobile equipment elements and workers, mobile-equipment-position data is determined using data transmitted by the respective mobile equipment element, and worker-position data is determined using data obtained by the system. In embodiments, the worker-position data is determined using information transmitted from a device associated with a respective worker. In embodiments, the actuation of the at least one proximity sensor occurs in response to interaction between the respective worker and the proximity sensor. In embodiments, the actuation of the at least one proximity sensor occurs in response to interaction between a worker and a respective at least one proximity-sensor digital twin corresponding to the at least one proximity sensor. In embodiments, the one or more processors collect updated location data for the plurality of elements using the set of proximity sensors in response to actuation of the at least one proximity sensor.

[0032] In embodiments, the present disclosure includes a system for modeling moving elements for an industrial digital twin. The system includes a digital twin datastore storing an industrial-environment digital twin corresponding to an industrial element, the industrial-environment digital twin including real-world-element digital twins embedded therein, wherein each real-world-element digital twin corresponds to a respective real- world element that is disposed within the industrial environment, the real-world-element digital twins including mobile-element digital twins that respectively correspond to a respective mobile element within the industrial environment; and one or more processors configured to: for each mobile element: determine whether the mobile element is in motion; and obtain path information from the mobile element, and model, in response to obtaining the path information for each mobile element, traffic within the industrial environment via a digital twin simulation system.

[0033] In embodiments, the path information is obtained from a navigation module of the mobile element. In embodiments, the one or more processors are further configured to obtain the path information by: detecting, using a plurality of sensors within the industrial environment, movement of the mobile element; obtaining a destination for the mobile element; calculating, using the plurality of sensors within the industrial environment, an optimized path for the mobile element; and instructing the mobile element to navigate the optimized path.

[0034] In embodiments, the optimized path includes path information for other mobile elements within the real-world elements and the optimized path minimizes interactions between mobile elements and humans within the industrial environment. In embodiments, the0 mobile elements include autonomous vehicles and non-autonomous vehicles and the optimized path reduces interactions of the autonomous vehicles with the non-autonomous vehicles. In embodiments, the traffic modeling includes use of a particle traffic model, a trigger-response mobile-element-following traffic model, a macroscopic traffic model, a microscopic traffic model, a submicroscopic traffic model, a mesoscopic traffic model, or a combination thereof

[0035] In embodiments, the present disclosure includes a method for updating one or more vibration fault level states of one or more digital twins including receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request, wherein the one or more dynamic models include a dynamic model that predicts when a vibration fault level occurs based on an input dataset; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more vibration fault level states of the one or more digital twins based on the output of the one or more dynamic models.

[0036] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment, In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system. In embodiments, the digital twins are digital twins of at least one of industrial entities and industrial environments. In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0037] In embodiments, the data source is selected from the set of an Intemet of Things connected device, a machine vision system, an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, and a cross-point switch. In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins. In embodiments, the one or more dynamic models are identified using a lookup table.

[0038] In embodiments, the present disclosure includes a system for monitoring navigational route data through an industrial environment having real-world elements disposed therein. The system includes a digital twin datastore including an industrial environment digital twin corresponding to the industrial environment and a worker digital twin corresponding to a respective worker of a set of workers within the industrial environment; and one or more processors configured to: maintain, via the digital twin datastore, the industrial-environment digital twin to include contemporaneous positions for the set of workers within the industrial environment; monitor movement of each worker in the set of workers via a sensor array; determine, in response to detecting movement of the respective worker, navigational route data for the respective worker; and update the industrial-environment digital twin to include indicia of the navigational route data for the respective worker and to indicate movement of the worker digital twin along a route corresponding to the navigational route data. In embodiments, the one or more processors are further configured to, in response to representing movement of the respective worker, determine navigational route data for remaining workers in the set of workers. In embodiments, the navigational route data is automatically transmitted to the system by one or more individual-associated devices. In embodiments, the individual-associated device is one of a mobile device having cellular data capabilities and a wearable device associated with the worker. In embodiments, the navigational route data is determined via environment-associated sensors. In embodiments, the navigational route data is determined using historical routing data stored in the digital twin datastore. In embodiments, the historical route data is obtained from a device associated with the respective worker. In embodiments, the historical route data is obtained a device associated with another worker. In embodiments, the historical route data is associated with a current task of the worker. In embodiments, the digital twin datastore includes an industrial- environment digital twin. In embodiments, the one or more processors are further configured to: determine existence of a conflict between the navigational route data and the industrial-environment digital twin; alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array, the navigational route data for the worker; and update, in response to determining inaccuracy of the industrial-environment digital twin via the sensor array, the industrial-environment digital twin to thereby resolve the conflict.

[0039] In embodiments, the industrial-environment digital twin is updated using collected data transmitted from the worker. In embodiments, the collected data includes proximity sensor data, image data, or combinations thereof. In embodiments, the navigational route includes a route for collecting vibration measurements.

[0040] According to some embodiments of the present disclosure, methods and systems are provided herein for updating properties of digital twins of industrial entities and digital twins of industrial environments, such as, without limitation, based on the impact of collected vibration data on a set of digital twin dynamic models such that the digital twins provide a computer-generated representation of the industrial entity or environment.

[0041] According to some embodiments of the present disclosure, a method for updating one or more properties of one or more digital twins is disclosed. The method includes receiving a request to update one or more properties of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more properties of the one or more digital twins based on the output of the one or more dynamic models.

[0042] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0043] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0044] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0045] In embodiments, the digital twins are digital twins of industrial entities.

[0046] In embodiments, the digital twins are digital twins of industrial environments.

[0047] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0048] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Internet of Things connected device, and a machine vision system.

[0049] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0050] In embodiments, the one or more dynamic models are identified using a lookup table.

[0051] According to some embodiments of the present disclosure, a method for updating one or more vibration fault level states of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more vibration fault level states of the one or more digital twins based on the output of the one or more dynamic models.

[0052] In embodiments, the vibration fault level states are selected from the set of normal, suboptimal, critical, and alarm.

[0053] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0054] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0055] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0056] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0057] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Internet of Things connected device, and a machine vision system.

[0058] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0059] In embodiments, the one or more dynamic models are identified using a lookup table.

[0060] According to some embodiments of the present disclosure, a method for updating one or more vibration severity unit values of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more vibration severity unit values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more vibration severity unit values of the one or more digital twins based on the output of the one or more dynamic models.

[0061] In embodiments, vibration severity units represent displacement.

[0062] In embodiments, vibration severity units represent velocity.

[0063] In embodiments, vibration severity units represent acceleration.

[0064] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0065] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0066] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0067] In embodiments, the digital twins are digital twins of industrial entities.

[0068] In embodiments, the digital twins are digital twins of industrial environments.

[0069] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0070] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Intemet of Things connected device, and a machine vision system,

[0071] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0072] In embodiments, the one or more dynamic models are identified using a lookup table.

[0073] According to some embodiments of the present disclosure, a method for updating one or more probability of failure values of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more probability of failure values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more probability of failure values of the one or more digital twins based on the output of the one or more dynamic models.

[0074] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0075] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0076] In embodiments, the request is received from a client application that supports a vibration sensor system,

[0077] In embodiments, the digital twins are digital twins of industrial entities.

[0078] In embodiments, the digital twins are digital twins of industrial environments.

[0079] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0080] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Internet of Things connected device, and a machine vision system.

[0081] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0082] In embodiments, the one or more dynamic models are identified using a lookup table.

[0083] According to some embodiments of the present disclosure, a method for updating one or more probability of downtime values of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more probability of downtime values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to a probability of downtime values of the one or more digital twins based on the output of the one or more dynamic models.

[0084] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0085] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0086] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0087] In embodiments, the digital twins are digital twins of industrial entities.

[0088] In embodiments, the digital twins are digital twins of industrial environments.

[0089] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0090] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Internet of Things connected device, and a machine vision system.

[0091] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0092] In embodiments, the one or more dynamic models are identified using a lookup table.

[0093] According to some embodiments of the present disclosure, a method for updating one or more probability of shutdown values of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more probability of shutdown values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to a probability of downtime of the one or more digital twins based on the output of the one or more dynamic models.

[0094] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0095] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0096] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0097] In embodiments, the digital twins are digital twins of industrial entities.

[0098] In embodiments, the digital twins are digital twins of industrial environments.

[0099] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0100] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Internet of Things connected device, and a machine vision system.

[0101] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective tvpe of the one or more digital twins.

[0102] In embodiments, the one or more dynamic models are identified using a lookup table.

[0103] According to some embodiments of the present disclosure, a method for updating one or more cost of downtime values of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more cost of downtime values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to cost of downtime values of the one or more digital twins based on the output of the one or more dynamic models.

[0104] In embodiments, the cost of downtime value is selected from the set of cost of downtime per hour, cost of downtime per day, cost of downtime per week, cost of downtime per month, cost of downtime per quarter, cost of downtime per year.

[0105] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0106] In embodiments, the request is received from a client application that supports an Industrial Intemet of Things sensor system,

[0107] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0108] In embodiments, the digital twins are digital twins of industrial entities.

[0109] In embodiments, the digital twins are digital twins of industrial environments.

[0110] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0111] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Internet of Things connected device, and a machine vision system.

[0112] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0113] In embodiments, the one or more dynamic models are identified using a lookup table.

[0114] According to some embodiments of the present disclosure, a method for updating one or more manufacturing key performance indicator (KPI) values of one or more digital twins is disclosed. The method includes receiving a request from a client application to update one or more manufacturing KPI values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more manufacturing KPI values of the one or more digital twins based on the output of the one or more dynamic models.

[0115] In embodiments, the manufacturing KPI is selected from the set of uptime, capacity utilization, on standard operating efficiency. overall operating efficiency, overall equipment effectiveness, machine downtime, unscheduled downtime, machine set up time, inventory turns, inventory accuracy, quality (e.g., percent defective), first pass yield, rework, scrap, failed audits, on-time delivery, customer returns, training hours, employee turnover, reportable health & safety incidents, revenue per employee, and profit per employee, schedule attainment, total cycle time, throughput, changeover time, yield, planned maintenance percentage, and availability.

[0116] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0117] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0118] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0119] In embodiments, the digital twins are digital twins of industrial entities.

[0120] In embodiments, the digital twins are digital twins of industrial environments.

[0121] In embodiments, the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0122] In embodiments, the data source is selected from the set of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a crosspoint switch, an Intemet of Things connected device, and a machine vision system,

[0123] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0124] In embodiments, the one or more dynamic models are identified using a lookup table.

[0125] According to some embodiments of the present disclosure, a method is disclosed. The method includes: receiving imported data from one or more data sources, the imported data corresponding to an industrial environment; generating an environment digital twin representing the industrial environment based on the imported data; identifying one or more industrial entities within the industrial environment; generating a set of discrete digital twins representing the one or more industrial entities within the environment; embedding the set of discrete digital twins within the environment digital twin; establishing a connection with a sensor system of the industrial environment; receiving real-time sensor data from one or more sensors of the sensor system via the connection; and updating at least one of the environment digital twin and the set of discrete digital twins based on the real-time sensor data.

[0126] In embodiments, the connection with the sensor system is established via an application programming interface (API).

[0127] In embodiments, the environmental digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual manner. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins via a virtual reality headset. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins via a display device of a user device. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins in a display interface with information related to the digital twins overlaid on the visual digital twins and / or displayed within the display interface. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins via an augmented reality-enabled device.

[0128] In some embodiments, the method further includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the environment digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In some embodiments, each edge represents a relationship between two respective digital twins. In some of these embodiments embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective industrial entity represented by the respective discrete digital twin and the industrial environment. In some embodiments, each edge represents a spatial relationship between two respective digital twins. In some embodiments, each edge represents an operational relationship between two respective digital twins. In some embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins. In some embodiments, each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node. In some embodiments, each entity node of the one or more entity nodes includes one or more behaviors of a respective properties of the respective industrial entity represented by the entity node. In some embodiments, the environment node includes one or more properties of the environment. In some embodiments, the environment node includes one or more behaviors of the environment.

[0129] In some embodiments, the method further includes executing a simulation based on the environment digital twin and the one or more discrete digital twins. In some embodiments, the simulation simulates an operation of a machine that produces an output based on a set of inputs. In some embodiments, the simulation simulates the vibrational patterns of a bearing in a machine of an industrial environment,

[0130] In embodiments, the one or more industrial entities are selected from a set of machine components, infrastructure components, equipment components, workpiece components, tool components, building components, electrical components, fluid handling components, mechanical components, power components, manufacturing components, energy production components, material extraction components, workers, robots, assembly lines, and autonomous vehicles.

[0131] In embodiments, the industrial environment is one of a factory, an energy production facility, a material extraction facility, a mining facility, a drilling facility, an industrial agricultural facility, and an industrial storage facility,

[0132] In embodiments, the imported data includes a three-dimensional scan of the environment.

[0133] In embodiments, the imported data includes a LIDAR scan of the industrial environment.

[0134] In embodiments, generating the digital twin of the industrial environment includes generating a set of surfaces of the industrial environment.

[0135] In embodiments, generating the digital twin of the industrial environment includes configuring a set of dimensions of the industrial environment,

[0136] In embodiments, generating the set of discrete digital twins includes importing a predefined digital twin of an industrial entity from a manufacturer of the industrial entity, wherein the predefined digital twin includes properties and behaviors of the industrial entity.

[0137] In embodiments, generating the set of discrete digital twins includes classifying an industrial entity within the imported data of the industrial environment and generating a discrete digital twin corresponding to the classified industrial entity.

[0138] According to aspects of the present disclosure, a system for monitoring interaction within an industrial environment includes a digital twin datastore and one or more processors. The digital twin datastore includes data collected by a set of proximity sensors disposed within an industrial environment. The data includes location data indicating respective locations of a plurality of elements within the industrial environment. The one or more processors are configured to maintain. via the digital twin datastore, an industrial- environment digital twin for the industrial environment, receive signals indicating actuation of at least one proximity sensor within the set of proximity sensors by a real- world element from the plurality of elements, collect. in response to actuation of the at least one proximity sensor, updated location data for the real-world element using the at least one proximity sensor. and update the industrial-environment digital twin within the digital twin datastore to include the updated location data.

[0139] In embodiments, each of the set of proximity sensors is configured to detect a device associated with the user.

[0140] In embodiments, the device is a wearable device.

[0141] In embodiments, the device is an RFID device.

[0142] In embodiments, each element of the plurality of elements is a mobile element.

[0143] In embodiments, each element of the plurality of elements is a respective worker.

[0144] In embodiments, the plurality of elements includes mobile equipment elements and workers, mobile-equipment-position data is determined using data transmitted by the respective mobile equipment element, and worker-position data is determined using data obtained by the system.

[0145] In embodiments, the worker-position data is determined using information transmitted from a device associated with respective workers,

[0146] In embodiments, the actuation of the at least one proximity sensor occurs in response to interaction between the respective worker and the proximity sensor.

[0147] In embodiments, the actuation of the at least one proximity sensor occurs in response to interaction between a worker and a respective at least one proximity-sensor digital twin corresponding to the at least one proximity sensor.

[0148] In embodiments, the one or more processors collect updated location data for the plurality of elements using the set of proximity sensors in response to actuation of the at least one proximity sensor.

[0149] According to aspects of the present disclosure, a system for monitoring an industrial environment having real-world elements disposed therein includes a digital twin datastore and one or more processors. The digital twin datastore includes a set of states stored therein. The set of states includes states for one or more of the real-world elements. Each state within the set of states is uniquely identifiable by a set of identifying criteria from the set of monitored attributes. The monitored attributes correspond to signals received from a sensor array operatively coupled to the real-world elements. The one or more processors are configured to maintain, via the digital twin datastore, an industrial- environment digital twin for the industrial environment, receive, via the sensor array, signals for one or more attributes within the set of monitored attributes, determine a present state for one or more of the real-world elements in response to determining that the signals for the one or more attributes satisfy a respective set of identifying criteria, and update, in response to determining the present state, the industrial-environment digital twin to include the present state of the one or more of the real-world elements. The present state corresponds to the respective state within the set of states.

[0150] In embodiments, a cognitive intelligence system stores the identifying criteria within the digital twin datastore.

[0151] In embodiments, a cognitive intelligence system, in response to receiving the identifying criteria, updates triggering conditions for the set of monitored attributes to include an updated triggering condition,

[0152] In embodiments, the updated triggering condition is reducing time intervals between receiving sensed attributes from the set of monitored attributes.

[0153] In embodiments, the sensed attributes are the attributes corresponding to the identifying criteria.

[0154] In embodiments, the sensed attributes are all attributes corresponding to the respective real-world element.

[0155] In embodiments, a cognitive intelligence system determines whether instructions exist for responding to the state and the cognitive intelligence system, in response to determining no instructions exist, determines instructions for responding to the state using a digital twin simulation system.

[0156] In embodiments, the digital twin simulation system and the cognitive intelligence system repeatedly iterate simulated values and response actions until an associated cost function is minimized and the one or more processors are further configured to, in response to minimization of the associated cost function, store the response action that minimizes the associated cost function within the digital twin datastore.

[0157] In embodiments, a cognitive intelligence system is configured to affect the response actions associated with the state.

[0158] In embodiments, the cognitive intelligence system is configured to halt operation of one or more real-world elements that are identified by the response actions.

[0159] In embodiments, the cognitive intelligence system is configured to determine resources for the industrial environment identified by the response actions and alter the resources in response thereto.

[0160] In embodiments, the resources include data transfer bandwidth and altering the resources includes establishing additional connections to thereby increase the data transfer bandwidth.

[0161] According to aspects of the present disclosure, a system for monitoring navigational route data through an industrial environment has real-world elements disposed therein includes a digital twin datastore and one or more processors. The digital twin datastore includes an industrial-environment digital twin corresponding to the industrial environment and a worker digital twin corresponding to a respective worker of a set of workers within the industrial environment. The one or more processors are configured to maintain, via the digital twin datastore, the industrial-environment digital twin to include contemporaneous positions for the set of workers within the industrial environment, monitor movement of each worker in the set of workers via a sensor array, determine, in response to detecting movement of the respective worker, navigational route data for the respective worker, update the industrial-environment digital twin to include indicia of the navigational route data for the respective worker, and move the worker digital twin along a route of the navigational route data.

[0162] In embodiments, the one or more processors are further configured to update, in response to representing movement of the respective worker, determine navigational route data for remaining workers in the set of workers.

[0163] In embodiments, the navigational route data includes a route for collecting vibration measurements from one or more machines in the industrial environment.

[0164] In embodiments, the navigational route data automatically transmitted to the system by one or more individual-associated devices.

[0165] In embodiments, the individual-associated device is a mobile device that has cellular data capabilities.

[0166] In embodiments, the individual-associated device is a wearable device associated with the worker.

[0167] In embodiments, the navigational route data is determined via environment- associated sensors.

[0168] In embodiments, the navigational route data is determined using historical routing data stored in the digital twin datastore.

[0169] In embodiments, the historical route data was obtained using the respective worker.

[0170] In embodiments, the historical route data was obtained using another worker.

[0171] In embodiments, the historical route data is associated with a current task of the worker.

[0172] In embodiments, the digital twin datastore includes an industrial-environment digital twin.

[0173] In embodiments, the one or more processors are further configured to determine existence of a conflict between the navigational route data and the industrial-environment digital twin, alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array, the navigational route data for the worker, and update, in response to determining inaccuracy of the industrial-environment digital twin via the sensor array, the industrial-environment digital twin to thereby resolve the conflict.

[0174] In embodiments, the industrial-environment digital twin is updated using collected data transmitted from the worker.

[0175] In embodiments, the collected data includes proximity sensor data, image data, or combinations thereof

[0176] According to aspects of the present disclosure, a system for monitoring navigational route data includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin with real-world- element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin provides a digital twin for corresponding real-world elements within the industrial environment. The real-world-elements include a set of workers. The one or more processors are configured to monitor movement of each worker in the set of workers, determine navigational route data for at least one worker in the set of workers, and represent the movement of the at least one worker by movement of associated digital twins using the navigational route data.

[0177] In embodiments, the one or more processors are further configured to update, in response to representing movement of the at least one worker, determine navigational route data for remaining workers in the set of workers.

[0178] In embodiments, the navigational route data includes a route for collecting vibration measurements from one or more machines in the industrial environment.

[0179] In embodiments, the navigational route data automatically transmitted to the system by one or more individual-associated devices.

[0180] In embodiments, the individual-associated device is a mobile device that has cellular data capabilities,

[0181] In embodiments, the individual-associated device is a wearable device associated with the worker.

[0182] In embodiments, the navigational route data is determined via environment- associated sensors.

[0183] In embodiments, the navigational route data is determined using historical routing data stored in the digital twin datastore.

[0184] In embodiments, the historical route data was obtained using the respective worker.

[0185] In embodiments, the historical route data was obtained using another worker.

[0186] In embodiments, the historical route data is associated with a current task of the worker.

[0187] In embodiments, the digital twin datastore includes an industrial-environment digital twin.

[0188] In embodiments, the one or more processors are further configured to determine existence of a conflict between the navigational route data and the industrial-environment digital twin, alter, in response to determining accuracy of the industrial-environment digital twin via a sensor array, the navigational route data for the worker, and update, in response to determining inaccuracy of the industrial-environment digital twin via the sensor array, the industrial-environment digital twin to thereby resolve the conflict.

[0189] In embodiments, the industrial-environment digital twin is updated using collected data transmitted from the worker.

[0190] In embodiments, the collected data includes proximity sensor data, image data, or combinations thereof

[0191] According to aspects of the present disclosure, a system for representing industrial workpiece objects in a digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin with real-world-element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin providing a digital twin for corresponding real-world elements within the industrial environment. The real-world-elements including an industrial workpiece and a worker. The one or more processors are configured to simulate, using a digital twin simulation system, a set of physical interactions to be performed on the industrial workpiece by the worker. The simulation includes obtaining the set of physical interactions, determining an expected duration for performance of each physical interaction within the set of physical interactions based on historical data of the worker, and storing, within the digital twin datastore, industrial-workpiece digital twins corresponding to performance of the set of physical interactions on the industrial workpiece.

[0192] In embodiments, the historical data is obtained from user-input data.

[0193] In embodiments, the historical data is obtained from a sensor array within the industrial environment.

[0194] In embodiments, the historical data is obtained from a wearable device worn by the worker.

[0195] In embodiments, each datum of the historical data includes indicia of a first time and a second time, and the first time is a time of performance for the physical interaction.

[0196] In embodiments, the second time is a time for beginning an expected break time of the worker.

[0197] In embodiments, the historical data further includes indicia of a duration for the expected break time.

[0198] In embodiments, the second time is a time for ending an expected break time of the worker.

[0199] In embodiments, the historical data further includes indicia of a duration for the expected break time.

[0200] In embodiments, the second time is a time for ending an unexpected break time of the worker.

[0201] In embodiments, the historical data further includes indicia of a duration for the unexpected break time.

[0202] In embodiments, each datum of the historical data includes indicia of consecutive interactions of the worker with a plurality of other workpieces prior to performing the set of physical interactions with the workpiece.

[0203] In embodiments, each datum of the historical data includes indicia of consecutive days the worker was present within the industrial environment.

[0204] In embodiments, each datum of the historical data includes indicia of an age of the worker.

[0205] In embodiments, the historical data further includes indicia of a first duration for an expected break time of the worker and a second duration for an unexpected break time of the worker, each datum of the historical data includes indicia of a plurality of times, indicia of consecutive interactions of the worker with a plurality of other workpieces prior to performing the set of physical interactions with the workpiece and indicia of consecutive days the worker was present within the industrial environment, and / or indicia of an age of the worker. The plurality of times includes a first time, a second time, a third time, and a fourth time, The first time is a time of performance for the physical interaction, the second time is a time for beginning the expected break time, the third time is a time for ending the expected break time, and the fourth time is a time for ending the unexpected break time.

[0206] In embodiments, the industrial-workpiece digital twins are a first industrial- workpiece digital twin corresponding to the industrial workpiece prior to performance of any physical interaction and a second industrial-workpiece digital twin corresponding to the industrial workpiece after performance of the set of physical interactions.

[0207] In embodiments, the industrial-workpiece digital twins are a plurality of industrial- workpiece digital twins, each of the plurality of industrial-workpiece digital twins corresponds to the industrial workpiece after performance of a respective one of the set of physical interactions.

[0208] According to aspects of the present disclosure, a system for inducing an experience via a wearable device includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin with real-world- element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin providing a digital twin for corresponding real-world elements within the industrial environment. The real-world-elements including a wearable device worn by a wearer within the industrial environment. The one or more processors are configured to embed a set of control instructions for a wearable device within the digital twins and induce, in response to an interaction between the wearable device and each respective one of the digital twins, an experience for the wearer of the wearable device.

[0209] In embodiments, the wearable device is configured to output video, audio, haptic feedback, or combinations thereof to induce the experience for the wearer.

[0210] In embodiments, the experience is a virtual reality experience.

[0211] In embodiments, the wearable device includes an image capture device and the interaction includes the wearable device capturing an image of the digital twin.

[0212] In embodiments, the wearable device includes a display device and the experience includes display of information related to the respective digital twin.

[0213] In embodiments, the information displayed includes financial data associated with the digital twin.

[0214] In embodiments, the information displayed includes a profit or loss associated with operation of the digital twin.

[0215] In embodiments, the information displayed includes information related to an occluded element that is at least partially occluded by a foreground element.

[0216] In embodiments, the information displayed includes an operating parameter for the occluded element.

[0217] In embodiments, the information displayed further includes a comparison to a design parameter corresponding to the operating parameter displayed.

[0218] In embodiments, the comparison includes altering display of the operating parameter to change a color, size, or display period for the operating parameter.

[0219] In embodiments, the information includes a virtual model of the occluded element overlaid on the occluded element and visible with the foreground element.

[0220] In embodiments, the information includes indicia for removable elements that are is configured to provide access to the occluded element. Each indicium is displayed proximate to the respective removable element.

[0221] In embodiments, the indicia are sequentially displayed such that a first indicium corresponding to a first removable element is displayed, and a second indicium corresponding to a second removable element is displayed in response to a worker removing the first removable element.

[0222] According to aspects of the present disclosure, a system for embedding device output in an industrial digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin having real-world-element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin providing a digital twin for corresponding real-world elements within the industrial environment. The real-world elements including a simultaneous location and mapping sensor. The one or more processors are configured to obtain location information from the simultaneous location and mapping sensor, determine that the simultaneous location and mapping sensor is disposed within the environment, collect mapping information, pathing information, or a combination thereof from the simultaneous location and mapping sensor, and update the industrial-environment digital twin using the mapping information, the pathing information, or the combination thereof. The collection is in response to determining the simultaneous location and mapping sensor is within the industrial environment.

[0223] In embodiments, the one or more processors are further configured to detect objects within the mapping information and, for each detected object within the mapping information, determine whether the detected object corresponds to an existing real-world- element digital twin, add, in response to determining that the detected object does not correspond to an existing real-world-element digital twin, a detected-object digital twin to the real-world-element digital twins within the digital twin datastore using a digital twin generation system, and update, in response to determining that the detected object corresponds to an existing real-world-element digital twin, the real-world-element digital twin to include new information detected by the simultaneous location and mapping Sensor.

[0224] In embodiments, the simultaneous location and mapping sensor is configured to produce the mapping information using a sub-optimal mapping algorithm.

[0225] In embodiments, the sub-optimal mapping algorithm produces bounded-region representations for elements within the industrial environment.

[0226] In embodiments, the one or more processors are further configured to obtain objects detected by the sub-optimal mapping algorithm, determine whether the detected object corresponds to an existing real-world-element digital twin, and update, in response to determining the detected object corresponds to the existing real-world-element digital twin, the mapping information to include dimensional information for the real-world- element digital twin.

[0227] In embodiments, the updated mapping information is provided to the simultaneous location and mapping sensor to thereby optimize navigation through the industrial environment.

[0228] In embodiments, the one or more processors are further configured to request, in response to determining the detected object does not correspond to an existing real-world- element digital twin, updated data for the detected object from the simultaneous location and mapping sensor that is configured to produce a refined map of the detected object.

[0229] In embodiments, the simultaneous location and mapping sensor provides the updated data using a second algorithm. The second algorithm is configured to increase resolution of the detected object.

[0230] In embodiments, the simultaneous location and mapping sensor, in response to receiving the request, captures the updated data for the real-world element corresponding to the detected object.

[0231] In embodiments, the simultaneous location and mapping sensor is within an autonomous vehicle navigating the industrial environment,

[0232] In embodiments, navigation of the autonomous vehicle includes use of digital twins received from the digital twin datastore,

[0233] According to aspects of the present disclosure, a system for embedding device output in an industrial digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin having real-world-element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin providing a digital twin for corresponding real-world elements within the industrial environment. The real-world elements including a light detection and ranging sensor. The one or more processors are configured to obtain output from the light detection and ranging sensor and embed the output of the light detection and ranging sensor into the industrial- environment digital twin to define external features of at least one of the real-world elements within the industrial environment.

[0234] In embodiments, the one or more processors are further configured to analyze the output to determine a plurality of detected objects within the output of the light detection and ranging sensor. Each of the plurality of detected objects is a closed shape.

[0235] In embodiments, the one or more processors are further configured to compare the plurality of detected objects to the real-world-element digital twins within the digital twin datastore and, for each of the plurality of detected objects, update, in response to determining the detected object corresponds to one or more of the real-world-element digital twins, the respective real-world-element digital twin within the digital twin datastore, and add, in response to determining the detected object does not correspond to the real-world-element digital twins, a new real-world-element digital twin to the digital twin datastore.

[0236] In embodiments, the output from the light detection and ranging sensor is received in a first resolution and the one or more processors are further configured to compare the plurality of detected objects to the real-world-element digital twins within the digital twin datastore and, for each of the plurality of detected objects that does not correspond to a real-world-element digital twin, direct the light detection and ranging sensor to increase scan resolution to a second resolution and perform a scan of the detected object using the second resolution.

[0237] In embodiments, the scan is at least 5 times the resolution of the first resolution.

[0238] In embodiments, the scan is at least 10 times the resolution of the first resolution.

[0239] In embodiments, the output from the light detection and ranging sensor is received in a first resolution and the one or more processors are further configured to compare the plurality of detected objects to the real-world-element digital twins within the digital twin datastore and, for each of the plurality of detected objects, update, in response to determining the detected object corresponds to one or more of the real-world-element digital twins, the respective real-world-element digital twin within the digital twin datastore. In response to determining the detected object does not correspond to the real- world-element digital twins, the system is further configured to direct the light detection and ranging sensor to increase scan resolution to a second resolution, perform a scan of the detected object using the second resolution, and add a new real-world-element digital twin for the detected object to the digital twin datastore.

[0240] According to aspects of the present disclosure, a system for embedding device output in an industrial digital twin includes a digital twin datastore and one or more processors. The digital twin datastore includes an industrial-environment digital twin providing a digital twin of an industrial environment. The industrial environment includes real-world elements disposed therein. The real-world elements include a plurality of wearable devices. The industrial-environment digital twin includes a plurality of real- world-element digital twins embedded therein. Each real-world-element digital twin corresponds to a respective at least one of the real-world elements. The one or more processors are configured to, for each of the plurality of wearable devices, obtain output from the wearable device, and update, in response to detecting a triggering condition, the industrial-environment digital twin using the output from the wearable device.

[0241] In embodiments, the triggering condition is receipt of the output from the wearable device.

[0242] In embodiments, the triggering condition is a determination that the output from the wearable device is different from a previously stored output from the wearable device.

[0243] In embodiments, the triggering condition is a determination that received output from another wearable device within the plurality of wearable devices is different from a previously stored output from the other wearable device.

[0244] In embodiments, the triggering condition includes a mismatch between the output from the wearable device and contemporaneous output from another of the wearable devices.

[0245] In embodiments, the triggering condition includes a mismatch between the output from the wearable device and a simulated value for the wearable device.

[0246] In embodiments, the triggering condition includes user interaction with a digital twin corresponding to the wearable device.

[0247] In embodiments, the one or more processors are further configured to detect objects within mapping information received from a simultaneous location and mapping sensor. For each detected object within the mapping information, the system is further configured to determine whether the detected object corresponds to an existing real-world-element digital twin, and, in response to determining that the detected object does not correspond to an existing real-world-element digital twin, a detected-object digital twin to the real- world-element digital twins within the digital twin datastore using a digital twin generation system, and update, in response to determining that the detected object corresponds to an existing real-world-element digital twin, the real-world-element digital twin to include new information detected by the simultaneous location and mapping sensor.

[0248] In embodiments, a simultaneous location and mapping sensor is configured to produce mapping information using a sub-optimal mapping algorithm,

[0249] In embodiments, the sub-optimal mapping algorithm produces bounded-region representations for elements within the industrial environment.

[0250] In embodiments, the one or more processors are further configured to obtain objects detected by the sub-optimal mapping algorithm, determine whether the detected object corresponds to an existing real-world-element digital twin, and update, in response to determining the detected object corresponds to the existing real-world-element digital twin, the mapping information to include dimensional information from the real-world- element digital twin.

[0251] In embodiments, the updated mapping information is provided to the simultaneous location and mapping sensor to thereby optimize navigation through the industrial environment.

[0252] In embodiments, the one or more processors are further configured to request, in response to determining the detected object does not correspond to an existing real-world- element digital twin, updated data for the detected object from the simultaneous location and mapping sensor that is configured to produce a refined map of the detected object.

[0253] In embodiments, the simultaneous location and mapping sensor provides the updated data using a second algorithm. The second algorithm is configured to increase resolution of the detected object.

[0254] In embodiments, the simultaneous location and mapping sensor, in response to receiving the request, captures the updated data for the real-world element corresponding to the detected object.

[0255] In embodiments, the simultaneous location and mapping sensor is within an autonomous vehicle navigating the industrial environment,

[0256] In embodiments, navigation of the autonomous vehicle includes use of real-world- element digital twins received from the digital twin datastore.

[0257] According to aspects of the present disclosure, a system for representing attributes in an industrial digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin including real- world-element digital twins embedded therein. The industrial-environment digital twin corresponds to an industrial environment. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the industrial environment. The real-world-element digital twins include mobile-element digital twins. Each mobile-element digital twin provides a digital twin of a respective mobile element within the real-world elements. The one or more processors are configured to, for each mobile element, determine, in response to occurrence of a triggering condition, a position of the mobile element, and update, in response to determining the position of the mobile element, the mobile-element digital twin corresponding to the mobile element to reflect the position of the mobile element.

[0258] In embodiments, the mobile elements are workers within the industrial environment.

[0259] In embodiments, the mobile elements are vehicles within the industrial environment.

[0260] In embodiments, triggering condition is expiration of dynamically determined time interval.

[0261] In embodiments, the dynamically determined time interval is increased in response to determining a single mobile element within the industrial environment.

[0262] In embodiments, the dynamically determined time interval is increased in response to determining occurrence of a predetermined period of reduced environmental activity.

[0263] In embodiments, the dynamically determined time interval is decreased in response to determining abnormal activity within the industrial environment.

[0264] In embodiments, the dynamically determined time interval is a first time interval, and the dynamically determined time interval is decreased to a second time interval in response to determining movement of the mobile element.

[0265] In embodiments, the dynamically determined time interval is increased from the second time interval to the first time interval in response to determining nonmovement of the mobile element for at least a third time interval.

[0266] In embodiments, the triggering condition is expiration of a time interval. The time interval is calculated based on a probability that the mobile element has moved.

[0267] In embodiments, the triggering condition is proximity of the mobile element to another of the mobile elements.

[0268] In embodiments, the triggering condition is based on density of movable elements within the industrial environment.

[0269] In embodiments, the path information obtained from a navigation module of the mobile element.

[0270] In embodiments, the one or more processors are further configured to obtain the path information including detecting, using a plurality of sensors within the industrial environment, movement of the mobile element, obtaining a destination for the mobile element, calculating, using the plurality of sensors within the industrial environment, an optimized path for the mobile element, and instructing the mobile element to navigate the optimized path.

[0271] In embodiments, the optimized path includes using path information for other mobile elements within the real-world elements.

[0272] In embodiments, the optimized path minimizes interactions between mobile elements and humans within the industrial environment.

[0273] In embodiments, the mobile elements include autonomous vehicles and non- autonomous vehicles, and the optimized path reduces interactions of the autonomous vehicles with the non-autonomous vehicles.

[0274] In embodiments, the traffic modeling includes use of a particle traffic model, a trigger-response mobile-element-following traffic model, a macroscopic traffic model, a microscopic traffic model, a submicroscopic traffic model, a mesoscopic traffic model, or a combination thereof

[0275] According to aspects of the present disclosure, a system for representing design specification information includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin including real-world- element digital twins embedded therein. The industrial-environment digital twin corresponds to an industrial environment. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the industrial environment. The one or more processors are configured to. for each of the real-world elements, determine a design specification for the real-world element, associate the design specification with the real-world-element digital twin, and display the design specification to a user in response to the user interacting with the real-world-element digital twin.

[0276] In embodiments, the user interacting with the real-world-element digital twin includes the user selecting the real-world-element digital twin.

[0277] In embodiments, the user interacting with the real-world-element digital twin includes the user directing an image capture device toward the real-world-element digital twin.

[0278] In embodiments, the image capture device is a wearable device.

[0279] In embodiments, the real-world element digital twin is an industrial-environment digital twin.

[0280] In embodiments, the design specification is stored in the digital twin datastore in response to input of the user.

[0281] In embodiments, the design specification is determined using a digital twin simulation system.

[0282] In embodiments, the one or more processors are further configured to, for each of the real-world elements, detect, using a sensor within the industrial environment, one or more contemporaneous operating parameters, compare the one or more contemporaneous operating parameters to the design specification, and automatically display the design specification, the one or more contemporaneous operating parameters, or a combination thereof in response to a mismatch between the one or more contemporaneous operating parameters and the design specification. The one or more contemporaneous operating parameters correspond to the design specification of the real-world element.

[0283] In embodiments, display of the design specification includes indicia of contemporaneous operating parameters.

[0284] In embodiments, display of the design specification includes source indicia for the specification information.

[0285] In embodiments, the source indicia inform the user that the design specification was determined via use of a digital twin simulation system.A more complete understanding of the disclosure will be appreciated from the description and accompanying drawings and the claims, which follow.

[0287] In embodiments, the one or more dynamic models are identified using a lookup table.

[0288] According to some embodiments of the present disclosure, a method for updating one or more fluid dynamics-related values of one or more digital twins is disclosed. The method includes: receiving a request from a client application to update one or more fluid dynamics-related values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to fluid dynamics-related values of the one or more digital twins based on the output of the one or more dynamic models.

[0289] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0290] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0291] In embodiments, the digital twins are digital twins of industrial entities.

[0292] In embodiments, the digital twins are digital twins of industrial environments.

[0293] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0294] In embodiments, the data source is an Internet of Things connected device.

[0295] In embodiments, the data source is a machine vision system.

[0296] In embodiments, the fluid dynamics-related values are fluid flow rate values.

[0297] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins,

[0298] In embodiments, the one or more dynamic models are identified using a lookup table.

[0299] According to some embodiments of the present disclosure, a method for updating one or more radiation values of one or more digital twins is disclosed. The method includes: receiving a request from a client application to update one or more radiation values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to radiation values of the one or more digital twins based on the output of the one or more dynamic models.

[0300] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0301] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0302] In embodiments, the digital twins are digital twins of industrial entities.

[0303] In embodiments, the digital twins are digital twins of industrial environments.

[0304] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0305] In embodiments, the data source is an Internet of Things connected device.

[0306] In embodiments, the data source is a machine vision system.

[0307] In embodiments, the radiation values are gamma dose rate values.

[0308] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins,

[0309] In embodiments, the one or more dynamic models are identified using a lookup table.

[0310] According to some embodiments of the present disclosure, a method for updating one or more quantum mechanical values of one or more digital twins is disclosed. The method includes; receiving a request from a client application to update one or more quantum mechanical values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to quantum mechanical values of the one or more digital twins based on the output of the one or more dynamic models.

[0311] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0312] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0313] In embodiments, the digital twins are digital twins of industrial entities.

[0314] In embodiments, the digital twins are digital twins of industrial environments.

[0315] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0316] In embodiments, the data source is an Internet of Things connected device.

[0317] In embodiments, the data source is a machine vision system.

[0318] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0319] In embodiments, the one or more dynamic models are identified using a lookup table.

[0320] According to some embodiments of the present disclosure, a method for updating one or more location values of one or more digital twins is disclosed. The method includes: receiving a request from a client application to update one or more location values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to location values of the one or more digital twins based on the output of the one or more dynamic models.

[0321] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0322] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0323] In embodiments, the digital twins are digital twins of industrial entities.

[0324] In embodiments, the digital twins are digital twins of industrial environments.

[0325] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0326] In embodiments, the data source is an Internet of Things connected device.

[0327] In embodiments, the data source is a machine vision system.

[0328] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0329] In embodiments, the one or more dynamic models are identified using a lookup table.

[0330] According to some embodiments of the present disclosure, a method for updating one or more metal concentration values of one or more digital twins is disclosed. The method includes:

[0331] receiving a request from a client application to update one or more metal concentration values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to metal concentration values of the one or more digital twins based on the output of the one or more dynamic models.

[0332] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0333] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0334] In embodiments, the digital twins are digital twins of industrial entities.

[0335] In embodiments, the digital twins are digital twins of industrial environments.

[0336] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0337] In embodiments, the data source is an Internet of Things connected device.

[0338] In embodiments, the data source is a machine vision system.

[0339] In embodiments, the metal is selected from the set of copper, chromium, nickel, and zinc.

[0340] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins,

[0341] In embodiments, the one or more dynamic models are identified using a lookup table.

[0342] According to some embodiments of the present disclosure, a method for updating one or more organic compound concentration values of one or more digital twins is disclosed. The method includes: receiving a request from a client application to update one or more organic compound concentration values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to organic compound concentration values of the one or more digital twins based on the output of the one or more dynamic models.

[0343] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0344] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0345] In embodiments, the digital twins are digital twins of industrial entities.

[0346] In embodiments, the digital twins are digital twins of industrial environments.

[0347] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0348] In embodiments, the data source is an Internet of Things connected device.

[0349] In embodiments, the data source is a machine vision system.

[0350] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

[0351] In embodiments, the one or more dynamic models are identified using a lookup table.

[0352] According to some embodiments of the present disclosure, a method for updating one or more biological compound concentration values of one or more digital twins is disclosed. The method includes: receiving a request from a client application to update one or more biological compound concentration values of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; retrieving data from selected data sources; calculating one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more values related to biological compound concentration values of the one or more digital twins based on the output of the one or more dynamic models.

[0353] In embodiments, the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

[0354] In embodiments, the request is received from a client application that supports an Industrial Internet of Things sensor system.

[0355] In embodiments, the digital twins are digital twins of industrial entities.

[0356] In embodiments, the digital twins are digital twins of industrial environments.

[0357] In embodiments, the dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0358] In embodiments, the data source is an Internet of Things connected device.

[0359] In embodiments, the data source is a machine vision system.

[0360] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective tvpe of the one or more digital twins.

[0361] In some embodiments, the method further includes receiving user input relating to one or more steps performed in an industrial process relating to the industrial environment; and generating a process digital twin that defines the steps of the industrial process with respect to the industrial environment and one or more of the set of industrial entities.

[0362] According to aspects of the present disclosure, a system for representing power outages includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin with real-world-element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin provides a digital twin for corresponding real-world elements within the industrial environment. The real-world- elements include a set of electrically powered elements. The one or more processors are configured to monitor supplied power for the set of electrically powered elements, determine whether the supplied power matches identifying criteria for a power-loss state, and represent, for each of the set of electrically powered elements, an effect of the power- loss state on the electrically powered element using the corresponding digital twin.

[0363] In embodiments, the one or more processors are further configured to simulate, via a digital twin simulation system, effects of the power-loss state on each of the real-world elements, and store, via the digital twin datastore, the effect of the power-loss state.

[0364] In embodiments, the one or more processors are further configured to automatically implement, in response to determining that the supplied power matches identifying criteria for the power-loss state, a mitigating action.

[0365] In embodiments, the mitigating action includes selecting a first portion of the real- world elements and a second portion of the real-world elements, stopping power consumption for the first portion of the real-world elements, and continuing power consumption for the second portion of the real-world elements.

[0366] In embodiments, continuing power consumption for the second portion of the real- world elements includes reducing power consumed by each respective real-world element to a suboptimal operating level.

[0367] In embodiments, the suboptimal operating level is a minimum power level required to operate the respective real-world element.

[0368] In embodiments, the mitigating action further includes supplying power to the second portion of the real-world elements from stored power, the stored power is present within the industrial environment prior to occurrence of the power-loss state.

[0369] In embodiments, the stored power is supplied from batteries within the environment.

[0370] In embodiments, the real-world elements include a third portion of the real-world elements, each real-world element within the third portion of the real-world elements including a respective battery is disposed therein, each respective battery is configured to supply power the respective real-world element in response to occurrence of a power-loss state, and the one or more processors are further configured to power the second portion of the real-world elements using the batteries of the third portion of the real-world elements.

[0371] In embodiments, the mitigating action is determined by simulating, using a digital twin simulation system, effects of the power-loss state on the industrial environment by simulating effects of the power-loss state on each of the real-world-element digital twins, determining, using a cognitive intelligence system, a plurality of potential actions, evaluating, using the cognitive intelligence system and the digital twin simulation system, effects of each of the plurality of potential actions on the industrial environment, and selecting, using the cognitive intelligence system, the mitigating action from the plurality of potential actions based on minimization of a cost function. The plurality of potential actions includes maintaining power, reducing power, and ceasing power to each real-world element.

[0372] In embodiments, minimizing the cost function includes maximizing output from the industrial environment to downstream processes.

[0373] In embodiments, minimizing the cost function includes minimizing maintenance of the real-world elements caused by the power-loss state.

[0374] In embodiments, minimizing the cost function includes minimizing a time period to achieve steady state operation after cessation of the power-loss state.

[0375] In embodiments, the one or more processors are further configured to maintain stored power within a backup power system at an under-capacity level, calculate a probability for occurrence of the power-loss state before lapse of a predetermined time period, and increase, in response to the probability for occurrence of the power-loss state exceeding a predetermined threshold, the stored power within the backup power system to full capacity of the backup power system.

[0376] In embodiments, the predetermined time period is the time period for the backup power system to reach full capacity.

[0377] In embodiments, calculating the probability for occurrence of the power-loss state includes use of weather forecast data.

[0378] According to aspects of the present disclosure, a system for representing loss of data connectivity includes a digital twin datastore and one or more processors. The digital twin datastore stores an industrial-environment digital twin with real-world-element digital twins embedded therein. The industrial-environment digital twin provides a digital twin of an industrial environment. Each real-world-element digital twin providing a digital twin for corresponding real-world elements within the industrial environment, the real- world elements including a plurality of sensors in data communication with a connected device that is external to the industrial environment. The one or more processors are configured to monitor connectivity of the real-world elements with the connected device, determine whether the monitored connectivity matches identifying criteria for a network- connectivity state, and represent an effect of the network-connectivity state on each real- world-element digital twin.

[0379] In embodiments, the one or more processors are further configured to simulate, via a digital twin simulation system, effects of the network-connectivity state on each of the real-world elements and store, via the digital twin datastore, the effect of the network- connectivity state.

[0380] In embodiments, the one or more processors are further configured to automatically implement, in response to determining occurrence of the network-connectivity state, a mitigating action.

[0381] In embodiments, the mitigating action includes determining that the network- connectivity state is a bandwidth-limited state, selecting a first portion of the sensors and a second portion of the sensors, reducing network communication for the first portion of the sensors, and continuing network communication for the second portion of the sensors.

[0382] In embodiments, reducing network communication for the first portion of the sensors includes increasing a time interval between communications from the first portion of the sensors.

[0383] In embodiments, reducing network communication for the first portion of the sensors includes decreasing an amount of information sent from the first portion of the Sensors.

[0384] In embodiments, reducing network communication for the first portion of the sensors includes edge processing of data collected by the first portion of the sensors to thereby produce edge-processed data and transmitting the edge-processed data to the connected device.

[0385] In embodiments, the mitigating action includes selecting a first portion of the real- world elements and a second portion of the real-world elements, establishing direct connections between the first portion of the real-world elements and devices external to the industrial environment, and transmitting data from the second portion of the real-world elements to the connected device via the direct connections. Each real-world element of the first portion of the real-world element includes a wireless-communication module is configured to directly connect to devices external to the industrial environment and transmit data originating from the respective real-world element therethrough.

[0386] In embodiments, the mitigating action further includes inhibiting transfer of data originating from the respective real-world element via the respective direct connection.

[0387] In embodiments, the mitigating action is determined by simulating, using a digital twin simulation system, effects of the network-connectivity state on the industrial environment by simulating effects of the network-connectivity state on reporting from and control of each of the real-world-element digital twins, determining, using a cognitive intelligence system, a plurality of potential actions, evaluating, using the cognitive intelligence system and the digital twin simulation system, effects of each of the plurality of potential actions on the industrial environment, and selecting, using the cognitive intelligence system, the mitigating action from the plurality of potential actions based on minimization of a cost function. The plurality of potential actions includes reducing communications and establishing alternate modes of communication with each real-world element.

[0388] In embodiments, minimizing the cost function includes minimizing impact on processes downstream from the industrial environment.

[0389] In embodiments, minimizing the cost function includes minimizing a time period to achieve steady state operation after cessation of the network-connectivity state.

[0390] According to aspects of the present disclosure, a system for representing power source characteristics includes a digital twin datastore and one or more processors. The digital twin datastore includes an industrial-environment digital twin providing a digital twin of an industrial environment. The industrial-environment digital twin includes a power-source digital twin representing a power source supplying electrical energy to the industrial environment. The industrial-environment digital twin further includes real- world-element digital twins embedded therein. Each real-world-element digital twin corresponds to respective real-world elements disposed within the industrial environment. The one or more processors are configured to determine, in response to occurrence of a triggering condition, contemporaneous characteristics of the power source, and update, in response to determining the contemporaneous characteristics of the power source, the power-source digital twin to represent the contemporaneous characteristics.

[0391] In embodiments, the contemporaneous characteristics of the power source include a power factor delivered to the industrial environment.

[0392] In embodiments, the contemporaneous characteristics of the power source include a power quality.

[0393] In embodiments, the contemporaneous characteristics of the power source include a utility frequency.

[0394] In embodiments, the one or more processors are further configured to simulate, via a digital twin simulation system, one or more operating parameters for the real-world elements in response to the industrial environment is supplied with the contemporaneous characteristics using the real-world-element digital twins, calculate, in response to the one or more operating parameters falling outside of respective design parameters, a mitigating action to be taken by one or more of the real-world elements in response to being supplied with the contemporaneous characteristics via the digital twin simulation system, and actuate, in response to detecting the contemporaneous characteristics of the power source, the mitigating action.

[0395] In embodiments, the simulation and the calculation are performed prior to determining the contemporaneous characteristics.

[0396] In embodiments, the mitigating action includes actuating one of an inductive circuit or a capacitive circuit operatively coupled between the power source and the real-world elements.

[0397] In embodiments, the mitigating action includes actuating a second power source to provide power to one or more of the real-world elements. The second power source is disposed within the industrial environment,

[0398] In embodiments, the second power source is a backup power source that is integral with another of the real-world elements.

[0399] Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the disclosure, BRIEF DESCRIPTION OF THE FIGURES

[0400] Figures 1 through Figure 5 are diagrammatic views that each depicts portions of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system in accordance with the present disclosure.

[0401] Figure 6 is a diagrammatic view of a platform including a local data collection system disposed in an industrial environment for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements in accordance with the present disclosure.

[0402] Figure 7 is a diagrammatic view that depicts elements of an industrial data collection system for collecting analog sensor data in an industrial environment in accordance with the present disclosure.

[0403] Figure 8 is a diagrammatic view of a rotating or oscillating machine having a data acquisition module that is configured to collect waveform data in accordance with the present disclosure.

[0404] Figure 9 is a diagrammatic view of an exemplary tri-axial sensor mounted to a motor bearing of an exemplary rotating machine in accordance with the present disclosure.

[0405] Figure 10 is a diagrammatic view of components and interactions of a data collection architecture involving application of cognitive and machine learning systems to data collection and processing in accordance with the present disclosure.

[0406] Figure 11 is a diagrammatic view of components and interactions of a data collection architecture involving application of a platform having a cognitive data marketplace in accordance with the present disclosure.

[0407] Figure 12 is a diagrammatic view of components and interactions of a data collection architecture involving application of a self-organizing swarm of data collectors in accordance with the present disclosure.

[0408] Figure 13 is a diagrammatic view of components and interactions of a data collection architecture involving application of a haptic user interface in accordance with the present disclosure.

[0409] Figure 14 is a diagrammatic view of a multi-format streaming data collection system in accordance with the present disclosure.

[0410] Figure 15 is a diagrammatic view of combining legacy and streaming data collection and storage in accordance with the present disclosure.

[0411] Figure 16 is a diagrammatic view of industrial machine sensing using both legacy and updated streamed sensor data processing in accordance with the present disclosure.

[0412] Figure 17 is a diagrammatic view of an industrial machine sensed data processing system that facilitates portal algorithm use and alignment of legacy and streamed sensor data in accordance with the present disclosure.

[0413] Figure 18 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure,

[0414] Figure 19 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument having an alarms module, expert analysis module, and a driver API to facilitate communication with a cloud network facility in accordance with the present disclosure.

[0415] Figure 20 is a diagrammatic view of components and interactions of a data collection architecture involving a streaming data acquisition instrument and first in, first out memory architecture to provide a real time operating system in accordance with the present disclosure.

[0416] Figure 21 is a diagrammatic view of components and interactions of a data collection architecture involving a multiple streaming data acquisition instrument receiving analog sensor signals and digitizing those signals to be obtained by a streaming hub server in accordance with the present disclosure,

[0417] Figure 22 is a diagrammatic view of components and interactions of a data collection architecture involving a master raw data server that processes new streaming data and data already extracted and processed in accordance with the present disclosure.

[0418] Figure 23, Figure 24, and Figure 25 are diagrammatic views of components and interactions of a data collection architecture involving a processing, analysis, report, and archiving server that processes new streaming data and data already extracted and processed in accordance with the present disclosure.

[0419] Figure 26 is a diagrammatic view of components and interactions of a data collection architecture involving a relation database server and data archives and their connectivity with a cloud network facility in accordance with the present disclosure.

[0420] Figure 27 through Figure 32 are diagrammatic views of components and interactions of a data collection architecture involving a virtual streaming data acquisition instrument receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0421] Figure 33 through Figure 40 are diagrammatic views of components and interactions of a data collection architecture involving data channel methods and systems for data collection of industrial machines in accordance with the present disclosure.

[0422] Figure 41 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0423] Figure 42 and Figure 43 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0424] Figure 44 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0425] Figures 45 and 46 are diagrammatic views that depict an embodiment of a system for data collection in accordance with the present disclosure.

[0426] Figures 47 and 48 are diagrammatic views that depict an embodiment of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0427] Figure 49 depicts an embodiment of a data monitoring device incorporating sensors in accordance with the present disclosure.

[0428] Figures 50 and 51 are diagrammatic views that depict embodiments of a data monitoring device in communication with external sensors in accordance with the present disclosure.

[0429] Figure 52 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0430] Figure 53 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0431] Figure 54 is a diagrammatic view that depicts embodiments of a data monitoring device with additional detail in the signal evaluation circuit in accordance with the present disclosure.

[0432] Figure 55 is a diagrammatic view that depicts embodiments of a system for data collection in accordance with the present disclosure.

[0433] Figure 56 is a diagrammatic view that depicts embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0434] Figure 57 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0435] Figures 58 and 59 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0436] Figures 60 and 61 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0437] Figures 62 and 63 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0438] Figures 64 and 65 is a diagrammatic view that depicts embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0439] Figure 66 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0440] Figures 67 and 68 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0441] Figure 69 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0442] Figure 70 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0443] Figures 71 and 72 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0444] Figures 73 and 74 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0445] Figure 75 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0446] Figures 76 and 77 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0447] Figure 78 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0448] Figures 79 and 80 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0449] Figures 81 and 82 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0450] Figure 83 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0451] Figures 84 and 85 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0452] Figure 86 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0453] Figures 87 and 88 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0454] Figures 89 and 90 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0455] Figure 91 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0456] Figures 92 and 93 are diagrammatic views that depict embodiments of a data monitoring device in accordance with the present disclosure.

[0457] Figure 94 is a diagrammatic view that depicts embodiments of a data monitoring device in accordance with the present disclosure.

[0458] Figures 95 and 96 are diagrammatic views that depict embodiments of a system for data collection in accordance with the present disclosure.

[0459] Figures 97 and 98 are diagrammatic views that depict embodiments of a system for data collection comprising a plurality of data monitoring devices in accordance with the present disclosure.

[0460] Figures 99 through Figure 101 are diagrammatic views of components and interactions of a data collection architecture involving a collector of route templates and the routing of data collectors in an industrial environment in accordance with the present disclosure.

[0461] Figure 102 is a diagrammatic view that depicts a monitoring system that employs data collection bands in accordance with the present disclosure.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0575] Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.

[0576] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate with existing data collection, processing, and storage systems while preserving access to existing format / frequency range / resolution compatible data. While the industrial machine sensor data streaming facilities described herein may collect a greater volume of data (e.g., longer duration of data collection) from sensors at a wider range of frequencies and with greater resolution than existing data collection systems, methods and systems may be employed to provide access to data from the stream of data that represents one or more ranges of frequency and / or one or more lines of resolution that are purposely compatible with existing systems. Further, a portion of the streamed data may be identified, extracted, stored, and / or forwarded to existing data processing systems to facilitate operation of existing data processing systems that substantively matches operation of existing data processing systems using existing collection-based data. In this way, a newly deployed system for sensing aspects of industrial machines, such as aspects of moving parts of industrial machines, may facilitate continued use of existing sensed data processing facilities, algorithms, models, pattern recognizers, user interfaces, and the like.

[0577] Through identification of existing frequency ranges, formats, and / or resolution, such as by accessing a data structure that defines these aspects of existing data, higher resolution streamed data may be configured to represent a specific frequency, frequency range, format, and / or resolution. This configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data. One approach to adapting streamed data for compatibility with existing sensed data may include aligning the streamed data with existing data so that portions of the streamed data that align with the existing data can be extracted, stored, and made available for processing with existing data processing methods. Alternatively, data processing methods may be configured to process portions of the streamed data that correspond, such as through alignment, to the existing data, with methods that implement functions substantially similar to the methods used to process existing data, such as methods that process data that contain a particular frequency range or a particular resolution and the like.

[0578] 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.

[0579] Figures 1 through 5 depict portions of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system 10. Figure 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. This allows the industrial environment to use the benefits of networking and control technologies, while also providing security, such as preventing cyber-attacks. The MANET 20 may use cognitive radio technologies 40, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In certain embodiments, the system depicted in Figures 1 through 5 provides network-sensitive or network-aware transport of data over the network to and from a data collection device or a heavy industrial machine.

[0580] Figures 3-4 depict intelligent data collection technologies deployed locally, at the edge of an IoT deployment, where heavy industrial machines are located. This includes various sensors 52, IoT devices 54, data storage capabilities (e.g., data pools 60, or distributed ledger 62) (including intelligent. self-organizing storage), sensor fusion (including self-organizing sensor fusion), and the like. Interfaces for data collection, including multi-sensory interfaces, tablets, smartphones 58, and the like are shown. Figure 3 also shows data pools 60 that may collect data published by machines or sensors that detect conditions of machines, such as for later consumption by local or remote intelligence. A distributed ledger system 62 may distribute storage across the local storage of various elements of the environment, or more broadly throughout the system. Figure 4 also shows on-device sensor fusion 80, such as for storing on a device data from multiple analog sensors 82, which may be analyzed locally or in the cloud, such as by machine learning 84, including by training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success) when operating the methods and systems disclosed herein.

[0581] Figure 1 depicts a server based portion of an industrial IoT 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.

[0582] 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.

[0583] 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 IoT data collection, monitoring and control system 10 depicted in Figures 1-5. The platform 100 may include a network data transport system 108, such as for transporting data to and from the local data collection system 102 over a network 110, such as to a host processing system 112, such as one that is disposed in a cloud computing environment or on the premises of an enterprise, or that consists of distributed components that interact with each other to process data collected by the local data collection system 102. The host processing system 112, referred to for convenience in some cases as the host system 112, may include various systems, components, methods, processes, facilities, and the like for enabling automated, or automation-assisted processing of the data, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements in a local environment 104 or in a network 110. The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116, which may comprise information feeds and inputs from a wide array of sources, including those in the local environment 104, in a network 110, in the host system 112, or in one or more external systems, databases, or the like. The platform 100 may include one or more intelligent systems 118, which may be disposed in, integrated with, or acting as inputs to one or more components of the platform 100. Details of these and other components of the platform 100 are provided throughout this disclosure.

[0584] 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.

[0585] 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 tum, be used for processing by the host system 112 as inputs to components of the platform 100 and portions of the industrial IoT 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 leaming may use one or more models, rules, 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, workflows, or other structured or semi-structured understandings is disclosed in U.S. Patent Number 8,200,775 to Moore, issued 12 June 2012, and hereby incorporated by reference as if fully set forth herein. Machine learning may be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, or the like (such as changing a function within a model) based on feedback (such as regarding the success of a model in a given situation) or based on iteration (such as in a recursive process). Where sufficient understanding of the underlying structure or behavior of a system is not known, insufficient data is not available, or in other cases where preferred for various reasons, machine learning may also be undertaken in the absence of an underlying model; that is, input sources may be weighted, structured, or the like within a machine learning facility without regard to any a priori understanding of structure, and outcomes (such as those based on measures of success at accomplishing various desired objectives) can be serially fed to the machine learning system to allow it to learn how to achieve the targeted objectives. For example, the system may learn to recognize faults, to recognize patterns, to develop models or functions, to develop rules, to optimize performance, to minimize failure rates, to optimize profits, to optimize resource utilization, to optimize flow (such as flow of traffic). or to optimize many other parameters that may be relevant to successful outcomes (such as outcomes in a wide range of environments). Machine learning may use genetic programming techniques, such as promoting or demoting one or more input sources, structures, 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 altemative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like. In embodiments, local machine learning may turn on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters, identification of a pattern (such as relating to a threat. a failure mode, a success mode, or the like) or the like. For example, a system may learn what sets of sensors should be turned on or off under given conditions to achieve the highest value utilization of a data collector 102. In embodiments, similar techniques may be used to handle optimization of transport of data in the platform 100 (such as in the network 110) by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring network security elements), and the like.

[0586] In embodiments, the local data collection system 102 may include a high- performance, multi-sensor data collector having a number of novel features for collection and processing of analog and other sensor data. In embodiments, a local data collection system 102 may be deployed to the industrial facilities depicted in Figure 3. A local data collection system 102 may also be deployed monitor other machines such as the machine 2200. The data collection system 102 may have on-board intelligent systems 118 (such as for learning to optimize the configuration and operation of the data collector, such as configuring permutations and combinations of sensors based on contexts and conditions). In one example, the data collection system 102 includes a crosspoint switch 130 or other analog switch. Automated, intelligent configuration of the local data collection system 102 may be based on a variety of types of information, such as information from various input sources, including those based on available power, power requirements of sensors, the value of the data collected (such as based on feedback information from other elements of the platform 100), the relative value of information (such as values based on the availability of other sources of the same or similar information), power availability (such as for powering sensors), network conditions, ambient conditions, operating states, operating contexts, operating events, and many others.

[0587] Figure 7 shows elements and sub-components of a data collection and analysis system 1100 for sensor data (such as analog sensor data) collected in industrial environments. As depicted in Figure 7, embodiments of the methods and systems disclosed herein may include hardware that has several different modules starting with the multiplexer (“MUX”) main board 1104. In embodiments, there may be a MUX option board 1108. The MUX 114 main board is where the sensors connect to the system. These connections are on top to enable ease of installation. Then there are numerous settings on the underside of this board as well as on the Mux option board 1108, which attaches to the MUX main board 1104 via two headers one at either end of the board. In embodiments, the Mux option board has the male headers, which mesh together with the female header on the main Mux board. This enables them to be stacked on top of each other taking up less real estate.

[0588] In embodiments, the main Mux board and / or the MUX option board then connects to the mother (e.g., with 4 simultaneous channels) and daughter (e.g., with 4 additional channels for 8 total channels) analog boards 1110 via cables where some of the signal conditioning (such as hardware integration) occurs. The signals then move from the analog boards 1110 to an anti-aliasing board (not shown) where some of the potential aliasing is removed. The rest of the aliasing removal is done on the delta sigma board 1112. The delta sigma board 1112 provides more aliasing protection along with other conditioning and digitizing of the signal. Next, the data moves to the Jennic™ board 1114 for more digitizing as well as communication to a computer via USB or Ethernet. In embodiments, the Jennic™ 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.

[0589] In embodiments, the system is meant to take in all types of data from volts to 4-20 mA signals, In embodiments, open formats of data storage and communication may be used. In some instances, certain portions of the system may be proprietary especially some of research and data associated with the analytics and reporting. In embodiments, smart band analysis is a way to break data down into easily analyzed parts that can be combined with other smart bands to make new more simplified vet sophisticated analytics. In embodiments, this unique information is taken and graphics are used to depict the conditions because picture depictions are more helpful to the user. In embodiments, complicated programs and user interfaces are simplified so that any user can manipulate the data like an expert.

[0590] In embodiments, the system in essence, works in a big loop. The system starts in software with a general user interface (“GUI”) 1124. In embodiments, rapid route creation may take advantage of hierarchical templates. In embodiments, a GUI is created so any general user can populate the information itself with simple templates. Once the templates are created the user can copy and paste whatever the user needs. In addition, users can develop their own templates for future ease of use and to institutionalize the knowledge. ‘When the user has entered all of the user’s information and connected all of the user’s sensors, the user can then start the system acquiring data.

[0591] 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 embodiments, a low-cost but efficient method is described for such protection without the need for external supplemental devices.

[0592] Typically, vibration data collectors are not designed to handle large input voltages due to the expense and the fact that, more often than not, it is not needed. A need exists for these data collectors to acquire many varied types of RPM data as technology improves and monitoring costs plummet. In embodiments, a method is using the already established OptoMOS™ technology which permits the switching up front of high voltage signals rather than using more conventional reed-relay approaches. Many historic concems 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 embodiments, printed circuit board routing topologies place all of the individual channel input circuitry as close to the input connector as possible. In embodiments, a unique electrostatic protection for trigger and vibration inputs may be placed upfront on the Mux and DAQ hardware in order to dissipate the built up electric charge as the signal passed from the sensor to the hardware. In embodiments, the Mux and analog board may support high-amperage input using a design topology comprising wider traces and solid state relays for upfront circuitry.

[0593] In some systems multiplexers are afterthoughts and the quality of the signal coming from the multiplexer is not considered. As aresult 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.

[0594] In embodiments, in addition to providing a better signal, the multiplexer may provide a continuous monitor alarming feature. Truly continuous systems monitor every sensor all the time but tend to be expensive. Typical multiplexer systems only monitor a set number of channels at one time and switch from bank to bank of a larger set of sensors. As a result, the sensors not being currently collected are not being monitored; if a level increases the user may never know. In embodiments, a multiplexer may have a continuous monitor alarming feature by placing circuitry on the multiplexer that can measure input channel levels against known alarm conditions even when the data acquisition (“DAQ”) is not monitoring the input. In embodiments, continuous monitoring Mux bypass offers a mechanism whereby channels not being currently sampled by the Mux system may be continuously monitored for significant alarm conditions via a number of trigger conditions using filtered peak-hold circuits or functionally similar that are in turn passed on to the monitoring system in an expedient manner using hardware interrupts or other means. This, in essence, makes the system continuously monitoring, although without the ability to instantly capture data on the problem like a true continuous system. In embodiments, coupling this capability to alarm with adaptive scheduling techniques for continuous monitoring and the continuous monitoring system’s software adapting and adjusting the data collection sequence based on statistics, analytics, data alarms and dynamic analysis may allow the system to quickly collect dynamic spectral data on the alarming sensor very soon after the alarm sounds.

[0595] Another restriction of typical multiplexers is that they may have a limited number of channels. In embodiments. use of distributed complex programmable logic device (“CPLD”) chips with dedicated bus for logic control of multiple Mux and data acquisition sections enables a CPLD to control multiple mux and DAQs so that there is no limit to the number of channels a system can handle. Interfacing to multiple types of predictive maintenance and vibration transducers requires a great deal of switching. This includes AC / DC coupling, 4-20 interfacing, integrated electronic piezoelectric transducer, channel power-down (for conserving op-amp power), single-ended or differential grounding options, and so on. Also required is the control of digital pots for range and gain control, switches for hardware integration, AA filtering and triggering. This logic can be performed by a series of CPLD chips strategically located for the tasks they control. A single giant CPLD requires long circuit routes with a great deal of density at the single giant CPLD. In embodiments, distributed CPLDs not only address these concerns but offer a great deal of flexibility. A bus is created where each CPLD that has a fixed assignment has its own unique device address. In embodiments, multiplexers and DAQs can stack together offering additional input and output channels to the system. For multiple boards (e.g., for multiple Mux boards), jumpers are provided for setting multiple addresses. In another example, three bits permit up to 8 boards that are jumper configurable. In embodiments, a bus protocol is defined such that each CPLD on the bus can either be addressed individually or as a group.

[0596] Typical multiplexers may be limited to collecting only sensors in the same bank. For detailed analysis, this may be limiting as there is tremendous value in being able to simultaneously review data from sensors on the same machine. Current systems using conventional fixed bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) that were assigned to a particular group at the time of installation. The only way to provide some flexibility is to either overlap channels or incorporate lots of redundancy in the system both of which can add considerable expense (in some cases an exponential increase in cost versus flexibility). The simplest Mux design selects one of many inputs and routes it into a single output line. A banked design would consist of a group of these simple building blocks, each handling a fixed group of inputs and routing to its respective output. Typically, the inputs are not overlapping so that the input of one Mux grouping cannot be routed into another. Unlike conventional Mux chips which typically switch a fixed group or banks of a fixed selection of channels into a single output (e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user to assign any input to any output. Previously, crosspoint multiplexers were used for specialized purposes such as RGB digital video applications and were as a practical matter too noisy for analog applications such as vibration analysis; however more recent advances in the technology now make it feasible. Another advantage of the crosspoint Mux is the ability to disable outputs by putting them into a high impedance state. This is ideal for an output bus so that multiple Mux cards may be stacked, and their output buses joined together without the need for bus switches,

[0597] In embodiments, this may be addressed by use of an analog crosspoint switch for collecting variable groups of vibration input channels and providing a matrix circuit so the system may access any set of eight channels from the total number of input sensors.

[0598] In embodiments, the ability to control multiple multiplexers with use of distributed CPLD chips with dedicated bus for logic control of multiple Mux and data acquisition sections is enhanced with a hierarchical multiplexer which allows for multiple DAQ to collect data from multiple multiplexers. A hierarchical Mux may allow modularly output of more channels, such as 16, 24 or more to multiple of eight channel card sets. In embodiments, this allows for faster data collection as well as more channels of simultaneous data collection for more complex analysis. In embodiments, the Mux may be configured slightly to make it portable and use data acquisition parking features, which turns SV3X DAQ into a protected system embodiment.

[0599] In embodiments, once the signals leave the multiplexer and hierarchical Mux they move to the analog board where there are other enhancements. In embodiments, power saving techniques may be used such as: power-down of analog channels when not in use; powering down of component boards; power-down of analog signal processing op-amps for non-selected channels ; powering down channels on the mother and the daughter analog boards. The ability to power down component boards and other hardware by the low-level firmware for the DAQ system makes high-level application control with respect to power-saving capabilities relatively easy. Explicit control of the hardware is always possible but not required by default. In embodiments, this power saving benefit may be of value to a protected system, especially if it is battery operated or solar powered.

[0600] In embodiments, in order to maximize the signal to noise ratio and provide the best data, a peak-detector for auto-scaling routed into a separate A / D will provide the system the highest peak in each set of data so it can rapidly scale the data to that peak. For vibration analysis purposes, the built-in A / D convertors in many microprocessors may be inadequate with regards to number of bits, number of channels or sampling frequency versus not slowing the microprocessor down significantly. Despite these limitations, it is useful to use them for purposes of auto-scaling. In embodiments, a separate A / D may be used that has reduced functionality and is cheaper. For each channel of input, after the signal is buffered (usually with the appropriate coupling: AC or DC) but before it is signal conditioned, the signal is fed directly into the microprocessor or low-cost A / D. Unlike the conditioned signal for which range, gain and filter switches are thrown, no switches are varied. This permits the simultaneous sampling of the auto-scaling data while the input data is signal conditioned, fed into a more robust external A / D, and directed into on-board memory using direct memory access (DMA) methods where memory is accessed without requiring a CPU. This significantly simplifies the auto-scaling process by not having to throw switches and then allow for settling time, which greatly slows down the auto-scaling process. Furthermore, the data may be collected simultaneously, which assures the best signal-to-noise ratio. The reduced number of bits and other features is usually more than adequate for auto-scaling purposes. In embodiments, improved integration using both analog and digital methods create an innovative hybrid integration which also improves or maintains the highest possible signal to noise ratio.

[0601] In embodiments, a section of the analog board may allow routing of a trigger channel, either raw or buffered, into other analog channels. This may allow a user to route the trigger to any of the channels for analysis and trouble shooting. Systems may have trigger channels for the purposes of determining relative phase between various input data sets or for acquiring significant data without the needless repetition of unwanted input. In embodiments, digitally controlled relays may be used to switch either the raw or buffered trigger signal into one of the input channels. It may be desirable to examine the quality of the triggering pulse because it may be corrupted for a variety of reasons including inadequate placement of the trigger sensor, wiring issues, faulty setup issues such as a dirty piece of reflective tape if using an optical sensor, and so on. The ability to look at either the raw or buffered signal may offer an excellent diagnostic or debugging vehicle. It also can offer some improved phase analysis capability by making use of the recorded data signal for various signal processing techniques such as variable speed filtering algorithms.

[0602] In embodiments, once the signals leave the analog board, the signals move into the delta-sigma board where precise voltage reference for A / D zero reference offers more accurate direct current sensor data. The delta sigma’s high speeds also provide for using higher input oversampling for delta-sigma A / D for lower sampling rate outputs to minimize antialiasing filter requirements. Lower oversampling rates can be used for higher sampling rates. For example, a 3% order AA filter set for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoff AA filter can then be used for Fmax ranges from 1 kHz and higher (with a secondary filter kicking in at 2.56x the highest sampling rate of 128 kHz). In embodiments, a CPLD may be used as a clock-divider for a delta-sigma A / D to achieve lower sampling rates without the need for digital resampling. In embodiments, a high-frequency crystal reference can be divided down to lower frequencies by employing a CPLD as a programmable clock divider. The accuracy of the divided down lower frequencies is even more accurate than the original source relative to their longer time periods. This also minimizes or removes the need for resampling processing by the delta-sigma A / D.

[0603] In embodiments, the data then moves from the delta-sigma board to the Jennic™ board where phase relative to input and trigger channels using on-board timers may be digitally derived. In embodiments, the Jennic™ board also has the ability to store calibration data and system maintenance repair history data in an on-board card set. In embodiments, the Jennic™ 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.

[0604] In embodiments, after the signal moves through the Jennic™ board it may then be transmitted to the computer. In embodiments, the computer software will be used to add intelligence to the system starting with an expert system GUI The GUI will offer a graphical expert system with simplified user interface for defining smart bands and diagnoses which facilitate anyone to develop complex analytics. In embodiments, this user interface may revolve around smart bands, which are a simplified approach to complex yet flexible analytics for the general user. In embodiments, the smart bands may pair with a self-learning neural network for an even more advanced analytical approach. In embodiments, this system may use the machine's hierarchy for additional analytical insight. One critical part of predictive maintenance is the ability to lean from known information during repairs or inspections. In embodiments, graphical approaches for back calculations may improve the smart bands and correlations based on a known fault or problem.

[0605] In embodiments, there is a smart route which adapts which sensors it collects simultaneously in order to gain additional correlative intelligence. In embodiments, smart operational data store (“ODS”) allows the system to elect to gather data to perform operational deflection shape analysis in order to further examine the machinery condition. In embodiments, adaptive scheduling techniques allow the system to change the scheduled data collected for full spectral analysis across a number (e.g. eight). of correlative channels. In embodiments, the system may provide data to enable extended statistics capabilities for continuous monitoring as well as ambient local vibration for analysis that combines ambient temperature and local temperature and vibration levels changes for identifying machinery issues.

[0606] In embodiments, a data acquisition device may be controlled by a personal computer (PC) to implement the desired data acquisition commands. In embodiments, the DAQ box may be self-sufficient. and can acquire, process, analyze and monitor independent of external PC control. Embodiments may include secure digital (SD) card storage. In embodiments, significant additional storage capability may be provided by utilizing an SD card. This may prove critical for monitoring applications where critical data may be stored permanently. Also, if a power failure should occur, the most recent data may be stored despite the fact that it was not off-loaded to another system.

[0607] A current trend has been to make DAQ systems as communicative as possible with the outside world usually in the form of networks including wireless. In the past it was common to use a dedicated bus to control a DAQ system with either a microprocessor or microcontroller / microprocessor paired with a PC. In embodiments, a DAQ system may comprise one or more microprocessor / microcontrollers, specialized microcontrollers / microprocessors, or dedicated processors focused primarily on the communication aspects with the outside world. These include USB, Ethernet and wireless with the ability to provide an IP address or addresses in order to host a webpage. All communications with the outside world are then accomplished using a simple text based menu. The usual array of commands (in practice more than a hundred) such as InitializeCard, AcquireData, StopAcquisition, RetrieveCalibration Info, and so on, would be provided.

[0608] In embodiments, intense signal processing activities including resampling, weighting, filtering, and spectrum processing may be performed by dedicated processors such as field-programmable gate array (“FPGAs”), digital signal processor (“DSP”), microprocessors, micro-controllers, or a combination thereof. In embodiments, this subsystem may communicate via a specialized hardware bus with the communication processing section. It will be facilitated with dual-port memory, semaphore logic, and so on. This embodiment will not only provide a marked improvement in efficiency but can significantly improve the processing capability, including the streaming of the data as well other high-end analytical techniques. This negates the need for constantly interrupting the main processes which include the control of the signal conditioning circuits, triggering, raw data acquisition using the A / D, directing the A / D output to the appropriate on-board memory and processing that data.

[0609] Embodiments may include sensor overload identification. A need exists for monitoring systems to identify when the sensor is overloading. There may be situations involving high-frequency inputs that will saturate a standard 100 mv / g sensor (which is most commonly used in the industry) and having the ability to sense the overload improves data quality for better analysis. A monitoring system may identify when their system is overloading, but in embodiments, the system may look at the voltage of the sensor to determine if the overload is from the sensor, enabling the user to get another sensor better suited to the situation, or gather the data again.

[0610] Embodiments may include radio frequency identification (“RFID”) and an inclinometer or accelerometer on a sensor so the sensor can indicate what machine / bearing it is attached to and what direction such that the software can automatically store the data without the user input. In embodiments, users could put the system on any machine or machines and the system would automatically set itself up and be ready for data collection in seconds.

[0611] 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 pattems that identify arcing, corona and other electrical issues indicating a break down or issue. Embodiments may include providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. In embodiments. an analysis engine may be used in ultrasonic online monitoring as well as identifying other faults by combining the ultrasonic data with other parameters such as vibration, temperature, pressure, heat flux, magnetic fields, electrical fields, currents, voltage, capacitance, inductance, and combinations (e.g., simple ratios) of the same, among many others.

[0612] Embodiments of the methods and systems disclosed herein may include use of an analog crosspoint switch for collecting variable groups of vibration input channels. For vibration analysis, it is useful to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or machines) in multiple directions. By obtaining the readings at the same time, for example, the relative phases of the inputs may be compared for the purpose of diagnosing various mechanical faults. Other types of cross channel analyses such as cross-correlation, transfer functions, Operating Deflection Shape (“ODS”) may also be performed.

[0613] Embodiments of the methods and systems disclosed herein may include precise voltage reference for A / D zero reference. Some A / D chips provide their own internal zero voltage reference to be used as a mid-scale value for external signal conditioning circuitry to ensure that both the A / D and external op-amps use the same reference. Although this sounds reasonable in principle, there are practical complications. In many cases these references are inherently based on a supply voltage using a resistor-divider. For many current systems, especially those whose power is derived from a PC via USB or similar bus, this provides for an unreliable reference, as the supply voltage will often vary quite significantly with load. This is especially true for delta-sigma A / D chips which necessitate increased signal processing. Although the offsets may drift together with load, a problem arises if one wants to calibrate the readings digitally. It is typical to modify the voltage offset expressed as counts coming from the A / D digitally to compensate for the DC drift. However, for this case, if the proper calibration offset is determined for one set of loading conditions, they will not apply for other conditions. An absolute DC offset expressed in counts will no longer be applicable. As a result, it becomes necessary to calibrate for all loading conditions which becomes complex, unreliable, and ultimately unmanageable. In embodiments, an external voltage reference is used which is simply independent of the supply voltage to use as the zero offset.

[0614] In embodiments, the system provides a phase-lock-loop band pass tracking filter method for obtaining slow-speed RPMs and phase for balancing purposes to remotely balance slow speed machinery, such as in paper mills, as well as offering additional analysis from its data. For balancing purposes, it is sometimes necessary to balance at very slow speeds. A typical tracking filter may be constructed based on a phase-lock loop or PLL design; however, stability and speed range are overriding concems. In embodiments, a number of digitally controlled switches are used for selecting the appropriate RC and damping constants. The switching can be done all automatically after measuring the frequency of the incoming tach signal. Embodiments of the methods and systems disclosed herein may include digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, digital phase derivation uses digital timers to ascertain an exact delay from a trigger event to the precise start of data acquisition. This delay, or offset, then, is further refined using interpolation methods to obtain an even more precise offset which is then applied to the analytically determined phase of the acquired data such that the phase is “in essence” an absolute phase with precise mechanical meaning useful for among other things, one-shot balancing, alignment analysis, and so on.

[0615] Embodiments of the methods and systems disclosed herein may include signal processing firmware / hardware. In embodiments, long blocks of data may be acquired at high-sampling rate as opposed to multiple sets of data taken at different sampling rates. Typically, in modern route collection for vibration analysis, it is customary to collect data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary from route point to point based on the specific mechanical analysis requirements at hand. For example, a motor may require a relatively low sampling rate with high resolution to distinguish running speed harmonics from line frequency harmonics. The practical trade-off here though is that it takes more collection time to achieve this improved resolution. In contrast, some 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 1980’s, the A / D sampling, digital storage, and computational abilities were not close to what they are today. so compromises were made between the time required for data collection and the desired resolution and accuracy. It was because of this limitation that some analysts in the field even refused to give up their analog tape recording systems, which did not suffer as much from these same digitizing drawbacks. A few hybrid systems were employed that would digitize the play back of the recorded analog data at multiple sampling rates and lengths desired, though these systems were admittedly less automated. The more common approach, as mentioned earlier, is to balance data collection time with analysis capability and digitally acquire the data blocks at multiple sampling rates and sampling lengths and digitally store these blocks separately. In embodiments, a long data length of data can be collected at the highest practical sampling rate (e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This long block of data can be acquired in the same amount of time as the shorter length of the lower sampling rates utilized by a priori methods so that there is no effective delay added to the sampling at the measurement point, always a concem in route collection. In embodiments, analog tape recording of data is digitally simulated with such a precision that it can be in effect considered continuous or “analog” for many purposes, including for purposes of embodiments of the present disclosure, except where context indicates otherwise.

[0616] Embodiments of the methods and systems disclosed herein may include storage of calibration data and maintenance history on-board card sets. Many data acquisition devices which rely on interfacing to a PC to function store their calibration coefficients on the PC. This is especially true for complex data acquisition devices whose signal paths are many and therefore whose calibration tables can be quite large. In embodiments, calibration coefficients are stored in flash memory which will remember this data or any other significant information for that matter, for all practical purposes, permanently. This information may include nameplate information such as serial numbers of individual components, firmware or software version numbers, maintenance history, and the calibration tables. In embodiments, no matter which computer the box is ultimately connected to, the DAQ box remains calibrated and continues to hold all of this critical information. The PC or external device may poll for this information at any time for implantation or information exchange purposes.

[0617] Embodiments of the methods and systems disclosed herein may include rapid route creation taking advantage of hierarchical templates. In the field of vibration monitoring, as well as parametric monitoring in general, it is necessary to establish in a database or functional equivalent the existence of data monitoring points. These points are associated a variety of attributes including the following categories: transducer attributes, data collection settings, machinery parameters and operating parameters. The transducer attributes would include probe type. probe mounting type and probe mounting direction or axis orientation. Data collection attributes associated with the measurement would involve a sampling rate, data length, integrated electronic piezoelectric probe power and coupling requirements, hardware integration requirements, 4-20 or voltage interfacing, range and gain settings (if applicable), filter requirements, and so on. Machinery parametric requirements relative to the specific point would include such items as operating speed, bearing type, bearing parametric data which for a rolling element bearing includes the pitch diameter, number of balls, inner race, and outer-race diameters, For a tilting pad bearing, this would include the number of pads and so on. For measurement points on a piece of equipment such as a gearbox, needed parameters would include, for example, the number of gear teeth on each of the gears. For induction motors, it would include the number of rotor bars and poles; for compressors, the number of blades and / or vanes; for fans, the number of blades. For belt / pulley systems, the number of belts as well as the relevant belt- passing frequencies may be calculated from the dimensions of the pulleys and pulley center-to-center distance, For measurements near couplings, the coupling type and number of teeth in a geared coupling may be necessary, and so on. Operating parametric data would include operating load, which may be expressed in megawatts, flow (either air or fluid), percentage, horsepower, feet-per-minute, and so on. Operating temperatures both ambient and operational, pressures, humidity, and so on, may also be relevant. As can be seen, the setup information required for an individual measurement point can be quite large. It is also crucial to performing any legitimate analysis of the data. Machinery, equipment, and bearing specific information are essential for identifying fault frequencies as well as anticipating the various kinds of specific faults to be expected. The transducer attributes as well as data collection parameters are vital for properly interpreting the data along with providing limits for the type of analytical techniques suitable. The traditional means of entering this data has been manual and quite tedious, usually at the lowest hierarchical level (for example, at the bearing level with regards to machinery parameters), and at the transducer level for data collection setup information. It cannot be stressed enough, however, the importance of the hierarchical relationships necessary to organize data— both for analytical and interpretive purposes as well as the storage and movement of data. Here, we are focusing primarily on the storage and movement of data. By its nature, the aforementioned setup information is extremely redundant at the level of the lowest hierarchies; however, because of its strong hierarchical nature, it can be stored quite efficiently in that form. In embodiments, hierarchical nature can be utilized when copying data in the form of templates. As an example, hierarchical storage structure suitable for many purposes is defined from general to specific of company, plant or site, unit or process, machine, equipment, shaft element, bearing, and transducer. It is much easier to copy data associated with a particular machine, piece of equipment, shaft element or bearing than it is to copy only at the lowest transducer level. In embodiments, the system not only stores data in this hierarchical fashion. but robustly supports the rapid copying of data using these hierarchical templates. Similarity of elements at specific hierarchical levels lends itself to effective data storage in hierarchical format. For example, so many machines have common elements such as motors, gearboxes, compressors, belts, fans, and so on. More specifically, many motors can be easily classified as induction, DC, fixed or variable speed. Many gearboxes can be grouped into commonly occurring groupings such as input / output, input pinion / intermediate pinion / output pinion, 4-posters, and so on. Within a plant or company, there are many similar types of equipment purchased and standardized on for both cost and maintenance reasons. This results in an enormous overlapping of similar types of equipment and, as a result, offers a great opportunity for taking advantage of a hierarchical template approach.

[0618] Embodiments of the methods and systems disclosed herein may include smart bands. Smart bands refer to any processed signal characteristics derived from any dynamic input or group of inputs for the purposes of analyzing the data and achieving the correct diagnoses. Furthermore, smart bands may even include mini or relatively simple diagnoses for the purposes of achieving a more robust and complex one. Historically, in the field of mechanical vibration analysis, Alarm Bands have been used to define spectral frequency bands of interest for the purposes of analyzing and / or trending significant vibration patterns. The Alarm Band typically consists of a spectral (amplitude plotted against frequency) region defined between a low and high frequency border. The amplitude between these borders is summed in the same manner for which an overall amplitude is calculated. A Smart Band is more flexible in that it not only refers to a specific frequency band but can also refer to a group of spectral peaks such as the harmonics of a single peak, a true-peak level or crest factor derived from a time waveform, an overall derived from a vibration envelope spectrum or other specialized signal analysis technique or a logical combination (AND, OR, XOR, etc.) of these signal attributes. In addition, a myriad assortment of other parametric data, including system load, motor voltage and phase information, bearing temperature, flow rates. and the like, can likewise be used as the basis for forming additional smart bands. In embodiments, Smart Band symptoms may be used as building blocks for an expert system whose engine would utilize these inputs to derive diagnoses. Some of these mini-diagnoses may then in tum be used as Smart-Band symptoms (smart bands can include even diagnoses) for more generalized diagnoses.

[0619] 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 alist 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 tum feed a simplified weighted output to other neurons. The output of these neurons can be also classified as smart bands which in tum feed other neurons. This produces a more layered approach to expert diagnosing as opposed to the one-shot approach of a rule-based system. In embodiments, the expert system utilizes this neural approach using smart bands; however, it does not preclude rule- based diagnoses being reclassified as smart bands as further stimuli to be utilized by the expert system. From this point-of-view. it can be overviewed as a hybrid approach, although at the highest level it is essentially neural.

[0620] Embodiments of the methods and systems disclosed herein may include use of database hierarchy in analysis smart band symptoms and diagnoses may be assigned to various hierarchical database levels. For example, a smart band may be called “Looseness™ at the bearing level, trigger “Looseness” at the equipment level, and trigger “Looseness” at the machine level. Another example would be having a smart band diagnosis called “Horizontal Plane Phase Flip” across a coupling and generate a smart band diagnosis of “Vertical Coupling Misalignment” at the machine level.

[0621] Embodiments of the methods and systems disclosed herein may include expert system GUIs. In embodiments, the system undertakes a graphical approach to defining smart bands and diagnoses for the expert system. The entry of symptoms, rules, or more generally smart bands for creating a particular machine diagnosis, may be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring, The proposed graphical interface consists of four major components: a symptom parts bin, diagnoses bin, tools bin, and graphical wiring area (“GWA”). In embodiments, a symptom parts bin includes various spectral, waveform, envelope and any type of signal processing characteristic or grouping of characteristics such as a spectral peak, spectral harmonic, waveform true-peak, waveform crest-factor, spectral alarm band, and so on. Each part may be assigned additional properties. For example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a 1x, 2x, 3x running speed, 1x, 2x, 3x gear mesh, 1x, 2x. 3x blade pass, number of motor rotor bars x running speed, and so on.

[0622] In embodiments, the diagnoses bin includes various pre-defined as well as user- defined diagnoses such as misalignment, imbalance, looseness, bearing faults, and so on. Like parts, diagnoses may also be used as parts for the purposes of building more complex diagnoses. In embodiments, the tools bin includes logical operations such as AND, OR, XOR, etc. or other ways of combining the various parts listed above such as Find Max, Find Min, Interpolate, Average, other Statistical Operations, etc. In embodiments, a graphical wiring area includes parts from the parts bin or diagnoses from the diagnoses bin and may be combined using tools to create diagnoses. The various parts, tools and diagnoses will be represented with icons which are simply graphically wired together in the desired manner.

[0623] Embodiments of the methods and systems disclosed herein may include a graphical approach for back-calculation definition. In embodiments, the expert system also provides the opportunity for the system to lear. If one already knows that a unique set of stimuli or smart bands corresponds to a specific fault or diagnosis, then it is possible to back- calculate a set of coefficients that when applied to a future set of similar stimuli would arrive at the same diagnosis. In embodiments, if there are multiple sets of data, a best-fit approach may be used. Unlike the smart band GUI, this embodiment will self-generate a wiring diagram. In embodiments, the user may tailor the back-propagation approach settings and use a database browser to match specific sets of data with the desired diagnoses. In embodiments, the desired diagnoses may be created or custom tailored with a smart band GUI. In embodiments, after that, a user may press the GENERATE button and a dynamic wiring of the symptom-to-diagnosis may appear on the screen as it works through the algorithms to achieve the best fit. In embodiments, when complete, a variety of statistics are presented which detail how well the mapping process proceeded. In some cases, no mapping may be achieved if, for example, the input data was all zero or the wrong data (mistakenly assigned) and so on. Embodiments of the methods and systems disclosed herein may include bearing analysis methods. In embodiments, bearing analysis methods may be used in conjunction with a computer aided design (“CAD”), predictive deconvolution, minimum variance distortionless response (“MVDR™) and spectrum sum- of-harmonics.

[0624] In recent years, there has been a strong drive to save power which has resulted in an influx of variable frequency drives and variable speed machinery. In embodiments, a bearing analysis method is provided. In embodiments, torsional vibration detection and analysis is provided utilizing transitory signal analysis to provide an advanced torsional vibration analysis for a more comprehensive way to diagnose machinery where torsional forces are relevant (such as machinery with rotating components). Due primarily to the decrease in cost of motor speed control systems, as well as the increased cost and consciousness of energy-usage, it has become more economically justifiable to take advantage of the potentially vast energy savings of load control. Unfortunately, one frequently overlooked design aspect of this issue is that of vibration. When a machine is designed to run at only one speed, it is far easier to design the physical structure accordingly so as to avoid mechanical resonances both structural and torsional, each of which can dramatically shorten the mechanical health of a machine. This would include such structural characteristics as the types of materials to use, their weight, stiffening member requirements and placement, bearing types, bearing location, base support constraints, etc. Even with machines running at one speed, designing a structure so as to minimize vibration can prove a daunting task, potentially requiring computer modeling, finite-element analysis, and field testing. By throwing variable speeds into the mix, in many cases, it becomes impossible to design for all desirable speeds. The problem then becomes one of minimization, e.g., by speed avoidance. This is why many modern motor controllers are typically programmed to skip or quickly pass through specific speed ranges or bands. Embodiments may include identifying speed ranges in a vibration monitoring system. Non-torsional, structural resonances are typically fairly easy to detect using conventional vibration analysis techniques. However, this is not the case for torsion. One special area of current interest is the increased incidence of torsional resonance problems, apparently due to the increased torsional stresses of speed change as well as the operation of equipment at torsional resonance speeds. Unlike non-torsional structural resonances which generally manifest their effect with dramatically increased casing or external vibration, torsional resonances generally show no such effect. In the case of a shaft torsional resonance, the twisting motion induced by the resonance may only be discernible by looking for speed and / or phase changes. The current standard methodology for analyzing torsional vibration involves the use of specialized instrumentation. Methods and systems disclosed herein allow analysis of torsional vibration without such specialized instrumentation. This may consist of shutting the machine down and employing the use of strain gauges and / or other special fixturing such as speed encoder plates and / or gears. Friction wheels are another altemative, but they typically require manual implementation and a specialized analyst. In general, these techniques can be prohibitively expensive and / or inconvenient. An increasing prevalence of continuous vibration monitoring systems due to decreasing costs and increasing convenience (e.g., remote access) exists. In embodiments, there is an ability to discem torsional speed and / or phase variations with just the vibration signal. In embodiments, transient analysis techniques may be utilized to distinguish torsionally induced vibrations from mere speed changes due to process control. In embodiments, factors for discernment might focus on one or more of the following aspects: the rate of speed change due to variable speed motor control would be relatively slow, sustained and deliberate; torsional speed changes would tend to be short, impulsive and not sustained; torsional speed changes would tend to be oscillatory, most likely decaying exponentially, process speed changes would not; and smaller speed changes associated with torsion relative to the shaft’s rotational speed which suggest that monitoring phase behavior would show the quick or transient speed bursts in contrast to the slow phase changes historically associated with ramping a machine’s speed up or down (as typified with Bode or Nyquist plots).

[0625] Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When a signal is digitally integrated using software, essentially the spectral low-end frequency data has its amplitude multiplied by a function which quickly blows up as it approaches zero and creates what is known in the industry as a “ski-slope” effect. The amplitude of the ski-slope is essentially the noise floor of the instrument. The simple remedy for this is the traditional hardware integrator, which can perform at signal-to-noise ratios much greater than that of an already digitized signal. It can also limit the amplification factor to a reasonable level so that multiplication by very large numbers is essentially prohibited. However, at high frequencies where the frequency becomes large, the original amplitude which may be well above the noise floor is multiplied by a very small number (1 / f) that plunges it well below the noise floor. The hardware integrator has a fixed noise floor that although low floor does not scale down with the now lower amplitude high-frequency data. In contrast, the same digital multiplication of a digitized high-frequency signal also scales down the noise floor proportionally. In embodiments, hardware integration may be used below the point of unity gain where (at a value usually determined by units and / or desired signal to noise ratio based on gain) and software integration may be used above the value of unity gain to produce an ideal result. In embodiments, this integration is performed in the frequency domain, In embodiments, the resulting hybrid data can then be transformed back into a waveform which should be far superior in signal-to-noise ratio when compared to either hardware integrated or software integrated data. In embodiments, the strengths of hardware integration are used in conjunction with those of digital software integration to achieve the maximum signal-to-noise ratio. In embodiments, the first order gradual hardware integrator high pass filter along with curve fitting allow some relatively low frequency data to get through while reducing or eliminating the noise, allowing very useful analytical data that steep filters kill to be salvaged.

[0626] Embodiments of the methods and systems disclosed herein may include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is often performed with an up-front Mux whose purpose it is to select a few channels of data among many to feed the hardware signal processing, A / D, and processing components of a DAQ system. This is done primarily out of practical cost considerations. The tradeoff is that all of the points are not monitored continuously (although they may be monitored to a lesser extent via alternative hardware methods). In embodiments, multiple scheduling levels are provided. In embodiments, at the lowest level, which is continuous for the most part, all of the measurement points will be cycled through in round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 points, then each point is serviced once every 15 minutes; however, if a point should alarm by whatever criteria the user selects, its priority level can be increased so that it is serviced more often. As there can be multiple grades of severity for each alarm, so can there me multiple levels of priority with regards to monitoring. In embodiments, more severe alarms will be monitored more frequently. In embodiments, a number of additional high-level signal processing techniques can be applied at less frequent intervals. Embodiments may take advantage of the increased processing power of a PC and the PC can temporarily suspend the round-robin route collection (with its multiple tiers of collection) process and stream the required amount of data for a point of its choosing. Embodiments may include various advanced processing techniques such as envelope processing, wavelet analysis, as well as many other signal processing techniques. In embodiments, after acquisition of this data, the DAQ card set will continue with its route at the point it was interrupted. In embodiments, various PC scheduled data acquisitions will follow their own schedules which will be less frequency than the DAQ card route. They may be set up hourly, daily, by number of route cycles (for example, once every 10 cycles) and also increased scheduling-wise based on their alarm severity priority or type of measurement (e.g., motors may be monitored differently than fans).

[0627] Embodiments of the methods and systems disclosed herein may include data acquisition parking features. In embodiments, a data acquisition box used for route collection, real time analysis and in general as an acquisition instrument can be detached from its PC (tablet or otherwise) and powered by an external power supply or suitable battery. In embodiments, the data collector still retains continuous monitoring capability and its on-board firmware can implement dedicated monitoring functions for an extended period of time or can be controlled remotely for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical capabilities for continuous monitoring.

[0628] Embodiments of the methods and systems disclosed herein may include ambient sensing plus local sensing plus vibration for analysis. In embodiments, ambient environmental temperature and pressure, sensed temperature and pressure may be combined with long / medium term vibration analysis for prediction of any of a range of conditions or characteristics. Variants may add infrared sensing, infrared thermography, ultrasound, and many other types of sensors and input types in combination with vibration or with each other. Embodiments of the methods and systems disclosed herein may include a smart route. In embodiments, the continuous monitoring system’s software will adapt / adjust the data collection sequence based on statistics, analytics, data alarms and dynamic analysis. Typically, the route is set based on the channels the sensors are attached to. In embodiments, with the crosspoint switch, the Mux can combine any input Mux channels to the (e.g., eight) output channels. In embodiments, as channels go into alarm or the system identifies key deviations, it will pause the normal route set in the software to gather specific simultaneous data, from the channels sharing key statistical changes, for more advanced analysis. Embodiments include conducting a smart ODS or smart transfer function.

[0629] Embodiments of the methods and systems disclosed herein may include smart ODS and one or more transfer functions. In embodiments, due to a system's multiplexer and crosspoint switch, an ODS, a transfer function, or other special tests on all the vibration sensors attached to a machine / structure can be performed and show exactly how the machine’s points are moving in relationship to each other. In embodiments, 40-50 kHz and longer data lengths (e.g. at least one minute) may be streamed. which may reveal different information than what a normal ODS or transfer function will show. In embodiments, the system will be able to determine, based on the data / statistics / analytics to use, the smart route feature that breaks from the standard route and conducts an ODS across a machine, structure or multiple machines and structures that might show a correlation because the conditions / data directs it. In embodiments, for the transfer functions there may be an impact hammer used on one channel and then compared against other vibration sensors on the machine. In embodiments, the system may use the condition changes such as load, speed, temperature or other changes in the machine or system to conduct the transfer function. In embodiments, different transfer functions may be compared to each other over time. In embodiments, difference transfer functions may be strung together like a movie that may show how the machinery fault changes, such as a bearing that could show how it moves through the four stages of bearing failure and so on. Embodiments of the methods and systems disclosed herein may include a hierarchical Mux.

[0630] With reference to Figure 8, the present disclosure generally includes digitally collecting or streaming waveform data 2010 from a machine 2020 whose operational speed can vary from relatively slow rotational or oscillational speeds to much higher speeds in different situations. The waveform data 2010, at least on one machine, may include data from a single axis sensor 2030 mounted at an unchanging reference location 2040 and from a three-axis sensor 2050 mounted at changing locations (or located at multiple locations), including location 2052. In embodiments, the waveform data 2010 can be vibration data obtained simultaneously from each sensor 2030, 2050 in a gap-free format for a duration of multiple minutes with maximum resolvable frequencies sufficiently large to capture periodic and transient impact events. By way of this example, the waveform data 2010 can include vibration data that can be used to create an operational deflecting shape. It can also be used, as needed. to diagnose vibrations from which a machine repair solution can be prescribed.

[0631] In embodiments, the machine 2020 can further include a housing 2100 that can contain a drive motor 2110 that can drive a shaft 2120. The shaft 2120 can be supported for rotation or oscillation by a set of bearings 2130, such as including a first bearing 2140 and a second bearing 2150. A data collection module 2160 can connect to (or be resident on) the machine 2020. In one example, the data collection module 2160 can be located and accessible through a cloud network facility 2170, can collect the waveform data 2010 from the machine 2020, and deliver the waveform data 2010 to a remote location. A working end 2180 of the drive shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, adrill, 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.

[0632] In embodiments, the waveform data 2010 can be obtained using a predetermined route format based on the layout of the machine 2020. The waveform data 2010 may include data from the single axis sensor 2030 and the three-axis sensor 2050. The single- axis sensor 2030 can serve as a reference probe with its one channel of data and can be fixed at the unchanging location 2040 on the machine under survey. The three-axis sensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes) with its three channels of data and can be moved along a predetermined diagnostic route format from one test point to the next test point. In one example, both sensors 2030, 2050 can be mounted manually to the machine 2020 and can connect to a separate portable computer in certain service examples. The reference probe can remain at one location while the user can move the tri- axial vibration probe along the predetermined route, such as from bearing-to-bearing on a machine. In this example, the user is instructed to locate the sensors at the predetermined locations to complete the survey (or portion thereof) of the machine.

[0633] With reference to Figure 9, a portion of an exemplary machine 2200 is shown having a tri-axial sensor 2210 mounted to a location 2220 associated with a motor bearing of the machine 2200 with an output shaft 2230 and output member 2240 in accordance with the present disclosure.

[0634] 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,

[0635] With reference to Figure 8, the various embodiments include collecting the waveform data 2010 by digitally recording locally, or streaming over, the cloud network facility 2170. The waveform data 2010 can be collected so as to be gap-free with no interruptions and, in some respects. can be similar to an analog recording of waveform data. The waveform data 2010 from all of the channels can be collected for one to two minutes depending on the rotating or oscillating speed of the machine being monitored. In embodiments, the data sampling rate can be at a relatively high-sampling rate relative to the operating frequency of the machine 2020.

[0636] In embodiments, a second reference sensor can be used, and a fifth channel of data can be collected. As such, the single-axis sensor can be the first channel and tri-axial vibration can occupy the second, the third, and the fourth data channels. This second reference sensor, like the first, can be a single axis sensor, such as an accelerometer. In embodiments, the second reference sensor, like the first reference sensor, can remain in the same location on the machine for the entire vibration survey on that machine. The location of the first reference sensor (i.e., the single axis sensor) may be different than the location of the second reference sensors (i.e., another single axis sensor). In certain examples, the second reference sensor can be used when the machine has two shafts with different operating speeds, with the two reference sensors being located on the two different shafts. In accordance with this example, further single-axis reference sensors can be employed at additional but different unchanging locations associated with the rotating machine.

[0637] In embodiments, the waveform data can be transmitted electronically in a gap-free free format at a significantly high rate of sampling for a relatively longer period of time. In one example, the period of time is 60 seconds to 120 seconds. In another example, the rate of sampling is 100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will be appreciated in light of this disclosure that the waveform data can be shown to approximate more closely some of the wealth of data available from previous instances of analog recording of waveform data.

[0638] In embodiments, sampling, band selection, and filtering techniques can permit one or more portions of a long stream of data (i.e.. one to two minutes in duration) to be under sampled or over sampled to realize varying effective sampling rates. To this end, interpolation and decimation can be used to further realize varying effective sampling rates. For example, oversampling may be applied to frequency bands that are proximal to rotational or oscillational operating speeds of the sampled machine, or to harmonics thereof, as vibration effects may tend to be more pronounced at those frequencies across the operating range of the machine. In embodiments, the digitally-sampled data set can be decimated to produce a lower sampling rate. It will be appreciated in light of the disclosure that decimate in this context can be the opposite of interpolate. In embodiments, decimating the data set can include first applying a low-pass filter to the digitally-sampled data set and then undersampling the data set.

[0639] In one example, a sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz, but the remaining nine points of that portion of the waveform are effectively discarded and not included in the modeling of the sample waveform. Moreover, this type of bare undersampling can create ghost frequencies due to the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample waveform.

[0640] 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 (“sinc”) 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.

[0641] By way of the above example, the sample waveform at 100 Hz can be hardware- sampled at 10 Hz and therefore each sampling point is averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz can have each point averaged over 10 milliseconds). In contrast to the effective discarding of nine out of the ten data points of the sampled waveform as discussed above, the present disclosure can include weighing adjacent data. The adjacent data can refer to the sample points that were previously discarded and the one remaining point that was retained. In one example, a low pass filter can average the adjacent sample data linearly, i.e. determining the sum of every ten points and then dividing that sum by ten. In a further example. the adjacent data can be weighted with a sinc function. The process of weighting the original waveform with the sinc function can be referred to as an impulse function, or can be referred to in the time domain as a convolution.

[0642] The present disclosure can be applicable to not only digitizing a waveform signal based on a detected voltage, but can also be applicable to digitizing waveform signals based on current waveforms, vibration waveforms, and image processing signals including video signal rasterization. In one example, the resizing of a window on a computer screen can be decimated, albeit in at least two directions. In these further examples, it will be appreciated that undersampling by itself can be shown to be insufficient. To that end, oversampling or upsampling by itself can similarly be shown to be insufficient, such that interpolation can be used like decimation but in lieu of only undersampling by itself.

[0643] It will be appreciated in light of the disclosure that interpolation in this context can refer to first applying a low pass filter to the digitally-sampled waveform data and then upsampling the waveform data. It will be appreciated in light of the disclosure that real- world examples can often require the use of use non-integer factors for decimation or interpolation, or both. To that end, the present disclosure includes interpolating and decimating sequentially in order to realize a non-integer factor rate for interpolating and decimating. In one example, interpolating and decimating sequentially can define applying a low-pass filter to the sample waveform, then interpolating the waveform after the low- pass filter, and then decimating the waveform after the interpolation. In embodiments, the vibration data can be looped to purposely emulate conventional tape recorder loops, with digital filtering techniques used with the effective splice to facilitate longer analyses. It will be appreciated in light of the disclosure that the above techniques do not preclude waveform, spectrum, and other types of analyses to be processed and displayed with a GUI of the user at the time of collection. It will be appreciated in light of the disclosure that newer systems can permit this functionality to be performed in parallel to the high- performance collection of the raw waveform data.

[0644] With respect to time of collection issues, it will be appreciated that older systems using the compromised approach of improving data resolution, by collecting at different sampling rates and data lengths, do not in fact save as much time as expected. To that end, every time the data acquisition hardware is stopped and started. latency issues can be created, especially when there is hardware auto-scaling performed. The same can be true with respect to data retrieval of the route information (i.e., test locations) that is often in a database format and can be exceedingly slow. The storage of the raw data in bursts to disk (whether solid state or otherwise) can also be undesirably slow.

[0645] 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 1K (i.e., 1,024) can be used. In certain instances, 1K 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.

[0646] To improve accuracy, the waveform data can be averaged. Eight averages can be used with, for example, fifty percent overlap. This would extend the time from 800 milliseconds to 3.6 seconds, which is equal to 800 msec x 8 averages x 0.5 (overlap ratio) + 0.5 x 800 msec (non-overlapped head and tail ends). After collection at Fmax = 500 Hz waveform data, a higher sampling rate can be used. In one example, ten times (10x) the previous sampling rate can be used and Fmax = 10 kHz. By way of this example, eight averages can be used with fifty percent (50%) overlap to collect waveform data at this higher rate that can amount to a collection time of 360 msec or 0.36 seconds. It will be appreciated in light of the disclosure that it can be necessary to read the hardware collection parameters for the higher sampling rate from the route list, as well as permit hardware auto-scaling, or the resetting of other necessary hardware collection parameters, or both. To that end, a few seconds of latency can be added to accommodate the changes in sampling rate. In other instances, introducing latency can accommodate hardware autoscaling and changes to hardware collection parameters that can be required when using the lower sampling rate disclosed herein. In addition to accommodating the change in sampling rate, additional time is needed for reading the route point information from the database (i.e, where to monitor and where to monitor next), displaying the route information, and processing the waveform data. Moreover, display 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.

[0647] In further examples, additional sampling rates can be added but this can make the total amount time for the vibration survey even longer because time adds up from changeover time from one sampling rate to another and from the time to obtain additional data at different sampling rate. In one example, a lower sampling rate is used, such as a sampling rate of 128 Hz where Fmax = 50 Hz. By way of this example, the vibration survey would, therefore, require an additional 36 seconds for the first set of averaged data at this sampling rate, in addition to others mentioned above, and consequently the total time spent at each measurement point increases even more dramatically. Further embodiments include using similar digital streaming of gap free waveform data as disclosed herein for use with wind turbines and other machines that can have relatively slow speed rotating or oscillating systems. In many examples, the waveform data collected can include long samples of data at a relatively high-sampling rate. In one example, the sampling rate can be 100 kHz and the sampling duration can be for two minutes on all of the channels being recorded. In many examples, one channel can be for the single axis reference sensor and three more data channels can be for the tri-axial three channel sensor. Tt will be appreciated in light of the disclosure that the long data length can be shown to facilitate detection of extremely low frequency phenomena. The long data length can also be shown to accommodate the inherent speed variability in wind turbine operations. Additionally, the long data length can further be shown to provide the opportunity for using numerous averages such as those discussed herein, to achieve very high spectral resolution, and to make feasible tape loops for certain spectral analyses. Many multiple advanced analytical techniques can now become available because such techniques can use the available long uninterrupted length of waveform data in accordance with the present disclosure.

[0648] 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.

[0649] The present disclosure includes the use of at least one of the single-axis reference probe on one of the channels to allow for acquisition of relative phase comparisons between channels. The reference probe can be an accelerometer or other type of transducer that is not moved and, therefore, fixed at an unchanging location during the vibration survey of one machine. Multiple reference probes can each be deployed as at suitable locations fixed in place (i.e., at unchanging locations) throughout the acquisition of vibration data during the vibration survey. In certain examples, up to seven reference probes can be deployed depending on the capacity of the data collection module 2160 or the like. Using transfer functions or similar techniques, the relative phases of all channels may be compared with one another at all selected frequencies. By keeping the one or more reference probes fixed at their unchanging locations while moving or monitoring the other tri-axial vibration sensors, it can be shown that the entire machine can be mapped with regard to amplitude and relative phase. This can be shown to be true even when there are more measurement points than channels of data collection. With this information, an operating deflection shape can be created that can show dynamic movements of the machine in 3 D, which can provide an invaluable diagnostic tool. In embodiments, the one or more reference probes can provide relative phase, rather than absolute phase. It will be appreciated in light of the disclosure that relative phase may not be as valuable absolute phase for some purposes, but the relative phase the information can still be shown to be very useful.

[0650] In embodiments, the sampling rates used during the vibration survey can be digitally synchronized to predetermined operational frequencies that can relate to pertinent parameters of the machine such as rotating or oscillating speed. Doing this, permits extracting even more information using synchronized averaging techniques. It will be appreciated in light of the disclosure that this can be done without the use of a key phasor or areference pulse from a rotating shaft, which is usually not available for route collected data. As such, non-synchronous signals can be removed from a complex signal without the need to deploy synchronous averaging using the key phasor. This can be shown to be very powerful when analyzing a particular pinion in a gearbox or generally applied to any component within a complicated mechanical mechanism. In many instances, the key phasor or the reference pulse is rarely available with route collected data, but the techniques disclosed herein can overcome this absence. In embodiments, there can be multiple shafts running at different speeds within the machine being analyzed. In certain instances, there can be a single-axis reference probe for each shaft. In other instances, it is possible to relate the phase of one shaft to another shaft using only one single axis reference probe on one shaft at its unchanging location. In embodiments, variable speed equipment can be more readily analyzed with relatively longer duration of data relative to single speed equipment. The vibration survey can be conducted at several machine speeds within the same contiguous set of vibration data using the same techniques disclosed herein. These techniques can also permit the study of the change of the relationship between vibration and the change of the rate of speed that was not available before.

[0651] In embodiments, there are numerous analytical techniques that can emerge from because raw waveform data can be captured in a gap-free digital format as disclosed herein. The gap-free digital format can facilitate many paths to analyze the waveform data in many ways after the fact to identify specific problems. The vibration data collected in accordance with the techniques disclosed herein can provide the analysis of transient, semi-periodic and very low frequency phenomena. The waveform data acquired in accordance with the present disclosure can contain relatively longer streams of raw gap- free waveform data that can be conveniently played back as needed, and on which many and varied sophisticated analytical techniques can be performed. A large number of such techniques can provide for various forms of filtering to extract low amplitude modulations from transient impact data that can be included in the relatively longer stream of raw gap- free waveform data. It will be appreciated in light of the disclosure that in past data collection practices, these types of phenomena were typically lost by the averaging process of the spectral processing algorithms because the goal of the previous data acquisition module was purely periodic signals; or these phenomena were lost to file size reduction methodologies due to the fact that much of the content from an original raw signal was typically discarded knowing it would not be used.

[0652] In embodiments, there is a method of monitoring vibration of a machine having at least one shaft supported by a set of bearings, The method includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method also includes monitoring a second, third, and fourth data channel assigned to a three-axis sensor. The method further includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation; and determining a change in relative phase based on the digital waveform data. The method also includes the tri-axial sensor being located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors on all of their channels simultaneously.

[0653] The method also includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location of the reference sensor is a position associated with a shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with a shaft of the machine and, wherein, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine. The various embodiments include methods of sequentially monitoring vibration or similar process parameters and signals of a rotating or oscillating machine or analogous process machinery from a number of channels simultaneously, which can be known as an ensemble. In various examples, the ensemble can include one to eight channels. In further examples, an ensemble can represent a logical measurement grouping on the equipment being monitored whether those measurement locations are temporary for measurement, supplied by the original equipment manufacturer, retrofit at a later date, or one or more combinations thereof.

[0654] In one example, an ensemble can monitor bearing vibration in a single direction. In a further example, an ensemble can monitor three different directions (e.g., orthogonal directions) using a tri-axial sensor. In yet further examples, an ensemble can monitor four or more channels where the first channel can monitor a single axis vibration sensor, and the second, the third, and the fourth channels can monitor each of the three directions of the tri-axial sensor. In other examples, the ensemble can be fixed to a group of adjacent bearings on the same piece of equipment or an associated shaft. The various embodiments provide methods that include strategies for collecting waveform data from various ensembles deployed in vibration studies or the like in a relatively more efficient manner. The methods also include simultaneously monitoring of a reference channel assigned to an unchanging reference location associated with the ensemble monitoring the machine. The cooperation with the reference channel can be shown to support a more complete correlation of the collected waveforms from the ensembles. The reference sensor on the reference channel can be a single axis vibration sensor, or a phase reference sensor that can be triggered by a reference location on a rotating shaft or the like. As disclosed herein, the methods can further include recording gap-free digital waveform data simultaneously from all of the channels of each ensemble at a relatively high rate of sampling so as to include all frequencies deemed necessary for the proper analysis of the machinery being monitored while it is in operation. The data from the ensembles can be streamed gap-free to a storage medium for subsequent processing that can be connected to a cloud network facility, a local data link, Bluetooth™ connectivity, cellular data connectivity, or the like.

[0655] In embodiments, the methods disclosed herein include strategies for collecting data from the various ensembles including digital signal processing techniques that can be subsequently applied to data from the ensembles to emphasize or better isolate specific frequencies or waveform phenomena. This can be in contrast with current methods that collect multiple sets of data at different sampling rates, or with different hardware filtering configurations including integration, that provide relatively less post-processing flexibility because of the commitment to these same (known as a priori hardware configurations). These same hardware configurations can also be shown to increase time of the vibration survey due to the latency delays associated with configuring the hardware for each independent test. In embodiments, the methods for collecting data from various ensembles include data marker technology that can be used for classifying sections of streamed data as homogenous and belonging to a specific ensemble. In one example, a classification can be defined as operating speed. In doing so, a multitude of ensembles can be created from what conventional systems would collect as only one. The many embodiments include post-processing analytic techniques for comparing the relative phases of all the frequencies of interest not only between each channel of the collected ensemble but also between all of the channels of all of the ensembles being monitored, when applicable.

[0656] 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.

[0657] 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.

[0658] The present disclosure can also include dynamic markers that can correlate to data that can be derived from post processing and analytics performed on the sample waveform. In further embodiments, the dynamic markers can also correlate to post-collection derived parameters including RPMs, as well as other operationally derived metrics such as alarm conditions like a maximum RPM. In certain examples, many 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 more and more prevalent. It will also be appreciated in light of the disclosure that various signal processing techniques can permit the derivation of RPM from the raw data without the need for a dedicated tachometer signal.

[0659] In many embodiments, the RPM information can be used to mark segments of the raw waveform data over its collection history. Further embodiments include techniques for collecting instrument data following a prescribed route of a vibration study. The dynamic markers can enable analysis and trending software to utilize multiple segments of the collection interval indicated by the markers (e.g., two minutes) as multiple historical collection ensembles, rather than just one as done in previous systems where route collection systems would historically store data for only one RPM setting. This could, in turn, be extended to any other operational parameter such as load setting, ambient temperature, and the like, as previously described. The dynamic markers, however, that can be placed in a type of index file pointing to the raw data stream can classify portions of the stream in homogenous entities that can be more readily compared to previously collected portions of the raw data stream

[0660] The many embodiments include the hybrid relational metadata-binary storage approach that can use the best of pre-existing technologies for both relational and raw data streams. In embodiments, the hybrid relational metadata - binary storage approach can marry them together with a variety of marker linkages. The marker linkages can permit rapid searches through the relational metadata and can allow for more efficient analyses of the raw data using conventional SQL techniques with pre-existing technology. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional database technologies do not provide.

[0661] The marker linkages can also permit rapid and efficient storage of the raw data using conventional binary storage and data compression techniques. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional raw data technologies provide such as TMDS (National Instruments), UFF (Universal File Format such as UFF58), and the like. The marker linkages can further permit using the marker technology links where a vastly richer set of data from the ensembles can be amassed in the same collection time as more conventional systems. The richer set of data from the ensembles can store data snapshots associated with predetermined collection criterion and the proposed system can derive multiple snapshots from the collected data streams utilizing the marker technology. In doing so, it can be shown that a relatively richer analysis of the collected data can be achieved. One such benefit can include more trending points of vibration at a specific frequency or order of running speed versus RPM, load, operating temperature, flow rates, and the like, which can be collected for a similar time relative to what is spent collecting data with a conventional system,

[0662] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines, elements of the machines and the environment of the machines including heavy duty machines deployed at a local job site or at distributed job sites under common control. The heavy-duty machines may include earthmoving equipment, heavy duty on-road industrial vehicles, heavy duty off-road industrial vehicles. industrial machines deployed in various settings such as turbines, turbomachinery, generators, pumps, pulley systems, manifold and valve systems, and the like. In embodiments, heavy industrial machinery may also include earth- moving equipment, earth-compacting equipment, hauling equipment, hoisting equipment, conveying equipment, aggregate production equipment, equipment used in concrete construction, and piledriving equipment. In examples, earth moving equipment may include excavators, backhoes, loaders, bulldozers, skid steer loaders, trenchers, motor graders, motor scrapers, crawler loaders, and wheeled loading shovels. In examples, construction vehicles may include dumpers, tankers, tippers, and trailers. In examples, material handling equipment may include cranes, conveyors, forklift, and hoists. In examples, construction equipment may include tunnel and handling equipment, road rollers, concrete mixers, hot mix plants, road making machines (compactors), stone crashers, pavers, slurry seal machines, spraying and plastering machines, and heavy-duty pumps. Further examples of heavy industrial equipment may include different systems such as implement traction, structure, power train, control, and information. Heavy industrial equipment may include many different powertrains and combinations thereof to provide power for locomotion and to also provide power to accessories and onboard functionality. In each of these examples, the platform 100 may deploy the local data collection system 102 into the environment 104 in which these machines, motors, pumps, and the like, operate and directly connected integrated into each of the machines, motors, pumps, and the like.

[0663] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines in operation and machines in being constructed such as turbine and generator sets like Siemens™ SGT6- 5000F™ gas turbine, an SST-900™ steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator, and the like, In embodiments, the local data collection system 102 may be deployed to monitor steam turbines as they rotate in the currents caused by hot water vapor that may be directed through the turbine but otherwise generated from a different source such as from gas-fired burners, nuclear cores, molten salt loops and the like. In these systems, the local data collection system 102 may monitor the turbines and the water or other fluids in a closed loop cycle in which water condenses and is then heated until it evaporates again. The local data collection system 102 may monitor the steam turbines separately from the fuel source deployed to heat the water to steam. In examples, working temperatures of steam turbines may be between 500 and 650 °C. In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement.

[0664] 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.

[0665] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from water turbines serving as rotary engines that may harvest energy from moving water and are used for electric power generation. The type of water turbine or hy dro-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 s0, 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.

[0666] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid- renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources. In embodiments, elements in such systems may include transmission lines, heat exchangers, desulphurization scrubbers, pumps, coolers, recuperators, chillers, and the like. In embodiments, certain implementations of turbomachinery, turbines, scroll compressors, and the like may be configured in arrayed control so as to monitor large facilities creating electricity for consumption, providing refrigeration, creating steam for local manufacture and heating, and the like, and that arrayed control platforms may be provided by the provider of the industrial equipment such as Honeywell and their Experion™ PKS platform. In embodiments, the platform 100 may specifically communicate with and integrate the local manufacturer-specific controls and may allow equipment from one manufacturer to communicate with other equipment. Moreover, the platform 100 provides allows for the local data collection system 102 to collect information across systems from many different manufacturers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from marine industrial equipment, marine diesel engines, shipbuilding, oil and gas plants, refineries, petrochemical plant, ballast water treatment solutions, marine pumps and turbines, and the like.

[0667] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from heavy industrial equipment and processes including monitoring one or more sensors. By way of this example, sensors may be devices that may be used to detect or respond to some type of input from a physical environment, such as an electrical, heat, or optical signal. In embodiments, the local data collection system 102 may include multiple sensors such as, without limitation, a temperature sensor, a pressure sensor, a torque sensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, a radiation sensor, a position sensor, an acceleration sensor, a strain sensor, a pressure Cycle sensor, a pressure sensor, an air temperature sensor, and the like. The torque sensor may encompass a magnetic twist angle sensor. In one example, the torque and speed sensors in the local data collection system 102 may be similar to those discussed in U.S. Patent Number 8,352,149 to Meachem, issued 8 January 2013 and hereby incorporated by reference as if fully set forth herein. In embodiments, one or more sensors may be provided such as a tactile sensor, a biosensor, a chemical sensor, an image sensor, a humidity sensor, an inertial sensor, and the like.

[0668] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors that may provide signals for fault detection including excessive vibration, incorrect material, incorrect material properties, trueness to the proper size, trueness to the proper shape, proper weight, trueness to balance. Additional fault sensors include those for inventory control and for inspections such as to confirm that parts are packaged to plan, parts are to tolerance in a plan, occurrence of packaging damage or stress, and sensors that may indicate the occurrence of shock or damage in transit. Additional fault sensors may include detection of the lack of lubrication, over lubrication, the need for cleaning of the sensor detection window, the need for maintenance due to low lubrication, the need for maintenance due to blocking or reduced flow in a lubrication region, and the like.

[0669] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 that includes aircraft operations and manufacture including monitoring signals from sensors for specialized applications such as sensors used in an aircraft's Attitude and Heading Reference System (AHRS), such as gyroscopes, accelerometers, and magnetometers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from image sensors such as semiconductor charge coupled devices (CCDs), active pixel sensors, in complementary metal-oxide-semiconductor (CMOS) or N-type metal-oxide- semiconductor (NMOS, Live MOS) technologies. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as an infra-red (IR) sensor, an ultraviolet (UV) sensor, a touch sensor, a proximity sensor, and the like. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for optical character recognition (OCR), reading barcodes, detecting surface acoustic waves, detecting transponders, communicating with home automation systems, medical diagnostics, health monitoring, and the like.

[0670] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as a Micro-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.

[0671] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines such as turbines, windmills, industrial vehicles, robots, and the like. These large mechanical machines include multiple components and elements providing multiple subsystems on each machine. To that end, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from individual elements such as axles, bearings, belts, buckets, gears, shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets, sleeves, valves, wheels, actuators, motors, servomotor, and the like. Many of the machines and their elements may include servomotors. The local data collection system 102 may monitor the motor, the rotary encoder, and the potentiometer of the servomechanism to provide three-dimensional detail of position, placement, and progress of industrial processes.

[0672] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from gear drives, powertrains, transfer cases, multispeed axles, transmissions, direct drives, chain drives, belt-drives, shaft-drives, magnetic drives, and similar meshing mechanical drives. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from fault conditions of industrial machines that may include overheating, noise, grinding gears, locked gears, excessive vibration, wobbling, under-inflation, over- inflation, and the like. Operation faults, maintenance indicators, and interactions from other machines may cause maintenance or operational issues may occur during operation, during installation, and during maintenance. The faults may occur in the mechanisms of the industrial machines but may also occur in infrastructure that supports the machine such as its wiring and local installation platforms. In embodiments, the large industrial machines may face different types of fault conditions such as overheating, noise, grinding gears, excessive vibration of machine parts. fan vibration problems. problems with large industrial machines rotating parts.

[0673] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from industrial machinery including failures that may be caused by premature bearing failure that may occur due to contamination or loss of bearing lubricant. In another example, a mechanical defect such as misalignment of bearings may occur. Many factors may contribute to the failure such as metal fatigue, therefore, the local data collection system 102 may monitor cycles and local stresses. By way of this example, the platform 100 may monitor the incorrect operation of machine parts, lack of maintenance and servicing of parts, corrosion of vital machine parts, such as couplings or gearboxes, misalignment of machine parts, and the like. Though the fault occurrences cannot be completely stopped, many industrial breakdowns may be mitigated to reduce operational and financial losses. The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments wherein it has been shown to present a 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 the element or machine. To that end, the platform 10 may provide reminders of, or perform some, preventive measures such as adhering to operating manual and mode instructions for machines, proper lubrication, and maintenance of machine parts, minimizing or eliminating overrun of machines beyond their defined capacities, replacement of worn but still functional parts as needed, properly training the personnel for machine use, and the like.

[0674] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by a plurality of physical, electronic, and symbolic formats or signals. The platform 100 may employ signal processing including a plurality of mathematical, statistical, computational, heuristic, and linguistic representations and processing of signals and a plurality of operations needed for extraction of useful information from signal processing operations such as techniques for representation, modeling, analysis, synthesis, sensing, acquisition, and extraction of information from signals. In examples, signal processing may be performed using a plurality of techniques, including but not limited to transformations, spectral estimations, statistical operations, probabilistic and stochastic operations, numerical theory analysis. data mining, and the like. The processing of various types of signals forms the basis of many electrical or computational process. As a result, signal processing applies to almost all disciplines and applications in the industrial environment such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, quality improvements such as noise reduction, image enhancement, and the like. Signal processing for images may include pattem 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 pattem recognition in various high precision applications. The platform 100 may use pattern recognition in face detection related applications such as security systems, tracking, sports related applications, fingerprint analysis, medical and forensic applications, navigation and guidance systems, vehicle tracking, public infrastructure systems such as transport systems, license plate monitoring, and the like.

[0675] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 using machine learning to enable derivation-based learning outcomes from computers without the need to program them. The platform 100 may, therefore, learn from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include presenting a set of example inputs and desired outputs to the machine learning systems. Unsupervised learning may include the learning algorithm itself structuring its input by methods such as pattern detection and / or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions. In examples. machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression. clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support ve...

Claims

CLAIMS ‘What is claimed is: 1 A method for updating one or more properties of one or more digital twins comprising: receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models: obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models. 2 The method of claim 1, wherein the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

3. The method of claim 1, wherein the request is received from a client application that supports an Industrial Internet of Things sensor system. 4, The method of claim 1, wherein the digital twins are digital twins of at least one of industrial entities and industrial environments. §. The method of claim 1. wherein the one or more dynamic models take data selected from the set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, velocity, acceleration, lighting level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

6. The method of claim 1, wherein the selected data sources include an Internet of Things connected device. % The method of claim 1, wherein the selected data sources include a machine vision system.

8. The method of claim 1, wherein retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties that are depicted in digital twins indicated by the request and a respective type of the one or more digital twins.

9. The method of claim 9, wherein the one or more dynamic models are identified using a lookup table.

10. A method comprising: receiving imported data from one or more data sources, the imported data corresponding to an industrial environment; generating an environment digital twin representing the industrial environment based on the imported data; identifying one or more industrial entities within the industrial environment; generating a set of discrete digital twins representing the one or more industrial entities within the environment; embedding the set of discrete digital twins within the environment digital twin; establishing a connection with a sensor system of the industrial environment; receiving real-time sensor data from one or more sensors of the sensor system via the connection; and updating at least one of the environment digital twin and the set of discrete digital twins based on the real-time sensor data.

11. The method of claim 10, wherein the connection with the sensor system is established via one of a webhook and an application programming interface (API).

12. The method of claim 10, wherein the environmental digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual ‘manner.

13. The method of claim 12, further comprising outputting the visual digital twins to a client application that displays the visual digital twins via a virtual reality headset.

14. The method of claim 12, further comprising outputting the visual digital twins to a client application that displays the visual digital twins via a display device of a user device.

15. The method of claim 12, further comprising outputting the visual digital twins to a client application that displays the visual digital twins via an augmented reality-enabled device.

16. The method of claim 10, further comprising; receiving user input relating to one or more steps performed in an industrial process relating to the industrial environment; and generating a process digital twin that defines the steps of the industrial process with respect to the industrial environment and one or more of the set of industrial entities.

17. The method of claim 10, further comprising instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the environment digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins.

18. The method of claim 17, wherein each edge represents a relationship between two respective digital twins.

19. The method of claim 18, wherein embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective industrial entity represented by the respective discrete digital twin and the industrial environment.

20. The method of claim 18, wherein each edge represents a spatial relationship between two respective digital twins, and an operational relationship between two respective digital twins, 21. The method of claim 18, wherein each edge stores metadata corresponding to the relationship between the two respective digital twins, 22. The method of claim 17, wherein each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node. 23, The method of claim 17, wherein each entity node of the one or more entity nodes includes one or more behaviors of a respective properties of the respective industrial entity represented by the entity node. 24, The method of claim 17, wherein the environment node includes one or more properties of the environment.

25. The method of claim 17, wherein the environment node includes one or more behaviors of the environment.

26. The method of claim 10, further comprising executing a simulation based on the environment digital twin and the one or more discrete digital twins.

27. The method of claim 26, wherein the simulation simulates one of an operation of a machine in the industrial environment that produces an output based on a set of inputs and movement of workers in the industrial environment.

28. The method of claim 10, wherein the imported data includes a three-dimensional scan of the environment.

29. The method of claim 10, wherein the imported data includes a LIDAR scan of industrial the environment.

30. The method of claim 10, wherein generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment.

31. The method of claim 10, wherein generating the set of discrete digital twins includes importing a predefined digital twin of an industrial entity from a manufacturer of the industrial entity, wherein the predefined digital twin includes properties and behaviors of the industrial entity. 32, The method of claim 10, wherein generating the set of discrete digital twins includes classifying an industrial entity within the imported data of the industrial environment and generating a discrete digital twin corresponding to the classified industrial entity.

33. A system for monitoring interaction within an industrial environment, the system comprising: a digital twin datastore including data collected by a set of proximity sensors disposed within an industrial environment, the data including location data indicating respective locations of a plurality of elements within the industrial environment; and one or more processors configured to: maintain, via the digital twin datastore, an industrial-environment digital twin for the industrial environment; receive signals indicating actuation of at least one proximity sensor within the set of proximity sensors by a real-world element from the plurality of elements; collect, in response to actuation of the at least one proximity sensor, updated location data for the real-world element using the at least one proximity sensor; and update the industrial-environment digital twin within the digital twin datastore to include the updated location data.

34. The system of claim 33, wherein each of the set of proximity sensors is configured to detect a device associated with the user.

35. The system of claim 34, wherein the device is a wearable device and an RFID device.

36. The system of claim 33, wherein each element of the plurality of elements is a mobile element.

37. The system of claim 33, wherein each element of the plurality of elements is a respective worker.

38. The system of claim 33, wherein the plurality of elements includes mobile equipment elements and workers. mobile-equipment-position data is determined using data transmitted by the respective mobile equipment element, and worker-position data is determined using data obtained by the system.

39. The system of claim 38, wherein the worker-position data is determined using information transmitted from a device associated with a respective worker.

40. The system of claim 33, wherein the actuation of the at least one proximity sensor occurs in response to interaction between the respective worker and the proximity sensor.

41. The system of claim 33, wherein the actuation of the at least one proximity sensor occurs in response to interaction between a worker and a respective at least one proximity-sensor digital twin corresponding to the at least one proximity sensor.

42. The system of claim 33, wherein the one or more processors collect updated location data for the plurality of elements using the set of proximity sensors in response to actuation of the at least one proximity sensor.

43. A system for modeling moving elements for an industrial digital twin, the system comprising: a digital twin datastore storing an industrial-environment digital twin corresponding to an industrial element, the industrial-environment digital twin including real-world-element digital twins embedded therein, wherein each real-world-element digital twin corresponds to a respective real-world element that is disposed within the industrial environment, the real-world-element digital twins including mobile-element digital twins that respectively correspond to a respective mobile element within the industrial environment; and one or more processors configured to: for each mobile element: determine whether the mobile element is in motion; and obtain path information from the mobile element, and model, in response to obtaining the path information for each mobile element, traffic within the industrial environment via a digital twin simulation system.

44. The system of claim 43, wherein the path information is obtained from a navigation module of the mobile element.

45. The system of claim 43, wherein the one or more processors are further configured to obtain the path information by: detecting, using a plurality of sensors within the industrial environment, movement of the mobile element; obtaining a destination for the mobile element; calculating, using the plurality of sensors within the industrial environment, an optimized path for the mobile element; and instructing the mobile element to navigate the optimized path.

46. The system of claim 45, wherein the optimized path includes path information for other mobile elements within the real-world elements and the optimized path minimizes interactions between mobile elements and humans within the industrial environment.

47. The system of claim 45, wherein the mobile elements include autonomous vehicles and non-autonomous vehicles and the optimized path reduces interactions of the autonomous vehicles with the non-autonomous vehicles.

48. The system of claim 43, wherein the traffic modeling includes use of a particle traffic model, a trigger-response mobile-element-following traffic model, a macroscopic traffic model. a microscopic traffic model, a submicroscopic traffic model, a mesoscopic traffic model, or a combination thereof.

49. A method for updating one or more vibration fault level states of one or more digital twins comprising: receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request, wherein the one or more dynamic models include a dynamic model that predicts when a vibration fault level occurs based on an input dataset; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models: obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more vibration fault level states of the one or more digital twins based on the output of the one or more dynamic models.

50. The method of claim 49, wherein the request is received from a client application that corresponds to an industrial environment and / or one or more industrial entities within the industrial environment.

51. The method of claim 49, wherein the request is received from a client application that supports an Industrial Internet of Things sensor system.

52. The method of claim 49, wherein the digital twins are digital twins of at least one of industrial entities and industrial environments.

53. The method of claim 49, wherein the dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

54. The method of claim 49, wherein the data source is selected from the set of an Internet of Things connected device, a machine vision system, an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, and a cross-point switch.

55. The method of claim 49, wherein retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more digital twins.

56. The method of claim 49, wherein the one or more dynamic models are identified using a lookup table.

57. A system for monitoring navigational route data through an industrial environment having real-world elements disposed therein, the system comprising: a digital twin datastore including an industrial-environment digital twin corresponding to the industrial environment and a worker digital twin corresponding to a respective worker of a set of workers within the industrial environment; and one or more processors configured to: maintain, via the digital twin datastore, the industrial-environment digital twin to include contemporaneous positions for the set of workers within the industrial environment; monitor movement of each worker in the set of workers via a sensor array; determine, in response to detecting movement of the respective worker, navigational route data for the respective worker; and update the industrial-environment digital twin to include indicia of the navigational route data for the respective worker and to indicate movement of the worker digital twin along a route corresponding to the navigational route data.

58. The system of claim 57, wherein the one or more processors are further configured 10, in response to representing movement of the respective worker, determine navigational route data for remaining workers in the set of workers.

59. The system of claim 58, wherein the navigational route data is automatically transmitted to the system by one or more individual-associated devices.

60. The system of claim 59, wherein the individual-associated device is one of a mobile device having cellular data capabilities and a wearable device associated with the worker.

61. The system of claim 57, wherein the navigational route data is determined via environment-associated sensors.

62. The system of claim 61, wherein the navigational route data is determined using historical routing data stored in the digital twin datastore.

63. The system of claim 62, wherein the historical route data is obtained from a device associated with the respective worker.

64. The system of claim 62. wherein the historical route data is obtained a device associated with another worker.

65. The system of claim 64, wherein the historical route data is associated with a current task of the worker.

66. The system of claim 57, wherein the digital twin datastore includes an industrial- environment digital twin.

67. The system of claim 66, wherein the one or more processors are further configured to: determine existence of a conflict between the navigational route data and the industrial-environment digital twin; alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array, the navigational route data for the worker; and update, in response to determining inaccuracy of the industrial-environment digital twin via the sensor array, the industrial-environment digital twin to thereby resolve the conflict.

68. The system of claim 67, wherein the industrial-environment digital twin is updated using collected data transmitted from the worker.

69. The system of claim 68, wherein the collected data includes proximity sensor data, image data, or combinations thereof.

70. The system of claim 57, wherein the navigational route includes a route for collecting vibration measurements.

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