Intelligent vibration digital twin system and method for industrial environments

Abstract

A platform for updating one or more properties of one or more digital twins, comprising: receiving a request for one or more digital twins; retrieving one or more digital twins needed to fulfill the request from a digital twin data store; retrieving one or more dynamic models corresponding to one or more properties depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data sources; using the retrieved data as one or more inputs to the one or more dynamic models to determine one or more outputs; and updating 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

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of priority to the following U.S. provisional patent applications: Provisional Application No. 62 / 939,769, entitled "METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS," filed November 25, 2019; Provisional Application No. 63 / 016,974, entitled "DIGITAL TWIN SYSTEMS FOR INDUSTRIAL ENVIRONMENTS," filed April 28, 2020; Provisional Application No. 63 / 054,600, entitled "INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS," filed July 21, 2020; and Provisional Application No. 63 / 054,600, entitled "INFORMATION TECHNOLOGY SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS," filed August 24, 2020. No. 63 / 069,548, entitled "METHODS FOR MANUFACTURING ARTIFICIAL INTELLIGENCE LEVERAGING DIGITAL TWINS." This application also claims the benefit of priority to U.S. patent application Ser. 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 incorporated herein by reference in its entirety as if fully set forth herein.

[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 the like. [Background technology]

[0003] Industrial environments, such as those for large-scale manufacturing (e.g., aircraft, ships, trucks, automobiles, and large industrial machinery manufacturing), energy production (e.g., oil and gas plants, renewable energy environments), energy extraction (e.g., mining, drilling), and construction (e.g., large building construction), contain highly complex machines, devices, systems, and workflows that require operators to consider numerous parameters, metrics, and the like to optimize the design, development, deployment, and operation of various technologies to improve overall outcomes. Historically, data collection in industrial environments has been performed by humans using specialized data collection equipment, often recording batches of specific sensor data onto media such as tape or hard drives for later analysis. Traditionally, data batches have been returned to a central office for analysis, e.g., signal processing or other analytics performed on data collected by various sensors, which can then be used as the basis for diagnosing environmental problems or proposing ways to improve operations. This work has traditionally been performed on timescales of weeks to months and with limited data sets.

[0004] The advent of the Internet of Things (IoT) has enabled continuous connectivity to and between a wider range of devices. Most of these devices are consumer devices, such as lights, thermostats, etc. However, in more complex industrial environments, the range of available data is often limited, and the complexity of handling data from multiple sensors continues to make it more difficult to create effective "smart" solutions for the industrial sector. A need exists for improved methods and systems for data collection in industrial environments, and for using the collected data to provide improved monitoring, control, intelligent diagnosis of problems, and intelligent optimization of operations in various heavy industrial environments.

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

[0006] Industrial companies that rely on industry experts struggle to capture the knowledge of these experts when they move to another company or leave the workforce. A need exists in the art to capture industry expertise and use the captured industry expertise in training new workers or mobile electronics workers to perform industry-related tasks. Summary of the Invention

[0007] The present disclosure is directed to a platform for facilitating the development of intelligence in an Industrial Internet of Things (IIoT) system. The platform may be composed of multiple distinct data processing layers. The multiple distinct data processing layers may include an industrial monitoring system layer that collects data from or about multiple industrial entities in the IIoT system, an industrial entity-oriented data storage system layer that stores the data collected by the industrial monitoring system layer, an adaptive intelligent system layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system, and an industrial management application platform layer that includes multiple applications and manages the platform in a common application environment. The adaptive intelligent system layer may include a robotic process automation system that develops and deploys automation functions for one or more of the multiple industrial entities in the IIoT 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 one or more digital twins needed to fulfill the request from a digital twin data store; retrieving one or more dynamic models corresponding to one or more properties depicted in the one or more digital twins indicated by the request; selecting a data source from a set of available data sources based on one or more inputs of the one or more dynamic models; acquiring data from the selected data source; using the acquired data as one or more inputs to the one or more dynamic models to determine one or more outputs; and updating 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 corresponding to the industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application supporting an Industrial Internet of Things sensor system. In embodiments, the digital twin is a digital twin of at least one of the industrial entity and the industrial environment. In embodiments, the one or more dynamic models acquire data selected from a set of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data. In embodiments, the selected data source includes an Internet of Things connected device. In embodiments, the selected data source includes a machine vision system.

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

[0011] In embodiments, the present disclosure includes a method that includes receiving import data from one or more data sources, the import data corresponding to an industrial environment; generating an environment digital twin that represents the industrial environment based on the import data; identifying one or more industrial entities within the industrial environment; generating a set of discrete digital twins that represent the one or more industrial entities in 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 environment digital twin and the set of discrete digital twins are visual digital twins configured to be rendered in a visual manner. In embodiments, the present disclosure includes outputting the visual digital twin to a client application that displays the visual digital twin through a virtual reality headset. In embodiments, the present disclosure includes outputting the visual digital twin to a client application that displays the visual digital twin through a display device of a user device. In embodiments, the present disclosure includes outputting the visual digital twin to a client application that displays the visual digital twin through an augmented reality-enabled device. In embodiments, the present disclosure includes receiving user input related to one or more steps performed in an industrial process associated with an industrial environment; and generating, with respect to one or more of the industrial environment and a set of industrial entities, a process digital twin that defines the steps of the industrial process. 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 includes data defining the environment digital twin and one or more entity nodes each include respective data defining a respective discrete digital twin of the set of discrete digital twins. In an embodiment, each edge represents a relationship between two respective digital twins. In an embodiment, embedding the discrete digital twins includes connecting entity nodes corresponding to the respective discrete digital twins to the first node with edges representing respective relationships between the respective industrial entities and the industrial environment represented by the respective discrete digital twins. In an embodiment, each edge represents a spatial relationship between the two respective digital twins and an operational relationship between the two respective digital twins.In embodiments, each edge stores metadata corresponding to a relationship between two respective digital twins. In embodiments, each entity node of the one or more entity nodes includes one or more respective properties of the respective industry entity represented by the entity node. In embodiments, each entity node of the one or more entity nodes includes one or more behaviors of the respective properties of the respective industry entity represented by the entity node. In embodiments, an environment node includes one or more properties of the environment. In embodiments, an environment node includes one or more behaviors of the environment.

[0013] In embodiments, the disclosure includes running a simulation based on the environment digital twin and one or more discrete digital twins. In embodiments, the simulation simulates one of the operation of a machine in an industrial environment generating an output based on a set of inputs and the movement of a worker 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 the industrial environment. In embodiments, generating a 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 the industrial entity from a manufacturer of the industrial entity, the predefined digital twin including properties and behaviors of the industrial entity. In embodiments, generating the set of discrete digital twins includes classifying industrial entities in the imported data of the industrial environment and generating a discrete digital twin corresponding to the classified industrial entity.

[0014] In an embodiment, the disclosure includes a system for monitoring interactions within an industrial environment. In an embodiment, the system includes a digital twin data store including data collected by a set of proximity sensors disposed within the 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 an industrial environment digital twin for the industrial environment via the digital twin data store; receive signals from the plurality of elements indicative of actuation of at least one proximity sensor in the set of proximity sensors by a real-world element; collect updated location data of the real-world element using the at least one proximity sensor in response to actuation of the at least one proximity sensor; and update the industrial environment digital twin in the digital twin data store to include the updated location data.

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

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

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

[0018] In embodiments, the optimized route includes route information of other mobile elements within the real-world element, and the optimized route minimizes interactions between the mobile elements and humans within the industrial environment. In embodiments, the mobile elements include autonomous vehicles and non-autonomous vehicles, and the optimized route reduces interactions between the autonomous vehicles and 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 sub-microscopic traffic model, a mesoscopic traffic model, or a combination thereof.

[0019] In an embodiment, the present disclosure includes a method for updating one or more vibration fault level states of one or more digital twins, the method including: receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; obtaining one or more digital twins necessary to fulfill the request; obtaining one or more dynamic models necessary to fulfill the request, the one or more dynamic models including a dynamic model that predicts when a vibration fault level will occur based on an input data set; selecting a data source from a set of available data sources based on one or more inputs of the one or more dynamic models; obtaining data from the selected data sources; using the obtained data as one or more inputs to the one or more dynamic models to determine one or more outputs; and updating one or more vibration fault level states of the one or more digital twins based on the outputs of the one or more dynamic models.

[0020] In embodiments, the request is received from a client application corresponding to the industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application supporting an Industrial Internet of Things sensor system. In embodiments, the digital twin is a digital twin of at least one of the industrial entity and the industrial environment. In embodiments, the dynamic model acquires data selected from the set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biochemical concentration, metal concentration, and organic compound concentration data.

[0021] In an embodiment, the data source is selected from the set consisting 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 three-axis vibration sensor, a single-axis vibration sensor, an optical vibration sensor, and a crosspoint switch. In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties and the respective types of the one or more digital twins indicated in the request. In an embodiment, the one or more dynamic models are identified using a lookup table.

[0022] In an embodiment, the present disclosure includes a system for monitoring navigational path data through an industrial environment having real-world elements disposed therein. The system includes a digital twin data store including an industrial environment digital twin corresponding to the industrial environment and a worker digital twin corresponding to each worker in a set of workers in the industrial environment; and one or more processors configured to: maintain the industrial environment digital twin to include contemporaneous positions for the set of workers in the industrial environment via the digital twin data store; monitor the movements of each worker in the set of workers via a sensor array; determine navigational path data for each worker in response to detecting each worker's movement; and update the industrial environment digital twin to include indices of the navigational path data for each worker and to represent the movement of the worker digital twin along a path corresponding to the navigational path data. In an embodiment, the one or more processors are further configured to determine navigational path data for remaining workers in the set of workers in response to representing the movement of each worker. In an embodiment, the navigational path data is automatically transmitted to the system by one or more personal-associated devices. In an embodiment, the personal-associated devices are one of a mobile device with cellular data capability and a wearable device associated with the worker. In embodiments, the navigational path data is determined via environment-related sensors. In embodiments, the navigational path data is determined using historical path data stored in a digital twin data store. In embodiments, the historical path data is obtained from a device associated with the respective worker. In embodiments, the historical path data is obtained from a device associated with another worker. In embodiments, the historical path data is associated with the worker's current task. In embodiments, the digital twin data store includes an industrial environment digital twin.In an embodiment, the one or more processors are further configured to: determine the existence of a conflict between the navigation path data and the industrial environment digital twin; modify the worker's navigation path data in response to determining the accuracy of the industrial environment digital twin via the sensor array; and update the industrial environment digital twin in response to determining the inaccuracy of the industrial environment digital twin via the sensor array, thereby resolving the conflict.

[0023] In embodiments, the industrial environment digital twin is updated using collected data sent from workers. In embodiments, the collected data includes proximity sensor data, image data, or a combination thereof. In embodiments, the navigation path includes a path 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, the method including receiving a request for one or more digital twins; retrieving one or more digital twins necessary to fulfill the request from a digital twin data store; retrieving one or more dynamic models corresponding to one or more properties depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data sources; using the retrieved data as one or more inputs to the one or more dynamic models to determine one or more outputs; and updating 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 corresponding to the industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application supporting an Industrial Internet of Things sensor system. In embodiments, the digital twin is a digital twin of at least one of the industrial entity and the industrial environment. In embodiments, the one or more dynamic models take data selected from the set consisting of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data. In embodiments, the selected data source comprises an Internet of Things connected device. In embodiments, the selected data source comprises a machine vision system.

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

[0027] In an embodiment, the present disclosure includes a method, the 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 in 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 environment digital twin and the set of discrete digital twins are visual digital twins configured to be rendered in a visual manner. In embodiments, the present disclosure includes outputting the visual digital twin to a client application that displays the visual digital twin through a virtual reality headset. In embodiments, the present disclosure includes outputting the visual digital twin to a client application that displays the visual digital twin through a display device of a user device. In embodiments, the present disclosure includes outputting the visual digital twin to a client application that displays the visual digital twin through an augmented reality enabled device. In embodiments, the present disclosure includes receiving user input related to one or more steps performed in an industrial process associated with an industrial environment, and generating a process digital twin that defines the steps of the industrial process with respect to one or more of the industrial environment and 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 includes data defining the environment digital twin and one or more entity nodes each include respective data defining a respective discrete digital twin of the set of discrete digital twins. In an embodiment, each edge represents a relationship between two respective digital twins. In an embodiment, embedding the discrete digital twins includes connecting entity nodes corresponding to each discrete digital twin to the first node with edges representing respective relationships between each industrial entity and the industrial environment represented by each discrete digital twin. In an embodiment, 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 a relationship between two respective digital twins. In embodiments, each entity node of the one or more entity nodes includes one or more respective properties of the respective industry entity represented by the entity node. In embodiments, each entity node of the one or more entity nodes includes one or more behaviors of the respective properties of the respective industry entity represented by the entity node. In embodiments, an environment node includes one or more properties of the environment. In embodiments, an environment node includes one or more behaviors of the environment.

[0029] In embodiments, the disclosure includes running a simulation based on an environment digital twin and one or more discrete digital twins. In embodiments, the simulation simulates one of the operation of a machine in an industrial environment generating an output based on a set of inputs and the movement of a worker 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 the industrial environment. In embodiments, generating a 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 the industrial entity from a manufacturer of the industrial entity, the predefined digital twin including properties and behaviors of the industrial entity. In embodiments, generating the set of discrete digital twins includes classifying industrial entities in the imported data of the industrial environment and generating a discrete digital twin corresponding to the classified industrial entity.

[0030] In an embodiment, the disclosure includes a system for monitoring interactions within an industrial environment. In an embodiment, the system includes a digital twin data store including data collected by a set of proximity sensors disposed within the 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 an industrial environment digital twin for the industrial environment via the digital twin data store; receive signals from the plurality of elements indicative of actuation of at least one proximity sensor in the set of proximity sensors by a real-world element; collect updated location data of the real-world element using the at least one proximity sensor in response to actuation of the at least one proximity sensor; and update the industrial environment digital twin in the digital twin data store to include the updated location data.

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

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

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

[0034] In embodiments, the optimized route includes route information for other mobile elements within the real-world element, and the optimized route minimizes interactions between the mobile elements and humans within the industrial environment. In embodiments, the mobile elements include autonomous vehicles and non-autonomous vehicles, and the optimized route reduces interactions between the autonomous vehicles and 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 sub-microscopic traffic model, a mesoscopic traffic model, or a combination thereof.

[0035] In an embodiment, the present disclosure includes a method for updating one or more vibration fault level states of one or more digital twins, the method including: receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; obtaining one or more digital twins necessary to fulfill the request; obtaining one or more dynamic models necessary to fulfill the request, the one or more dynamic models including a dynamic model that predicts when a vibration fault level will occur based on an input data set; selecting a data source from a set of available data sources based on one or more inputs of the one or more dynamic models; obtaining data from the selected data sources; using the obtained data as one or more inputs to the one or more dynamic models to determine one or more outputs; and updating one or more vibration fault level states of the one or more digital twins based on the outputs of the one or more dynamic models.

[0036] In embodiments, the request is received from a client application corresponding to the industrial environment and / or one or more industrial entities within the industrial environment. In embodiments, the request is received from a client application supporting an Industrial Internet of Things sensor system. In embodiments, the digital twin is a digital twin of at least one of the industrial entity and the industrial environment. In embodiments, the dynamic model retrieves data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biochemical concentration, metal concentration, and organic compound concentration data.

[0037] In an embodiment, the data source is selected from the set consisting 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 three-axis vibration sensor, a single-axis vibration sensor, an optical vibration sensor, and a crosspoint switch. In an embodiment, obtaining the one or more dynamic models includes identifying one or more dynamic models based on the one or more characteristics indicated in the request and the respective types of the one or more digital twins. In an embodiment, the one or more dynamic models are identified using a lookup table.

[0038] In an embodiment, the present disclosure includes a system for monitoring navigational path data through an industrial environment having real-world elements disposed therein. The system includes a digital twin data store including an industrial environment digital twin corresponding to the industrial environment and a worker digital twin corresponding to each worker in a set of workers in the industrial environment, and one or more processors configured to: maintain the industrial environment digital twin via the digital twin data store to include contemporaneous locations of the set of workers in the industrial environment; monitor the movements of each worker in the set of workers via a sensor array; determine navigational path data for each worker in response to detecting each worker's movement; and update the industrial environment digital twin to include indices of the navigational path data for each worker and to indicate the movement of the worker digital twin along a path corresponding to the navigational path data. In an embodiment, the one or more processors are further configured to determine navigational path data for remaining workers in the set of workers in response to representing each worker's movement. In an embodiment, the navigational path data is automatically transmitted to the system by one or more personal-associated devices. In an embodiment, the personal-associated devices are one of a mobile device with cellular data capability and a wearable device associated with the worker. In embodiments, the navigational path data is determined via environment-related sensors. In embodiments, the navigational path data is determined using historical path data stored in a digital twin data store. In embodiments, the historical path data is obtained from a device associated with the respective worker. In embodiments, the historical path data is obtained from a device associated with another worker. In embodiments, the historical path data is associated with the worker's current task. In embodiments, the digital twin data store includes an industrial environment digital twin.In an embodiment, the one or more processors are further configured to: determine the existence of a conflict between the navigation path data and the industrial environment digital twin; modify the worker's navigation path data in response to determining the accuracy of the industrial environment digital twin via the sensor array; and update the industrial environment digital twin in response to determining the inaccuracy of the industrial environment digital twin via the sensor array, thereby resolving the conflict.

[0039] In embodiments, the industrial environment digital twin is updated using collected data submitted by workers. In embodiments, the collected data includes proximity sensor data, image data, or a combination thereof. In embodiments, the navigation path includes a path for collecting vibration measurements.

[0040] According to some embodiments of the present disclosure, provided herein are methods and systems for updating properties of a digital twin of an industrial entity and a digital twin of an industrial environment, such as, but not limited to, based on the impact of collected vibration data on a set of digital twin dynamic models, such that the digital twin provides 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; obtaining one or more digital twins necessary to satisfy the request; obtaining one or more dynamic models necessary to satisfy the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; obtaining data from the selected data sources; using the obtained data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating one or more properties of the one or more digital twins based on outputs of the one or more dynamic models.

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

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

[0044] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0045] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0046] In an embodiment, the digital twin is a digital twin of an industrial environment.

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

[0048] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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 an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0050] In an embodiment, the one or more dynamic models are identified using a look-up 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; obtaining one or more digital twins necessary to satisfy the request; obtaining one or more dynamic models necessary to satisfy the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; obtaining data from the selected data sources; using the obtained data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating one or more vibration fault level states of the one or more digital twins based on the outputs of the one or more dynamic models.

[0052] In an embodiment, the vibration fault level condition is selected from the set of normal, suboptimal, critical, and warning.

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

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

[0055] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0056] In an embodiment, the dynamic model incorporates data selected from the group of data related to vibration, temperature, pressure, humidity, wind, rainfall, tides, storm surges, cloud cover, snowfall, visibility, radiation, audio, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biochemical concentration, metal concentration, and organic compound concentration.

[0057] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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 an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0059] In an embodiment, the one or more dynamic models are identified using a look-up 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; obtaining one or more digital twins necessary to satisfy the request; obtaining one or more dynamic models necessary to satisfy the request; selecting a data source from a set of available data sources based on one or more inputs of the one or more dynamic models; obtaining data from the selected data source; using the obtained data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating the one or more vibration severity unit values ​​of the one or more digital twins based on the outputs of the one or more dynamic models.

[0061] In an embodiment, the vibration severity units represent displacement.

[0062] In an embodiment, the units of vibration severity represent velocity.

[0063] In an embodiment, the vibration severity units represent acceleration.

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

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

[0066] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0067] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0068] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0069] In an embodiment, the dynamic model obtains selected data from a group of data representing vibration, temperature, pressure, humidity, wind, rainfall, tides, storm surges, cloud cover, snowfall, visibility, radiation, audio, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration.

[0070] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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.

[0071] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0072] In an embodiment, the one or more dynamic models are identified using a look-up table.

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

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

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

[0076] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0077] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0078] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0079] In an embodiment, the dynamic model incorporates data selected from a set of data representing vibration, temperature, pressure, humidity, wind, rainfall, tides, storm surges, cloud cover, snowfall, visibility, radiation, audio, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration.

[0080] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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 an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0082] In an embodiment, the one or more dynamic models are identified using a look-up table.

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

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

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

[0086] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0087] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0088] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0089] In an embodiment, the dynamic model takes data selected from the set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biochemical concentration, metal concentration, and organic concentration data.

[0090] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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 an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0092] In an embodiment, the one or more dynamic models are identified using a look-up table.

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

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

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

[0096] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0097] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0098] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0099] In an embodiment, the dynamic model takes data selected from the set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biochemical concentration, metal concentration, and organic compound concentration data.

[0100] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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 an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0102] In an embodiment, the one or more dynamic models are identified using a look-up table.

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

[0104] In an embodiment, the downtime cost value is selected from the set consisting of: downtime cost per hour, downtime cost per day, downtime cost per week, downtime cost per month, downtime cost per quarter, and downtime cost per year.

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

[0106] In an embodiment, the request is received from a client application supporting an Industrial Internet of Things sensor system.

[0107] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0108] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0109] In an embodiment, the digital twin is a digital twin of an industrial environment.

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

[0111] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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 an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0113] In an embodiment, the one or more dynamic models are identified using a look-up table.

[0114] According to some embodiments of the present disclosure, a method is disclosed for updating one or more manufacturing key performance indicator (KPI) values ​​of one or more digital twins. 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 one or more digital twins necessary to satisfy the request; retrieving one or more dynamic models necessary to satisfy the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data sources; using the retrieved data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating the one or more manufacturing KPI values ​​of the one or more digital twins based on outputs of the one or more dynamic models.

[0115] In an embodiment, the manufacturing KPIs are selected from the set consisting of uptime, utilization rate, standard operating efficiency, overall operating efficiency, overall equipment effectiveness, machine downtime, unscheduled downtime, machine setup time, inventory turns, inventory accuracy, quality (e.g., defect rate), first-order yield, rework, scrap, rejected inspections, on-time delivery, customer returns, training hours, employee turnover, reportable health and safety incidents, revenue per employee, profit per employee, schedule maintenance, total cycle time, throughput, changeover time, yield, planned maintenance rate, and utilization rate.

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

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

[0118] In an embodiment, the request is received from a client application supporting the vibration sensor system.

[0119] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0120] In an embodiment, the digital twin is a digital twin of an industrial environment.

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

[0122] In an embodiment, the data source is selected from the set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis 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.

[0123] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0124] In an embodiment, the one or more dynamic models are identified using a look-up table.

[0125] According to some embodiments of the present disclosure, a method is disclosed that includes receiving import data from one or more data sources, the import data corresponding to an industrial environment; generating an environment digital twin that represents the industrial environment based on the import data; identifying one or more industrial entities within the industrial environment; generating a set of discrete digital twins that represent the one or more industrial entities within the environment; 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 an embodiment, the connection to the sensor system is established via an application programming interface (API).

[0127] In embodiments, the set of environment digital twins and discrete digital twins is a visual digital twin configured to be rendered in a visual manner. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin through a virtual reality headset. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin through a display device of the user device. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin within a display interface, with information related to the visual digital twin overlaid on the visual digital twin and / or displayed within the display interface. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin through 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 includes data defining the environment digital twin, and wherein one or more entity nodes each include 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 the discrete digital twins includes connecting an entity node corresponding to each 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 the two respective digital twins. In some embodiments, each edge represents an operational relationship between the 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 respective properties of a 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 property of a respective industrial entity represented by the entity node. In some embodiments, an environment node includes one or more properties of an environment. In some embodiments, an environment node includes one or more behaviors of an environment.

[0129] In some embodiments, the method further includes running a simulation based on the environmental digital twin and the one or more discrete digital twins. In some embodiments, the simulation simulates the operation of a machine that generates outputs based on a set of inputs. In some embodiments, the simulation simulates vibration patterns of bearings in a machine in an industrial environment.

[0130] In an embodiment, the one or more industrial entities are selected from the set consisting of a machine component, an infrastructure component, an equipment component, a workpiece component, a tool component, a building component, an electrical component, a fluid handling component, a mechanical component, a power component, a manufacturing component, an energy production component, a material extraction component, a worker, a robot, an assembly line, and an autonomous vehicle.

[0131] In an embodiment, the industrial environment is one of a factory, an energy production facility, a material extraction facility, a mining facility, an excavation facility, an industrial agriculture facility, and an industrial storage facility.

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

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

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

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

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

[0137] In an embodiment, generating the set of discrete digital twins includes classifying industrial entities in the import data of the industrial environment and generating discrete digital twins corresponding to the classified industrial entities.

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

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

[0140] In an embodiment, the device is a wearable device.

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

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

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

[0144] In an embodiment, the plurality of elements includes mobile device elements and workers, and the mobile device location data is measured using data transmitted by each mobile device element, and the worker location data is measured using data acquired by the system.

[0145] In an embodiment, worker location data is determined using information transmitted from devices associated with each worker.

[0146] In an embodiment, activation of the at least one proximity sensor occurs in response to an interaction between a respective operator and the proximity sensor.

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

[0148] In an embodiment, the one or more processors collect updated position data for the plurality of elements using the set of proximity sensors in response to activation of 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 data store and one or more processors. The digital twin data store includes a set of states stored therein. The set of states includes one or more states of the real-world elements. Each state in the set of states is uniquely identifiable by a set of criteria from a set of monitored attributes. The monitored attributes correspond to signals received from a sensor array operably coupled to the real-world elements. The one or more processors are configured to maintain an industrial environment digital twin for the industrial environment via the digital twin data store, receive signals for one or more attributes in the set of monitored attributes via the sensor array, determine one or more current states of the real-world elements in response to determining that the signals for the one or more attributes satisfy a respective set of the identification criteria, and update the industrial environment digital twin to include the current states of the one or more real-world elements in response to determining the current states. The current states correspond to each state in the set of states.

[0150] In an embodiment, the cognitive intelligence system stores the identification criteria in a digital twin data store.

[0151] In an embodiment, in response to receiving the identification criteria, the cognitive intelligence system updates the trigger conditions for the set of monitored attributes to include the updated trigger conditions.

[0152] In an embodiment, the updated trigger condition is one that decreases the time interval between receiving a sensed attribute from the set of monitored attributes.

[0153] In an embodiment, the sensed attribute is an attribute that corresponds to the discrimination criteria.

[0154] In an embodiment, the sensed attributes are all attributes corresponding to each real-world element.

[0155] In an embodiment, the cognitive intelligence system determines whether instructions exist for responding to the condition, and in response to determining that instructions do not exist, the cognitive intelligence system uses the digital twin simulation system to determine instructions for responding to the condition.

[0156] In an embodiment, the digital twin simulation system and the cognitive intelligence system repeatedly iterate the simulation values ​​and response actions until the associated cost function is minimized, and the one or more processors are further configured to, in response to minimizing the associated cost function, store the response action that minimizes the associated cost function in the digital twin data store.

[0157] In an embodiment, the cognitive intelligence system is configured to affect a responsive action associated with the condition.

[0158] In an embodiment, the cognitive intelligence system is configured to stop the operation of one or more real-world elements identified by the responsive action.

[0159] In an embodiment, the cognitive intelligence system is configured to determine resources for the industrial environment identified by the responsive action and modify the resources in response thereto.

[0160] In an embodiment, the resource comprises data transfer bandwidth, and modifying the resource comprises establishing additional connections to thereby increase the data transfer bandwidth.

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

[0162] In an embodiment, the one or more processors are further configured to update determining navigation path data for remaining workers in the set of workers in response to representing the movement of each worker.

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

[0164] In an embodiment, navigation route data is automatically transmitted to the system by one or more individual-associated devices.

[0165] In an embodiment, the personal associated device is a mobile device with cellular data capability.

[0166] In an embodiment, the person-associated device is a wearable device associated with the worker.

[0167] In an embodiment, the navigation path data is determined via environment-related sensors.

[0168] In an embodiment, the navigation route data is determined using historical route data stored in a digital twin data store.

[0169] In an embodiment, historical route data is obtained using each worker.

[0170] In an embodiment, the historical route data is obtained using a separate operator.

[0171] In an embodiment, the historical route data is associated with the worker's current task.

[0172] In an embodiment, the digital twin data store includes an industrial environment digital twin.

[0173] In an embodiment, the one or more processors are further configured to determine the existence of a conflict between the navigation path data and the industrial environment digital twin, modify the worker's navigation path data in response to determining the accuracy of the industrial environment digital twin via the sensor array, and update the industrial environment digital twin in response to determining the inaccuracy of the industrial environment digital twin via the sensor array, thereby resolving the conflict.

[0174] In an embodiment, the industrial environment digital twin is updated using collected data sent by workers.

[0175] In an embodiment, the collected data includes proximity sensor data, image data, or a combination thereof.

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

[0177] In an embodiment, the one or more processors are further configured to, in response to representing the movement of the at least one worker, determine navigation path data for remaining workers in the set of workers.

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

[0179] In an embodiment, navigation route data is automatically transmitted to the system by one or more person-associated devices.

[0180] In an embodiment, the personal associated device is a mobile device with cellular data capability.

[0181] In an embodiment, the person-associated device is a wearable device associated with the worker.

[0182] In an embodiment, the navigation path data is determined via environment-related sensors.

[0183] In an embodiment, the navigation route data is determined using historical route data stored in a digital twin data store.

[0184] In an embodiment, historical route data is obtained using each worker.

[0185] In an embodiment, the historical route data is obtained using a separate operator.

[0186] In an embodiment, the historical route data is associated with the worker's current task.

[0187] In an embodiment, the digital twin data store includes an industrial environment digital twin.

[0188] In an embodiment, the one or more processors are further configured to determine the existence of a conflict between the navigation path data and the industrial environment digital twin, modify the worker's navigation path data in response to determining the accuracy of the industrial environment digital twin via the sensor array, and update the industrial environment digital twin in response to determining the inaccuracy of the industrial environment digital twin via the sensor array, thereby resolving the conflict.

[0189] In an embodiment, the industrial environment digital twin is updated using collected data sent by workers.

[0190] In an embodiment, the collected data includes proximity sensor data, image data, or a combination thereof.

[0191] According to an aspect of the present disclosure, a system for representing an industrial workpiece object in a digital twin includes a digital twin data store and one or more processors. The digital twin data store stores an industrial environment digital twin with real-world element digital twins embedded therein. The industrial environment digital twin provides a digital twin of the industrial environment. Each real-world element digital twin provides a digital twin of a corresponding real-world element in the industrial environment. The real-world elements include 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 performed by a worker on the industrial workpiece. The simulation includes obtaining the set of physical interactions, estimating an expected duration for performance of each physical interaction in the set of physical interactions based on historical data of the worker, and storing, in the digital twin data store, an industrial workpiece digital twin corresponding to the performance of the set of physical interactions of the industrial workpiece.

[0192] In an embodiment, the historical data is obtained from user-entered data.

[0193] In an embodiment, historical data is obtained from a sensor array in an industrial environment.

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

[0195] In an embodiment, each datum of the historical data includes an indicator of a first time and a second time, the first time being the time of execution of the physical interaction.

[0196] In an embodiment, the second time is the time when the worker's expected break period begins.

[0197] In an embodiment, the historical data further includes an indicator of the duration for the expected rest period.

[0198] In an embodiment, the second time is the time that the worker's expected break period ends.

[0199] In an embodiment, the historical data further includes an indicator of the duration for the expected rest period.

[0200] In an embodiment, the second time is the time that ends the worker's unplanned break.

[0201] In an embodiment, the historical data further includes an indicator of the duration for the unexpected break period.

[0202] In an embodiment, each datum of the historical data includes an indicator of successive interactions of the worker with a plurality of other workpieces prior to performing the set of physical interactions with the workpiece.

[0203] In an embodiment, each datum in the historical data includes an indicator of the number of consecutive days that the worker was present in the industrial environment.

[0204] In an embodiment, each datum in the historical data includes an indicator of the worker's age.

[0205] In an embodiment, the historical data further includes an indicator of a first duration of the worker's expected break time and a second duration of the worker's unexpected break time, each datum of the historical data including an indicator of a plurality of times, an indicator of consecutive interactions of the worker with a plurality of other workpieces prior to performing the set of physical interactions with the workpieces, an indicator of the number of consecutive days the worker has been present in the industrial environment, and / or an indicator of the worker's age. 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 performing the physical interactions, the second time is a start time of the expected break time, the third time is an end time of the expected break time, and the fourth time is an end time of the unexpected break time.

[0206] In an embodiment, the industrial workpiece digital twin is a first industrial workpiece digital twin corresponding to the industrial workpiece before performance of any physical interactions, and a second industrial workpiece digital twin corresponding to the industrial workpiece after performance of a set of physical interactions.

[0207] In an embodiment, the industrial workpiece digital twin is a plurality of industrial workpiece digital twins, each of which corresponds to the industrial workpiece after performing 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 data store and one or more processors. The digital twin data store stores an industrial environment digital twin with real-world element digital twins embedded therein. The industrial environment digital twin provides a digital twin of the industrial environment. Each real-world element digital twin provides a digital twin of a corresponding real-world element in the industrial environment. The real-world elements include a wearable device worn by a wearer in the industrial environment. The one or more processors are configured to embed a set of control instructions for the wearable device in the digital twin and to induce an experience for a wearer of the wearable device in response to an interaction between the wearable device and a respective one of the digital twins.

[0209] In embodiments, the wearable device is configured to output video, audio, haptic feedback, or a combination thereof to elicit an experience for the wearer.

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

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

[0212] In an embodiment, the wearable device includes a display device and the experience includes displaying information related to each digital twin.

[0213] In an embodiment, the displayed information includes financial data related to the digital twin.

[0214] In an embodiment, the information displayed includes profits or losses associated with the operation of the digital twin.

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

[0216] In an embodiment, the displayed information includes the operating parameters of the occluded element.

[0217] In an embodiment, the displayed information further includes a comparison of the displayed operating parameters with corresponding design parameters.

[0218] In an embodiment, the comparison includes altering the display of the operational parameter to change the color, size, or display duration of the operational parameter.

[0219] In an embodiment, the information includes a virtual model of the occluded element overlaid on the occluded element and visualized together with the foreground element.

[0220] In an embodiment, the information includes indicators for the removable elements configured to provide access to the occluded elements, each indicator displayed proximate to a respective removable element.

[0221] In an embodiment, the indicators are displayed sequentially such that a first indicator corresponding to a first removable element is displayed and, in response to the operator removing the first removable element, a second indicator corresponding to a second removable element is displayed.

[0222] According to aspects of the present disclosure, a system for embedding device outputs in an industrial digital twin includes a digital twin data store and one or more processors. The digital twin data store stores an industrial environment digital twin having real-world element digital twins embedded therein. The industrial environment digital twin provides a digital twin of the industrial environment. Each real-world element digital twin provides a digital twin of a corresponding real-world element in the industrial environment. The real-world elements include simultaneous location and mapping sensors. The one or more processors are configured to acquire location information from the simultaneous location and mapping sensors, determine that the simultaneous location and mapping sensors are located within the environment, collect mapping information, path information, or a combination thereof from the simultaneous location and mapping sensors, and update the industrial environment digital twin using the mapping information, path information, or a combination thereof. The collection occurs in response to determining that the simultaneous location and mapping sensors are located within the industrial environment.

[0223] In an embodiment, the one or more processors are configured to detect objects in the mapping information, determine for each detected object in the mapping information 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, use the digital twin generation system to add the digital twin of the detected object to the real-world element digital twin in the digital twin data store, and in response to determining that the detected object does not correspond to an existing real-world element digital twin, update the real-world element digital twin to include the new information detected by the synchronous position and mapping sensors.

[0224] In an embodiment, the synchronous position and mapping sensor is configured to generate the mapping information using a sub-optimal mapping algorithm.

[0225] In an embodiment, a suboptimal mapping algorithm generates bounded-region representations for elements within an industrial environment.

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

[0227] In an embodiment, updated mapping information is provided to synchronous position and mapping sensors to thereby optimize navigation through industrial environments.

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

[0229] In an embodiment, the synchronous position and mapping sensor provides updated data using a second algorithm, the second algorithm configured to increase the resolution of the detected objects.

[0230] In an embodiment, the synchronous location and mapping sensor captures updated data of real-world elements corresponding to detected objects in response to receiving a request.

[0231] In an embodiment, the synchronous position and mapping sensors are in an autonomous vehicle navigating an industrial environment.

[0232] In an embodiment, navigation of an autonomous vehicle includes use of a digital twin received from a digital twin data store.

[0233] According to aspects of the present disclosure, a system for embedding device outputs in an industrial digital twin includes a digital twin data store and one or more processors. The digital twin data store stores an industrial environment digital twin having real-world element digital twins embedded therein. The industrial environment digital twin provides a digital twin of the industrial environment. Each real-world element digital twin provides a digital twin for a corresponding real-world element in the industrial environment. The real-world element includes 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 in the industrial environment digital twin to define at least one external feature of the real-world element in the industrial environment.

[0234] In an embodiment, the one or more processors are further configured to analyze the output of the light detection and ranging sensor to identify a plurality of detected objects within the output, each of the plurality of detected objects being a closed shape.

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

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

[0237] In an embodiment, the scan is at a resolution at least five times greater than the first resolution.

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

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

[0240] According to aspects of the present disclosure, a system for embedding device outputs in an industrial digital twin includes a digital twin data store and one or more processors. The digital twin data store 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 corresponding to a respective one of the real-world elements. The one or more processors are configured, for each of the plurality of wearable devices, to obtain an output from the wearable device and, in response to detecting a trigger condition, update the industrial environment digital twin using the output from the wearable device.

[0241] In an embodiment, the trigger condition is the receipt of an output from the wearable device.

[0242] In an embodiment, the trigger condition is a determination that an output from the wearable device differs from a previously stored output from the wearable device.

[0243] In an embodiment, the trigger condition is a determination that a received output from another wearable device in the plurality of wearable devices is different from a previously stored output from that other wearable device.

[0244] In an embodiment, the trigger condition comprises a mismatch between an output from a wearable device and a contemporaneous output from another of the wearable devices.

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

[0246] In an embodiment, the trigger condition includes a user interaction with a digital twin corresponding to the wearable device.

[0247] In an embodiment, the one or more processors are further configured to detect objects in the mapping information received from the synchronous location and mapping sensors. For each detected object in 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, add, using the digital twin generation system, the digital twin of the detected object to the real-world element digital twin in the digital twin data store, and in response to determining that the detected object corresponds to an existing real-world element digital twin, update the real-world element digital twin to include the new information detected by the synchronous location and mapping sensors.

[0248] In an embodiment, the synchronous position and mapping sensor is configured to generate the mapping information using a suboptimal mapping algorithm.

[0249] In an embodiment, a suboptimal mapping algorithm generates a bounded region representation of an element in an industrial environment.

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

[0251] In an embodiment, updated mapping information is provided to synchronous position and mapping sensors to thereby optimize navigation through industrial environments.

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

[0253] In an embodiment, the synchronous location and mapping sensors provide updated data using a second algorithm, the second algorithm configured to increase the resolution of the detected objects.

[0254] In an embodiment, the synchronous location and mapping sensor retrieves updated data of the real-world element corresponding to the detected object in response to receiving the request.

[0255] In an embodiment, the synchronous position and mapping sensors are in an autonomous vehicle navigating an industrial environment.

[0256] In an embodiment, navigation of an autonomous vehicle includes use of real-world element digital twins received from a digital twin data store.

[0257] According to aspects of the present disclosure, a system for representing attributes in an industrial digital twin includes a digital twin data store and one or more processors. The digital twin data store 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 located within the industrial environment. The real-world element digital twin includes a mobile element digital twin. Each mobile element digital twin provides a digital twin of a respective mobile element within the real-world element. The one or more processors are configured, for each mobile element, to estimate a location of the mobile element in response to an occurrence of a trigger condition, and to update the mobile element digital twin corresponding to the mobile element to reflect the location of the mobile element in response to estimating the location of the mobile element.

[0258] In an embodiment, the mobile elements are workers in an industrial environment.

[0259] In an embodiment, the mobile element is a vehicle in an industrial environment.

[0260] In an embodiment, the trigger condition is the expiration of a dynamically determined time interval.

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

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

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

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

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

[0266] In an embodiment, the trigger condition is the expiration of a time interval, the time interval being calculated based on the probability that the mobile element has moved.

[0267] In an embodiment, the trigger condition is the proximity of a mobile element to another mobile element.

[0268] In an embodiment, the trigger condition is based on the density of moving elements in the industrial environment.

[0269] In an embodiment, the route information is obtained from a navigation module of the mobile element.

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

[0271] In an embodiment, the optimized route includes using route information of other mobile elements within the real-world element.

[0272] In an embodiment, the optimized route minimizes interactions between mobile elements and humans within the industrial environment.

[0273] In an embodiment, the mobile elements include autonomous and non-autonomous vehicles, and the optimized route reduces interaction between the autonomous and non-autonomous vehicles.

[0274] In an embodiment, traffic modeling includes the use of a particle traffic model, a trigger-response mobile element following traffic model, a macroscopic traffic model, a microscopic 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 located within the industrial environment. The one or more processors are configured to: determine, for each of the real-world elements, 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 an embodiment, a user's interaction with a real-world element digital twin includes the user selecting the real-world element digital twin.

[0277] In an embodiment, a user's interaction with a real-world element digital twin includes the user pointing an image capture device at the real-world element digital twin.

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

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

[0280] In an embodiment, the design specifications are stored in a digital twin data store in response to user input.

[0281] In an embodiment, the design specifications are determined using a digital twin simulation system.

[0282] In an embodiment, the one or more processors are further configured to: detect, for each of the real-world elements, one or more synchronic operating parameters using sensors in the industrial environment; compare the one or more synchronic operating parameters to a design specification; and, in response to a discrepancy between the one or more synchronic operating parameters and the design specification, automatically display the design specification, the one or more synchronic operating parameters, or a combination thereof. The one or more synchronic operating parameters correspond to the design specification of the real-world element.

[0283] In an embodiment, the representation of the design specification includes an indicator of the synchronous operating parameter.

[0284] In an embodiment, the display of the design specification includes a source indicator of the specification information.

[0285] In an embodiment, the source indicator notifies the user that the design specifications were determined through the use of a digital twin simulation system.

[0286] A more complete understanding of the present disclosure will be appreciated from the following description and accompanying drawings, and the appended claims.

[0287] In an embodiment, the one or more dynamic models are identified using a look-up table.

[0288] According to some embodiments of the present disclosure, a method is disclosed for updating one or more fluid dynamics-related values ​​of one or more digital twins. 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; obtaining one or more digital twins necessary to satisfy the request; obtaining one or more dynamic models necessary to satisfy the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; obtaining data from the selected data sources; using the obtained data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating one or more values ​​for the fluid dynamics-related values ​​of the one or more digital twins based on the outputs of the one or more dynamic models.

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

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

[0291] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0292] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0293] In an embodiment, the dynamic model takes data selected from the set consisting 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, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

[0296] In an embodiment, the fluid dynamics related value is a fluid flow rate value.

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

[0298] In an embodiment, the one or more dynamic models are identified using a look-up 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 one or more digital twins necessary to fulfill the request; retrieving one or more dynamic models necessary to fulfill the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data sources; using the retrieved data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating one or more values ​​related to radiation doses of the one or more digital twins based on the outputs of the one or more dynamic models.

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

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

[0302] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0303] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0304] In an embodiment, the dynamic model obtains data selected from the set consisting of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

[0307] In an embodiment, the radiation value is a gamma dose rate value.

[0308] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0309] In an embodiment, the one or more dynamic models are identified using a look-up table.

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

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

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

[0313] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0314] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0315] In an embodiment, the dynamic model incorporates data selected from the set consisting of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

[0318] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0319] In an embodiment, the one or more dynamic models are identified using a look-up table.

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

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

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

[0323] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0324] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0325] In an embodiment, the dynamic model incorporates data selected from the set consisting of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

[0328] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0329] In an embodiment, the one or more dynamic models are identified using a look-up 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 one or more digital twins necessary to fulfill the request; retrieving one or more dynamic models necessary to fulfill the request; selecting a data source from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data source; using the retrieved data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating one or more values ​​related to the metal concentration values ​​of the one or more digital twins based on the outputs of the one or more dynamic models.

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

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

[0334] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0335] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0336] In an embodiment, the dynamic model takes data selected from the set consisting 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, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

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

[0340] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and the respective types of the one or more digital twins.

[0341] In an embodiment, the one or more dynamic models are identified using a look-up 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 one or more digital twins necessary to fulfill the request; retrieving one or more dynamic models necessary to fulfill the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data sources; using the retrieved data as one or more inputs of the one or more dynamic models to calculate one or more outputs; and updating one or more values ​​related to the organic compound concentration values ​​of the one or more digital twins based on the outputs of the one or more dynamic models.

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

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

[0345] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0346] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0347] In an embodiment, the dynamic model incorporates data selected from the set consisting of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

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

[0351] In an embodiment, the one or more dynamic models are identified using a look-up 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 one or more digital twins necessary to fulfill the request; retrieving one or more dynamic models necessary to fulfill the request; selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models; retrieving data from the selected data sources; using the retrieved data as one or more inputs to the one or more dynamic models to calculate one or more outputs; and updating one or more values ​​related to the biological compound concentration values ​​of the one or more digital twins based on the outputs of the one or more dynamic models.

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

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

[0355] In an embodiment, the digital twin is a digital twin of an industrial entity.

[0356] In an embodiment, the digital twin is a digital twin of an industrial environment.

[0357] In an embodiment, the dynamic model acquires data selected from the set consisting of temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, speed, acceleration, light level, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

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

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

[0360] In an embodiment, obtaining the one or more dynamic models includes identifying the one or more dynamic models based on one or more properties indicated in the request and a respective type of the one or more digital twins.

[0361] In some embodiments, the method further includes receiving user input related to one or more steps performed in an industrial process associated with 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 a power outage includes a digital twin data store and one or more processors. The digital twin data store stores an industrial environment digital twin with real-world element digital twins embedded therein. The industrial environment digital twin provides a digital twin of the industrial environment. Each real-world element digital twin provides a digital twin of a corresponding real-world element in the industrial environment. The real-world elements include a set of electrically powered elements. The one or more processors are configured to monitor a power supply for the set of electrically powered elements, determine whether the power supply meets criteria for identifying a power loss condition, and, for each of the set of electrically powered elements, represent an effect of the power loss condition on the electrically powered elements using a corresponding digital twin.

[0363] In an embodiment, the one or more processors are further configured to simulate an effect of the power loss condition on each of the real-world elements via the digital twin simulation system and store the effect of the power loss condition via the digital twin data store.

[0364] In an embodiment, the one or more processors are further configured to automatically perform a mitigating action in response to determining that the supplied power meets the identification criteria for a power loss condition.

[0365] In an embodiment, the mitigation action includes selecting a first portion of the real-world element and a second portion of the real-world element, stopping power consumption for the first portion of the real-world element, and continuing power consumption for the second portion of the real-world element.

[0366] In an embodiment, continuing to consume power for the second portion of the real-world elements includes reducing the power consumed by each real-world element to a non-optimal operating level.

[0367] In an embodiment, the non-optimal operating level is the minimum power level required to operate each real-world element.

[0368] In an embodiment, the mitigation action further includes powering a second portion of the real-world elements from stored power, the stored power being present in the industrial environment prior to the occurrence of the power loss condition.

[0369] In an embodiment, the stored power is provided by a battery in the environment.

[0370] In an embodiment, the real-world elements include a third portion of real-world elements, each real-world element within the third portion of real-world elements including a respective battery disposed therein, each battery configured to provide power to the respective real-world element in response to an occurrence of a power loss condition, and the one or more processors further configured to power the second portion of real-world elements using the battery of the third portion of real-world elements.

[0371] In an embodiment, the mitigation action is determined by simulating the effect of a power loss condition on an industrial environment by simulating the effect of the power loss condition on each of the real-world element digital twins using a digital twin simulation system, determining a plurality of potential actions using a cognitive intelligence system, evaluating the effect of each of the plurality of potential actions on the industrial environment using the cognitive intelligence system and the digital twin simulation system, and selecting a mitigation action from the plurality of potential actions based on minimization of a cost function, wherein the plurality of potential actions include maintaining power, reducing power, and shutting down power to each real-world element.

[0372] In an embodiment, minimizing the cost function includes maximizing output from the industrial environment to a downstream process.

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

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

[0375] In an embodiment, the one or more processors are further configured to maintain stored power in the backup power system at a below-capacity level, calculate a probability of a power loss condition occurring before a predetermined period of time has elapsed, and, in response to the probability of a power loss condition occurring exceeding a predetermined threshold, increase stored power in the backup power system to full capacity of the backup power system.

[0376] In an embodiment, the predetermined period of time is the period of time until the backup power system reaches full capacity.

[0377] In an embodiment, calculating the probability of occurrence of a power loss condition includes using weather forecast data.

[0378] According to an aspect of the present disclosure, a system for representing a loss of data connectivity includes a digital twin data store and one or more processors. The digital twin data store stores an industrial environment digital twin with real-world element digital twins embedded therein. The industrial environment digital twin provides a digital twin of the industrial environment. Each real-world element digital twin provides a digital twin of a corresponding real-world element in the industrial environment, the real-world element including a plurality of sensors in data communication with a connected device outside the industrial environment. The one or more processors are configured to monitor connectivity between the real-world elements and the connected device, determine whether the monitored connectivity meets identification criteria for a network connectivity state, and represent an impact of the network connectivity state on each real-world element digital twin.

[0379] In an embodiment, the one or more processors are further configured to simulate the effect of the network connection conditions on each of the real-world elements via the digital twin simulation system and store the effect of the network connection conditions via the digital twin data store.

[0380] In an embodiment, the one or more processors are further configured to automatically perform a mitigating action in response to determining the occurrence of the network connectivity condition.

[0381] In an embodiment, the mitigation operations include determining that the network connection state is a bandwidth-limited state, selecting a first portion of sensors and a second portion of sensors, reducing network communication to the first portion of sensors, and continuing network communication to the second portion of sensors.

[0382] In an embodiment, reducing network communications of the first portion of sensors includes increasing the time interval between communications from the first portion of sensors.

[0383] In an embodiment, reducing network communication of the first portion of sensors includes reducing the amount of information transmitted from the first portion of sensors.

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

[0385] In an embodiment, the mitigation operations include selecting a first portion of real-world elements and a second portion of real-world elements, establishing a direct connection between the first portion of real-world elements and a device external to the industrial environment, and transmitting data from the second portion of real-world elements to the connected device via the direct connection, wherein each real-world element of the first portion of real-world elements includes a wireless communication module and is configured to directly connect to the device external to the industrial environment and transmit data originating from the respective real-world element therethrough.

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

[0387] In an embodiment, the mitigation action is determined by: simulating the effects of network connectivity conditions on the industrial environment by simulating the effects of network connectivity conditions on reporting and control from each of the real-world element digital twins using a digital twin simulation system; determining a plurality of potential actions using a cognitive intelligence system; evaluating the impact of each of the plurality of potential actions on the industrial environment using the cognitive intelligence system and the digital twin simulation system; and selecting a mitigation action from the plurality of potential actions based on minimizing a cost function using the cognitive intelligence system. The plurality of potential actions include reducing communication with each real-world element and establishing an alternative communication mode.

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

[0389] In an embodiment, minimizing the cost function includes minimizing the time period for achieving steady state operation after an outage of network connectivity.

[0390] According to aspects of the present disclosure, a system for representing power source characteristics includes a digital twin data store and one or more processors. The digital twin data store 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 that provides 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 a respective real-world element disposed within the industrial environment. The one or more processors are configured to determine contemporaneous characteristics of the power source in response to an occurrence of a trigger condition, and to update the power source digital twin to represent the contemporaneous characteristics in response to determining the contemporaneous characteristics of the power source.

[0391] In an embodiment, the contemporaneous characteristics of the power source include the power factor supplied to the industrial environment.

[0392] In an embodiment, the contemporaneous characteristics of the power source include power quality.

[0393] In an embodiment, the contemporaneous characteristics of the power source include utility frequency.

[0394] In an embodiment, the one or more processors are further configured to: simulate one or more operating parameters of the real-world elements in response to being provided with contemporaneous characteristics of the industrial environment using the real-world element digital twin via the digital twin simulation system; calculate mitigation actions to be taken by the one or more real-world elements in response to one or more operating parameters deviating from their respective design parameters via the digital twin simulation system; and actuate the mitigation actions in response to detecting contemporaneous characteristics of the power source.

[0395] In an embodiment, simulations and calculations are performed prior to determining the contemporaneous properties.

[0396] In an embodiment, the mitigation action includes activating one of an inductive circuit or a capacitive circuit operably coupled between the power source and the real-world element.

[0397] In an embodiment, the mitigation action includes activating a second power source to provide power to one or more of the real-world elements, the second power source being located within the industrial environment.

[0398] In an embodiment, the second power source is a backup power supply integrated 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 present disclosure. [Brief explanation of the drawings]

[0400] [Figure 1] 1 to 5 each show a portion of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system according to the present disclosure. [Figure 2] 1 to 5 each show a portion of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system according to the present disclosure. [Figure 3] 1 to 5 each show a portion of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system according to the present disclosure. [Figure 4] 1 to 5 each show a portion of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system according to the present disclosure. [Figure 5] 1 to 5 each show a portion of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system according to the present disclosure.

[0401] [Figure 6] FIG. 6 is a diagram of a platform including a local data collection system deployed within an industrial environment to collect data from or regarding elements of the environment, such as machines, components, systems, subsystems, ambient conditions, states, workflows, processes, and other elements, in accordance with the present disclosure.

[0402] [Figure 7] FIG. 7 is a diagram illustrating elements of an industrial data collection system for collecting analog sensor data in an industrial environment according to the present disclosure.

[0403] [Figure 8] FIG. 8 is a diagram of a rotating or vibrating machine having a data acquisition module configured to acquire waveform data in accordance with the present disclosure.

[0404] [Figure 9] FIG. 9 is a diagram of an exemplary three-axis sensor mounted on a motor bearing of an exemplary rotating machine according to the present disclosure.

[0405] [Figure 10] FIG. 10 is a diagram of components and interactions of a data collection architecture including the application of cognitive and machine learning systems to data collection and processing according to the present disclosure.

[0406] [Figure 11] FIG. 11 is a diagram of components and interactions of a data collection architecture including applications of a platform with a cognitive data marketplace according to the present disclosure.

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

[0408] [Figure 13] FIG. 13 is a diagram of components and interactions of a data collection architecture involving the application of a haptic user interface according to the present disclosure.

[0409] [Figure 14] FIG. 14 is a diagram illustrating a multi-format streaming data collection system according to the present disclosure.

[0410] [Figure 15] FIG. 15 is a diagram illustrating combined legacy and streaming data collection and storage according to the present disclosure.

[0411] [Figure 16] FIG. 16 is an illustration of industrial machine sensing using both legacy sensor data processing and updated stream sensor data processing in accordance with the present disclosure.

[0412] [Figure 17] FIG. 17 is a diagram of an industrial machine sensing data processing system that facilitates portal algorithm use and alignment of legacy and streaming sensor data in accordance with the present disclosure.

[0413] [Figure 18] FIG. 18 is a diagram of components and interactions of a data collection architecture including streaming data acquisition equipment receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0414] [Figure 19] FIG. 19 is a diagram of components and interactions of a data collection architecture including an alarm module, an expert analysis module, and a streaming data acquisition appliance with a driver API that facilitates communication with cloud network facilities in accordance with the present disclosure.

[0415] [Figure 20] FIG. 20 is a diagram of the components and interactions of a data collection architecture including streaming data acquisition equipment and a first-in, first-out memory architecture to provide a real-time operating system in accordance with the present disclosure.

[0416] [Figure 21] FIG. 21 is a diagram of the components and interactions of a data collection architecture including multiple streaming data acquisition devices that receive analog sensor signals and digitize them to be acquired by a streaming hub server in accordance with the present disclosure.

[0417] [Figure 22]FIG. 22 is a diagram of the components and interactions of a data collection architecture including a master raw data server that processes new streaming data and data that has already been extracted and processed according to the present disclosure.

[0418] [Figure 23] 23, 24, and 25 are diagrammatic representations of the components and interactions of a data collection architecture including processing, analysis, reporting, and archiving servers that handle new streaming data and already extracted and processed data in accordance with the present disclosure. [Figure 24] 23, 24, and 25 are diagrammatic representations of the components and interactions of a data collection architecture including processing, analysis, reporting, and archiving servers that handle new streaming data and already extracted and processed data in accordance with the present disclosure. [Figure 25] 23, 24, and 25 are diagrammatic representations of the components and interactions of a data collection architecture including processing, analysis, reporting, and archiving servers that handle new streaming data and already extracted and processed data in accordance with the present disclosure.

[0419] [Figure 26] FIG. 26 is a diagram of components and interactions of a data collection architecture including a relational database server, a data archive, and connectivity with cloud network facilities in accordance with the present disclosure.

[0420] [Figure 27] 27-32 are diagrams of components and interactions of a data collection architecture including a virtual streaming data acquisition appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 28]27-32 are diagrams of components and interactions of a data collection architecture including a virtual streaming data acquisition appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 29] 27-32 are diagrams of components and interactions of a data collection architecture including a virtual streaming data acquisition appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 30] 27-32 are diagrams of components and interactions of a data collection architecture including a virtual streaming data acquisition appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 31] 27-32 are diagrams of components and interactions of a data collection architecture including a virtual streaming data acquisition appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure. [Figure 32] 27-32 are diagrams of components and interactions of a data collection architecture including a virtual streaming data acquisition appliance receiving analog sensor signals from an industrial environment connected to a cloud network facility in accordance with the present disclosure.

[0421] [Figure 33] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 34] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 35] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 36]33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 37] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 38] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 39] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure. [Figure 40] 33-40 are diagrams of components and interactions of a data collection architecture including a data channel method and system for industrial machine data collection according to the present disclosure.

[0422] [Figure 41] FIG. 41 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0423] [Figure 42] 42 and 43 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 43] 42 and 43 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure.

[0424] [Figure 44] FIG. 44 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0425] [Figure 45] 45 and 46 are diagrams illustrating one embodiment of a system for data collection according to the present disclosure. [Figure 46]45 and 46 are diagrams illustrating one embodiment of a system for data collection according to the present disclosure.

[0426] [Figure 47] 47 and 48 are diagrams illustrating one embodiment of a system for collecting data comprising multiple data monitoring devices according to the present disclosure. [Figure 48] 47 and 48 are diagrams illustrating one embodiment of a system for collecting data comprising multiple data monitoring devices according to the present disclosure.

[0427] [Figure 49] FIG. 49 illustrates one embodiment of a data monitoring device incorporating sensors according to the present disclosure.

[0428] [Figure 50] 50 and 51 illustrate embodiments of a data monitoring device in communication with an external sensor according to the present disclosure. [Figure 51] 50 and 51 illustrate embodiments of a data monitoring device in communication with an external sensor according to the present disclosure.

[0429] [Figure 52] FIG. 52 illustrates an embodiment of a data monitoring device that adds further details to the signal evaluation circuitry according to the present disclosure.

[0430] [Figure 53] FIG. 53 illustrates an embodiment of a data monitoring device that adds further details to the signal evaluation circuitry according to the present disclosure.

[0431] [Figure 54] FIG. 54 illustrates an embodiment of a data monitoring device that adds further details to the signal evaluation circuitry according to the present disclosure.

[0432] [Figure 55] FIG. 55 illustrates an embodiment of a system for data collection according to the present disclosure.

[0433] [Figure 56] FIG. 56 illustrates an embodiment of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0434] [Figure 57] FIG. 57 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0435] [Figure 58] 58 and 59 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 59] 58 and 59 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure.

[0436] [Figure 60] 60 and 61 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 61] 60 and 61 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure.

[0437] [Figure 62] 62 and 63 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 63] 62 and 63 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure.

[0438] [Figure 64] 64 and 65 are diagrams illustrating embodiments of a system for data collection including multiple data monitoring devices according to the present disclosure. [Figure 65] 64 and 65 are diagrams illustrating embodiments of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0439] [Figure 66] FIG. 66 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0440] [Figure 67] 67 and 68 illustrate embodiments of data monitoring devices according to the present disclosure. [Figure 68] 67 and 68 illustrate embodiments of data monitoring devices according to the present disclosure.

[0441] [Figure 69] FIG. 69 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0442] [Figure 70] FIG. 70 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0443] [Figure 71] 71 and 72 are diagrams illustrating an embodiment of a system for data collection according to the present disclosure. [Figure 72] 71 and 72 are diagrams illustrating an embodiment of a system for data collection according to the present disclosure.

[0444] [Figure 73] 73 and 74 illustrate an embodiment of a system for data collection including multiple data monitoring devices according to the present disclosure. [Figure 74] 73 and 74 illustrate an embodiment of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0445] [Figure 75] FIG. 75 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0446] [Figure 76] 76 and 77 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure. [Figure 77] 76 and 77 are diagrams illustrating an embodiment of a data monitoring device according to the present disclosure.

[0447] [Figure 78] FIG. 78 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0448] [Figure 79] 79 and 80 illustrate an embodiment of a system for data collection according to the present disclosure. [Figure 80] 79 and 80 illustrate an embodiment of a system for data collection according to the present disclosure.

[0449] [Figure 81] 81 and 82 are diagrams illustrating an embodiment of a system for data collection including multiple data monitoring devices according to the present disclosure. [Figure 82] 81 and 82 are diagrams illustrating an embodiment of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0450] [Figure 83] FIG. 83 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0451] [Figure 84] 84 and 85 illustrate embodiments of data monitoring devices according to the present disclosure. [Figure 85] 84 and 85 illustrate embodiments of data monitoring devices according to the present disclosure.

[0452] [Figure 86] FIG. 86 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0453] [Figure 87] 87 and 88 illustrate an embodiment of a system for data collection according to the present disclosure. [Figure 88] 87 and 88 illustrate an embodiment of a system for data collection according to the present disclosure.

[0454] [Figure 89] 89 and 90 illustrate embodiments of a system for data collection including multiple data monitoring devices according to the present disclosure. [Figure 90] 89 and 90 illustrate embodiments of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0455] [Figure 91] FIG. 91 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0456] [Figure 92] 92 and 93 illustrate embodiments of a data monitoring device according to the present disclosure. [Figure 93] 92 and 93 illustrate embodiments of a data monitoring device according to the present disclosure.

[0457] [Figure 94] FIG. 94 illustrates an embodiment of a data monitoring device according to the present disclosure.

[0458] [Figure 95] 95 and 96 illustrate an embodiment of a system for data collection according to the present disclosure. [Figure 96] 95 and 96 illustrate an embodiment of a system for data collection according to the present disclosure.

[0459] [Figure 97] 97 and 98 illustrate embodiments of a system for data collection including multiple data monitoring devices according to the present disclosure. [Figure 98] 97 and 98 illustrate embodiments of a system for data collection including multiple data monitoring devices according to the present disclosure.

[0460] [Figure 99]99-101 are diagrammatic illustrations of components and interactions of a data collection architecture including a collector of route templates and routing data collectors in an industrial environment according to the present disclosure. [Figure 100] 99-101 are diagrammatic illustrations of components and interactions of a data collection architecture including a collector of route templates and routing data collectors in an industrial environment according to the present disclosure. [Figure 101] 99-101 are diagrammatic illustrations of components and interactions of a data collection architecture including a collector of route templates and routing data collectors in an industrial environment according to the present disclosure.

[0461] [Figure 102] FIG. 102 is a diagram illustrating a monitoring system employing a data collection band according to the present disclosure.

[0462] [Figure 103] FIG. 103 illustrates a system for using vibrations and other noises in predicting conditions and outcomes according to the present disclosure.

[0463] [Figure 104] FIG. 104 is a diagram illustrating a system for data collection in an industrial environment according to the present disclosure.

[0464] [Figure 105] FIG. 105 is a diagram illustrating an apparatus for data collection in an industrial environment according to the present disclosure.

[0465] [Figure 106] FIG. 106 is a schematic flow diagram illustrating a procedure for data collection in an industrial environment according to the present disclosure.

[0466] [Figure 107] FIG. 107 is a diagram illustrating a system for data collection in an industrial environment according to the present disclosure.

[0467] [Figure 108] FIG. 108 illustrates an apparatus for data collection in an industrial environment according to the present disclosure.

[0468] [Figure 109] FIG. 109 is a schematic flow diagram illustrating a procedure for data collection in an industrial environment according to the present disclosure.

[0469] [Figure 110] FIG. 110 illustrates industry-specific feedback in an industrial environment according to the present disclosure.

[0470] [Figure 111] FIG. 111 is a diagram depicting an example user interface for smart band configuration of a system for data collection in an industrial environment in accordance with the present disclosure.

[0471] [Figure 112] FIG. 112 illustrates a graphical approach 11300 for inverse calculations according to the present disclosure.

[0472] [Figure 113] FIG. 113 illustrates a wearable tactile user interface device for providing a user with tactile stimuli responsive to data collected in an industrial environment by a system adapted to collect data in an industrial environment in accordance with the present disclosure.

[0473] [Figure 114] FIG. 114 illustrates an augmented reality display of a heat map based on data collected in an industrial environment by a system adapted to collect data in the environment in accordance with the present disclosure.

[0474] [Figure 115] FIG. 115 is a diagram illustrating an augmented reality display with real-time data overlaid on a view of an industrial environment in accordance with the present disclosure.

[0475] [Figure 116] FIG. 116 is a diagram illustrating components of a user interface display and a neural network in a graphical user interface according to the present disclosure.

[0476] [Figure 117] FIG. 117 is a diagram of components and interactions of a data collection architecture including swarming data collectors and a sensor mesh protocol in an industrial environment according to the present disclosure.

[0477] [Figure 118] FIG. 118 is a diagram illustrating a data collection system according to some aspects of the present disclosure.

[0478] [Figure 119] FIG. 119 illustrates a system for self-organized network-sensitive data collection in an industrial environment according to the present disclosure.

[0479] [Figure 120] FIG. 120 illustrates an apparatus for self-organized network-sensitive data collection in an industrial environment according to the present disclosure.

[0480] [Figure 121] FIG. 121 illustrates an apparatus for self-organized network-sensitive data collection in an industrial environment according to the present disclosure.

[0481] [Figure 122] FIG. 122 illustrates an apparatus for self-organized network-sensitive data collection in an industrial environment according to the present disclosure.

[0482] [Figure 123] 123 and 124 are diagrams illustrating embodiments of transmission conditions according to the present disclosure. [Figure 124]123 and 124 are diagrams illustrating embodiments of transmission conditions according to the present disclosure.

[0483] [Figure 125] FIG. 125 illustrates an embodiment of a sensor data transmission protocol according to the present disclosure.

[0484] [Figure 126] 126 and 127 are diagrams illustrating embodiments of benchmarking data according to the present disclosure. [Figure 127] 126 and 127 are diagrams illustrating embodiments of benchmarking data according to the present disclosure.

[0485] [Figure 128] FIG. 128 illustrates an embodiment of a system for data collection and storage in an industrial environment according to the present disclosure.

[0486] [Figure 129] FIG. 129 illustrates an embodiment of an apparatus for self-organizing storage for data collection in an industrial system according to the present disclosure.

[0487] [Figure 130] FIG. 130 illustrates an embodiment of a storage time definition according to the present disclosure.

[0488] [Figure 131] FIG. 131 is a diagram illustrating an embodiment of a data resolution description according to the present disclosure.

[0489] [Figure 132] 132 and 133 are diagrams of an apparatus for self-organizing network coding for data collection in an industrial system according to the present disclosure. [Figure 133] 132 and 133 are diagrams of an apparatus for self-organizing network coding for data collection in an industrial system according to the present disclosure.

[0490] [Figure 134] 134 and 135 are diagrams of a data marketplace that interacts with data collection in an industrial system according to the present disclosure. [Figure 135] 134 and 135 are diagrams of a data marketplace that interacts with data collection in an industrial system according to the present disclosure.

[0491] [Figure 136] FIG. 136 is a diagram illustrating a smart heating system as an element in a network for an Industrial Internet of Things ecosystem according to the present disclosure.

[0492] [Figure 137] FIG. 137 is a diagram illustrating the architecture of an Industrial Internet of Things solution according to the present disclosure, its components, and functional relationships.

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

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

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

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

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

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

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

[0500] [Figure 145] FIG. 145 is a schematic diagram illustrating an example of a backend system receiving sensor data from sensor kits deployed in an industrial environment, according to some embodiments of the present disclosure.

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

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

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

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

[0505] [Figure 150] FIG. 150 is a flowchart illustrating an example set of operations for 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] [Figure 151] 151-155 are schematic diagrams illustrating different configurations of sensor kits according to some embodiments of the present disclosure. [Figure 152] 151-155 are schematic diagrams illustrating different configurations of sensor kits according to some embodiments of the present disclosure. [Figure 153] 151-155 are schematic diagrams illustrating different configurations of sensor kits according to some embodiments of the present disclosure. [Fig. 154] 151-155 are schematic diagrams illustrating different configurations of sensor kits according to some embodiments of the present disclosure. [Figure 155] 151-155 are schematic diagrams illustrating different configurations of sensor kits according to some embodiments of the present disclosure.

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

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

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

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

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

[0512] [Figure 161] 161-164 illustrate an embodiment of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure. [Figure 162] 161-164 illustrate an embodiment of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure. [Figure 163] 161-164 illustrate an embodiment of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure. [Fig. 164] 161-164 illustrate an embodiment of a system for using one or more wearable devices for mobile data collection in accordance with the present disclosure.

[0513] [Figure 165] 165-167 illustrate an embodiment of a system for using one or more mobile robots and / or mobile vehicles for mobile data collection according to the present disclosure. [Figure 166]165-167 illustrate an embodiment of a system for using one or more mobile robots and / or mobile vehicles for mobile data collection according to the present disclosure. [Figure 167] 165-167 illustrate an embodiment of a system for using one or more mobile robots and / or mobile vehicles for mobile data collection according to the present disclosure.

[0514] [Figure 168] 168-171 illustrate embodiments of a system for using one or more handheld devices for mobile data collection according to the present disclosure. [Figure 169] 168-171 illustrate embodiments of a system for using one or more handheld devices for mobile data collection according to the present disclosure. [Figure 170] 168-171 illustrate embodiments of a system for using one or more handheld devices for mobile data collection according to the present disclosure. [Figure 171] 168-171 illustrate embodiments of a system for using one or more handheld devices for mobile data collection according to the present disclosure.

[0515] [Fig. 172] 172-174 are diagrams illustrating embodiments of a computer vision system according to the present disclosure. [Figure 173] 172-174 are diagrams illustrating embodiments of a computer vision system according to the present disclosure. [Fig. 174] 172-174 are diagrams illustrating embodiments of a computer vision system according to the present disclosure.

[0516] [Figure 175] 175-176 are diagrams illustrating an embodiment of a deep learning system for training a computer vision system according to the present disclosure. [Figure 176] 175-176 are diagrams illustrating an embodiment of a deep learning system for training a computer vision system according to the present disclosure.

[0517] [Figure 177] Figure 177 is a diagram illustrating the predictive maintenance ecosystem network architecture.

[0518] [Figure 178] Figure 178 depicts using machine learning to find service workers for the predictive maintenance ecosystem in Figure 177.

[0519] [Figure 179] Figure 179 shows parts and service ordering in a predictive maintenance ecosystem.

[0520] [Figure 180] FIG. 180 is a diagram illustrating the placement of smart RFID elements in an industrial machinery environment.

[0521] [Figure 181] Figure 181 shows a generalized data structure of machine information in smart RFID.

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

[0523] [Figure 183] FIG. 183 is a diagram showing an example of data stored in a smart RFID.

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

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

[0526] [Figure 186] FIG. 186 illustrates an online maintenance management system with interfaces for data sources to update information in the online maintenance management system's data storage.

[0527] [Figure 187] Figure 187 illustrates a distributed ledger for predictive maintenance information and its role-specific access methods.

[0528] [Figure 188] FIG. 188 illustrates a process for capturing an image of a portion of industrial machinery.

[0529] [Figure 189] Figure 189 shows the process of using machine learning on images to recognize what is likely to be the internal structure of industrial machinery.

[0530] [Figure 190] FIG. 190 is a diagram showing a knowledge graph of predictive maintenance collection information.

[0531] [Figure 191] FIG. 191 illustrates an artificial intelligence system that generates service recommendations and the like based on predictive maintenance analysis.

[0532] [Figure 192] FIG. 192 illustrates a predictive maintenance timeline overlaid with a preventive maintenance timeline.

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

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

[0535] [Figure 195] FIG. 195 is a diagram of the process for rating procedures.

[0536] [Figure 196] Figure 196 is a diagram of blockchain applied to transactions in the predictive maintenance ecosystem.

[0537] [Figure 197] FIG. 197 shows a transfer function that facilitates converting vibration data into precise units.

[0538] [Figure 198] FIG. 198 shows a table that facilitates associating vibration data with severity units.

[0539] [Figure 199] Figure 199 shows a composite frequency graph of the conventional vibration rating and the rating in severity units.

[0540] [Figure 200] FIG. 200 shows a rendering of a portion of an industrial machine for use in an electronic user interface for depicting and locating severity units and related information regarding rotating components of the industrial machine.

[0541] [Figure 201] FIG. 201 shows a data table of rotating component design parameters used to predict maintenance events.

[0542] [Figure 202] FIG. 202 is a flowchart for predicting maintenance of at least one of gears, motors, and roller bearings based on severity units such as the number of gear teeth and the number of actuators.

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

[0544] [Figure 204] FIG. 204 is a schematic diagram showing additional details, components, subsystems, and other elements of any implementation of the exemplary platform of FIG. 203.

[0545] [Figure 205] FIG. 205 is a schematic diagram illustrating a robotic process automation (hereinafter "RPA") system of the exemplary platform of FIG. 203.

[0546] [Figure 206] FIG. 206 is a schematic diagram illustrating the opportunity mining system and adaptive intelligence layer of the exemplary platform of FIG. 203.

[0547] [Figure 207] FIG. 207 is a schematic diagram illustrating optional elements of the adaptive intelligent systems layer that facilitate improved edge intelligence for the exemplary platform of FIG. 203.

[0548] [Figure 208] FIG. 208 is a schematic diagram illustrating optional elements of the industry entity-oriented data storage system layer of the exemplary platform of FIG. 203.

[0549] [Figure 209] FIG. 209 is a schematic diagram illustrating a robotic process automation system for the exemplary platform of FIG. 203.

[0550] [Figure 210]FIG. 210 is a schematic diagram of an example system for data processing in an industrial environment utilizing a protocol adapter according to some aspects of the present disclosure.

[0551] [Figure 211] FIG. 211 is another schematic diagram showing additional components and elements of the exemplary system of FIG. 210; and

[0552] [Figure 212] FIG. 212 illustrates an example connection attempt for the example system of FIG. 210, according to some aspects of the disclosure.

[0553] [Figure 213] FIG. 213 is a schematic diagram illustrating an example of the architecture of a digital twin system according to an embodiment of the present disclosure.

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

[0555] [Figure 215] FIG. 215 is a schematic diagram illustrating an example of a digital twin I / O system interfacing with an environment, a digital twin system, and / or components thereof to provide bidirectional transfer of data between coupled components, according to an embodiment of the present disclosure.

[0556] [Figure 216] FIG. 216 is a schematic diagram illustrating an example set of identification states for an industrial environment that a digital twin system may identify and / or store for access by an intelligent system (e.g., a cognitive intelligence system) or user of the digital twin system, according to an embodiment of the present disclosure.

[0557] [Figure 217]FIG. 217 is a schematic diagram illustrating an exemplary embodiment of a method 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 an embodiment of the present disclosure.

[0558] [Figure 218] FIG. 218 is a display diagram illustrating an example embodiment of a display interface of the present disclosure rendering a digital twin of a dryer centrifuge with information related to the dryer centrifuge in accordance with an embodiment of the present disclosure.

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

[0560] [Figure 220] FIG. 220 is a schematic diagram illustrating an example embodiment of a method for updating a set of vibration severity unit values ​​of a machine component, such as a bearing, in a digital twin of a machine on behalf of a client application, in accordance with an embodiment of the present disclosure.

[0561] [Figure 221] FIG. 221 is a schematic diagram illustrating an example embodiment of a method for updating a set of failure probability values ​​in a digital twin of a machine part on behalf of a client application, according to an embodiment of the present disclosure.

[0562] [Figure 222] FIG. 222 is a schematic diagram illustrating an example embodiment of a method for updating a set of downtime probability values ​​for machines in a digital twin of a manufacturing facility on behalf of a client application, in accordance with an embodiment of the present disclosure.

[0563] [Figure 223] FIG. 223 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of shutdown probability values ​​for manufacturing equipment in an enterprise's digital twin on behalf of a client application, according to an embodiment of the present disclosure.

[0564] [Figure 224] FIG. 224 is a schematic diagram illustrating an exemplary embodiment of a method for updating a cost set of machine downtime values ​​in a digital twin of a manufacturing facility, according to an embodiment of the present disclosure.

[0565] [Figure 225] FIG. 225 is a schematic diagram illustrating an example embodiment of a method for updating one or more manufacturing KPI values ​​in a digital twin of a manufacturing facility on behalf of a client application according to an embodiment of the present disclosure.

[0566] [Figure 226] FIG. 226 is a display diagram illustrating a further exemplary embodiment of a display interface of the present disclosure rendering a digital twin of a dryer centrifuge with information related to its drive components, in accordance with an embodiment of the present disclosure.

[0567] [Figure 227] FIG. 227 is a display diagram illustrating a further exemplary embodiment of a display interface of the present disclosure that provides a digital twin showing components of a vibration according to an embodiment of the present disclosure.

[0568] [Figure 228] FIG. 228 is a display diagram illustrating a further exemplary embodiment of a display interface of the present disclosure providing selection of digital twins showing various components experiencing faults, according to an embodiment of the present disclosure.

[0569] [Figure 229]FIG. 229 is a display diagram illustrating an example embodiment of a display interface of the present disclosure rendering a digital twin of views each incorporating a connected machine having a drive bearing according to an embodiment of the present disclosure.

[0570] [Figure 230] FIG. 230 is a display diagram illustrating an exemplary embodiment of a display interface of the present disclosure, each rendering views incorporating a connected machine having a drive bearing exhibiting off-nominal motion in accordance with an embodiment of the present disclosure.

[0571] [Figure 231] FIG. 231 is a display diagram illustrating an exemplary embodiment of a display interface of the present disclosure rendering a digital twin showing a drive bearing corrected to nominal motion in accordance with an embodiment of the present disclosure.

[0572] [Figure 232] FIG. 232 is a display diagram showing an example of a display interface of the present disclosure rendering digital twins each incorporating a connected machine such as a motor and a mill having a drive bearing exhibiting off-nominal motion according to an embodiment of the present disclosure.

[0573] [Figure 233] FIG. 233 is a display diagram illustrating an exemplary embodiment of a display interface of the present disclosure rendering a digital twin showing a drive bearing corrected to nominal motion in accordance with an embodiment of the present disclosure.

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

[0575] Although detailed embodiments of the present disclosure are disclosed herein, it should be understood that the disclosed embodiments are merely exemplary and can be embodied in various forms. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a basis for the claims and as representative basis for teaching those skilled in the art how to variously employ the present disclosure in substantially any appropriately detailed structure.

[0576] The methods and systems described herein for streaming, collecting, processing, and storing industrial machine sensor data may be configured to operate with existing data collection, processing, and storage systems while maintaining access to data in existing formats, frequency ranges, and resolutions that are compatible with the existing data collection systems. While the data streaming facilities described herein for industrial machine sensors may collect large amounts of data from sensors at wider frequency ranges and higher resolutions than existing data collection systems, the methods and systems may be employed to provide access to data from streams of data representing one or more frequency ranges and / or one or more resolution lines that are intentionally compatible with the existing systems. Furthermore, portions of the streamed data may be identified, extracted, stored, and / or transferred to existing data processing systems to facilitate operation of the existing data processing systems that use the existing collection-based data in a manner that substantially matches their operation. In this way, newly deployed systems for sensing aspects of industrial machinery, such as aspects of moving parts of industrial machinery, can facilitate the continued use of existing sensed data processing facilities, algorithms, models, pattern recognizers, user interfaces, and the like.

[0577] The higher resolution stream data can be configured to represent a particular frequency, frequency range, format, and / or resolution by identifying existing frequency ranges, formats, and / or resolutions, such as by accessing data structures that define these aspects of the existing data. This configured stream data can be stored in a data structure that is compatible with the existing sensed data structure, so that existing processing systems and equipment can access and process the data substantially as if it were existing data. One approach to adapting streamed data for compatibility with existing sensed data includes aligning the streamed data with the existing data, such that portions of the streamed data that align with the existing data are extracted, stored, and made available for processing by existing data processing methods. Alternatively, the data processing methods may be configured to process portions of the corresponding stream data, such as by aligning with the existing data, using methods that implement substantially similar functionality to methods used to process the existing data, such as methods that process data including a particular frequency range or a particular resolution.

[0578] The method used to process existing data may be related to particular characteristics of the sensed data, such as a particular frequency range, the source of the data, etc. As an example, a method for processing bearing sensing information for moving parts of industrial machinery may be capable of processing data from bearing sensors that fall within a particular frequency range. The method can therefore be identified at least in part by these characteristics of the data being processed. Thus, a data processing system can select an appropriate method given a set of conditions, such as the moving equipment being sensed, the type of industrial machinery, and the frequency of the sensed data. Also, given such conditions, the industrial machine's data sensing and processing equipment can configure elements such as data filters, routers, and processors to process data that meets the conditions.

[0579] FIGS. 1-5 depict a portion of an overall view of an Industrial Internet of Things (IoT) data collection, monitoring, and control system 10. FIG. 2 depicts a mobile ad hoc network ("MANET") 20, which may form a secure, temporary network connection 22 (sometimes connected, sometimes isolated) with a cloud 30 or other remote networking system, allowing network functions to occur on the MANET 20 within the environment without the need for an external network, while at other times transmitting information to and from a central location. This allows industrial environments to take advantage of the benefits of networking and control technologies while ensuring security, such as preventing cyberattacks. The MANET 20 may use cognitive radio technologies 40, including routers 42, MAC 44, and physical layer technologies 46, which form the equivalent of IP protocols. In certain embodiments, the systems depicted in FIGS. 1-5 provide network-sensitive or network-aware transport of data over the network to and from data collection devices or heavy industrial machinery.

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

[0581] FIG. 1 depicts a server-based portion of an industrial IoT system that may be deployed in the cloud or on the premises of an enterprise owner or operator. The server portion includes network coding (including self-organizing network coding and / or auto-configuration) that can configure a network coding model based on feedback measures, network conditions, etc., to efficiently transport large amounts of data over the network to and from data collection systems and the cloud. The network coding, as depicted in FIG. 1, may provide a wide range of functionality for intelligence, analytics, remote control, remote operation, remote optimization, various storage configurations, etc. The various storage configurations may include distributed ledger storage for supporting transaction data or other elements of the system.

[0582] 5 depicts a programmatic data marketplace 70, which may be a self-organizing marketplace, such as for making available data collected in an industrial environment, including data collectors, data pools, distributed ledgers, and other elements disclosed herein. Additional details regarding the various components and subcomponents of FIGS. 1-5 are provided throughout this disclosure.

[0583] Referring to FIG. 6, one embodiment of a platform 100 may include a local data collection system 102 disposed in an environment 104, such as an industrial environment similar to that shown in FIG. 3, to collect data from or about elements of the environment, such as machines, components, systems, subsystems, surroundings, conditions, workflows, processes, and other elements. The platform 100 may be connected to or include part of the industrial IoT data collection, monitoring, and control system 10 depicted in FIGS. 1-5. The platform 100 may include a network data transport system 108 for transporting data to and from the local data collection system 102 over a network 110, such as to a host processing system 112 disposed in a cloud computing environment or on an enterprise premises, or comprised of distributed components that interact with each other to process data collected by the local data collection system 102. The host processing system 112, sometimes referred to for convenience as the host system 112, may include various systems, components, methods, processes, equipment, etc., for enabling automated or automation-assisted processing of data, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements within the local environment 104 or network 110. The platform 100 may also include one or more local autonomous systems, such as for enabling autonomous operation based on application of a set of rules or models to input data from the local data collection system 102 or one or more input sources 116, which may constitute information feeds and inputs from a wide range of sources, including the local environment 104, the network 110, the host system 112, or one or more external systems, databases, etc. The platform 100 may also include one or more intelligent systems 118, which may be located in, integrated with, or serve as input to one or more components of the platform 100.Details of these and other components of platform 100 are provided throughout this disclosure.

[0584] The intelligent system 118 may include a cognitive system 120 that enables some degree of cognitive operation as a result of the coordination of processing elements, such as in a mesh, peer-to-peer, ring, serial, or other architecture, where one or more node elements coordinate with other node elements to provide collective, coordinated operation to assist in processing, communication, data collection, etc. The MANET 20 depicted in FIG. 2 may also use cognitive radio technologies, including routers 42, MAC 44, physical layer technology 46, etc., that form the equivalent of the IP protocol. In one example, the cognitive system technology stack may include the example disclosed in U.S. Patent No. 8,060,017 to Schlicht et al., issued November 15, 2011, and incorporated by reference as if fully set forth herein.

[0585] The intelligent system may include a machine learning system 122, such as for learning from one or more datasets. The one or more datasets may include information collected using the local data collection system 102 or other information from input sources 116, such as for recognizing states, objects, events, patterns, conditions, etc., which may in turn be used for processing by the host system 112 as input to components of the platform 100 and portions of the industrial IoT data collection, monitoring, and control system 10, etc. Learning may be human-supervised, such as using one or more input sources 116 to provide datasets with information about items to learn, or may be fully automated. Machine learning may use one or more models, rules, semantic understanding, workflows, or other structured or semi-structured understanding of the world, such as for automated optimization of control of a system or process based on feedback or feedforward to a model of the system or process behavior. One such machine learning technique for semantic and contextual understanding, workflow, or other structured or semi-structured understanding is disclosed in U.S. Patent No. 8,200,775, issued June 12, 2012, to Moore, and incorporated by reference as if fully set forth herein. Machine learning can be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, etc. (e.g., changing functions within the model) based on feedback (e.g., regarding the model's success in a given situation) or iteration (e.g., in a recursive process). Machine learning can also be performed in the absence of a basic model when there is insufficient understanding of the system's underlying structure or behavior, when insufficient data is available, or in other cases where this is preferable for various reasons.That is, input sources can be weighted, structured, etc. within a machine learning facility without any a priori understanding of the structure, and results (e.g., results based on measures of success for achieving various desired objectives) can be continuously fed to the machine learning system to learn how to achieve a desired objective. For example, defect recognition, pattern recognition, model or feature development, rule development, performance optimization, failure rate minimization, profit optimization, resource utilization optimization, flow optimization (e.g., traffic flow), or many other parameters associated with successful outcomes (e.g., outcomes across a wide range of environments) can be learned. Machine learning can use genetic programming techniques to promote or demote one or more input sources, structures, data types, objects, weights, nodes, links, or other elements based on feedback (so that successful elements emerge over a series of generations). For example, alternative available sensor inputs for the data collection system 102 may be arranged in alternative configurations and permutations such that the system, using generic programming techniques over a series of data collection events, determines what permutation provides successful results based on various conditions (conditions of the components of the platform 100, conditions of the network 110, conditions of the data collection system 102, conditions of the environment 104, etc.). In embodiments, local machine learning may sequentially turn one or more sensors in the multi-sensor data collection device 102 on or off over time while tracking successful outcomes such as contribution to successful failure prediction, contribution to a performance indicator (efficiency, effectiveness, return on investment, yield, etc.), contribution to optimization of one or more parameters, identification of patterns (associated with threats, failure modes, success modes, etc.). For example, the system can derive what set of sensors should be turned on or off under given conditions to achieve the highest value utilization of the data collector 102.In embodiments, similar techniques may be used to handle optimizing the transport of data in platform 100 (e.g., network 110) by using general purpose programming or other machine learning techniques to learn the configuration of network elements (e.g., configuration of network transport paths, configuration of network coding types and architectures, configuration of network security elements, etc.).

[0586] In an embodiment, the local data collection system 102 may include a high-performance, multi-sensor data collector with numerous novel features for collecting and processing analog and other sensor data. In an embodiment, the local data collection system 102 may be deployed in an industrial facility such as that depicted in FIG. 3. The local data collection system 102 may also be deployed monitoring other machines, such as machine 2200. The data collection system 102 may have an on-board intelligent system 118 (e.g., learning to optimize the configuration and operation of the data collection device, such as configuring permutations and combinations of sensors based on context and conditions). In one example, the data collection system 102 includes a crosspoint switch 130 or other analog switch. The automated, intelligent configuration of the local data collection system 102 may be based on various types of information, such as information from various input sources, including available power, power requirements of sensors, value of collected data (e.g., based on feedback information from other elements of the platform 100), relative value of information (e.g., value based on availability of other sources of the same or similar information), availability of power (e.g., to power sensors), network conditions, ambient conditions, operating states, operating contexts, operating events, and many others.

[0587] FIG. 7 illustrates elements and subcomponents of a data acquisition and analysis system 1100 for sensor data (e.g., analog sensor data) collected in an industrial environment. As depicted in FIG. 7, embodiments of the methods and systems disclosed herein may include hardware with several different modules, starting with a multiplexer (“MUX”) main board 1104. In embodiments, there may be a MUX option board 1108. The MUX 114 main board is where sensors connect to the system. These connections are on the top for ease of installation. There are then numerous settings on the underside of this board and on the Mux option board 1108, which attaches to the MUX main board 1104 via two headers, one on each end of the board. In embodiments, the Mux option board has a male header that mates with a female header on the Mux main board. This allows MUX option boards to be stacked, thereby reducing the footprint.

[0588] In an embodiment, the main Mux board and / or MUX option board then connect via cables to mother (e.g., having four simultaneous channels) and daughter (e.g., having four additional channels for a total of eight channels) analog boards 1110, where some of the signal conditioning (e.g., hardware integration) occurs. The signal then travels from the analog board 1110 to an anti-aliasing board (not shown), where some of the potential aliasing is removed. The remaining portion of the aliasing removal occurs on the delta-sigma board 1112, which performs more of the aliasing countermeasures, along with other signal conditioning and digitization. The data then travels to the Jennie board 1114 for further digitization and communication to a computer via USB or Ethernet. In an embodiment, replacing the Jennie board 1114 with a Pic board 1118 allows for more advanced and efficient data collection and communication. Once the data is in the computer software 1102, the computer software 1102 can manipulate the data to display trends, spectra, waveforms, statistics, and analysis.

[0589] In embodiments, the system captures any type of data, from volts to 4-20 mA signals. In embodiments, open formats for data storage and communication can be used. In some instances, certain parts of the system can be dedicated to research and data specifically related to analysis and reporting. In embodiments, smart band analysis breaks down data into easy-to-analyze pieces and can be combined with other smart bands to create new, simpler, yet more sophisticated analyses. In embodiments, this unique information is captured and depicted using graphics, as pictorial depictions are more useful to users. In embodiments, complex programs and user interfaces are simplified, allowing anyone to manipulate data like an expert.

[0590] In embodiments, the system essentially operates in a big loop. The system begins with software with a general user interface ("GUI") 1124. In embodiments, rapid route creation can utilize hierarchical templates. In embodiments, the GUI is created so that any general user can enter the information themselves using simple templates. Once the templates are created, users can copy and paste what they need. It is also possible for users to develop their own templates for future ease of use and knowledge institutionalization. Once the user has entered all the information and connected all the sensors, the user can begin acquiring data with the system.

[0591] Method and system embodiments disclosed herein can include unique electrostatic protection for trigger and vibration inputs. Proper transducer and trigger input protection is required in many critical industrial environments, such as rotating machinery and slow-speed balancing with large belts, where large electrostatic forces that can harm electrical equipment can build up. Embodiments describe a low-cost yet efficient method for providing such protection without the need for external auxiliary equipment.

[0592] Typically, vibration data collectors are not designed to handle large input voltages due to expense and often not required. As technology improves and monitoring costs plummet, there is a need for these data collectors to capture many different types of RPM data. In this embodiment, rather than using traditional reed relays, a method is used that uses established OptoMOS technology, which allows for pre-switching of high-voltage signals. Many historical concerns about nonlinear zero crossings and other nonlinear solid-state behavior are eliminated when passing weak, buffered analog signals. Furthermore, in this embodiment, the PCB wiring topology places all of the input circuitry for each individual channel as close as possible to the input connector. In this embodiment, unique electrostatic protection for the trigger and vibration inputs is located on the front of the Mux and DAQ hardware, allowing for the dissipation of charge buildup as signals pass from the sensor to the hardware. In this embodiment, the Mux and analog board support high-amperage inputs using a design topology consisting of wider traces and solid-state relays for the upfront circuitry.

[0593] In some systems, the multiplexer is an afterthought, and the quality of the signal coming out of the multiplexer is not taken into consideration. A poor multiplexer can result in a loss of signal quality of 30 dB or more. Thus, using a 24-bit DAQ with a 110 dB signal-to-noise ratio can result in significant signal quality loss, and if the signal-to-noise ratio at the mux drops to 80 dB, it may be much worse than a 16-bit system from 20 years ago. In this system embodiment, a key part of the stage before the mux is upfront signal conditioning on the mux to improve the signal-to-noise ratio. In embodiments, signal conditioning (range / gain control, integration, filtering, etc.) may be performed on not only vibration but also other signal inputs before the mux switching to achieve the best signal-to-noise ratio.

[0594] In addition to providing a better signal, in embodiments, a multiplexer may also provide continuous monitoring alarm functionality. True continuous systems monitor all sensors constantly but tend to be expensive. Typical multiplexer systems monitor only a set number of channels at a time, switching between banks of larger sensor sets. As a result, sensors not currently being sampled are not being monitored, and rising levels go unnoticed to the user. In embodiments, a multiplexer can have continuous monitoring alarm functionality even when the data acquisition ("DAQ") is not monitoring the inputs by placing circuitry in the multiplexer that can measure the levels of input channels for known alarm conditions. A continuous monitor mux bypass allows channels not sampled by the mux system to be continuously monitored for critical alarm conditions via some trigger condition using a filtered peak-hold circuit or functionally similar circuit, but these conditions are passed to the monitoring system in a convenient manner using hardware interrupts or other means. This allows for continuous monitoring of the system, although without the ability to immediately obtain data about a problem as with a true continuous system. In embodiments, this alarm functionality may be combined with adaptive scheduling techniques for continuous monitoring, allowing the continuous monitoring system software to adapt and adjust data collection sequences based on statistics, analytics, data alarms, and dynamic analysis, allowing the system to rapidly collect dynamic spectral data from alarm sensors immediately after an alarm sounds.

[0595] Another limitation of typical multiplexers is their limited channel count. In an embodiment, distributed Complex Programmable Logic Device (CPLD) chips with dedicated buses for logic control of multiple Muxes and data acquisition units allow the CPLD to control multiple Muxes and DAQs, eliminating the need for a limited number of channels. Interfacing with multiple types of predictive maintenance and vibration transducers requires a lot of switching. This includes AC / DC coupling, 4-20 interfaces, integrated electronic piezoelectric transducers, channel power-down (to conserve op-amp power), and single-ended or differential grounding options. Also required are controls for digital pots for range and gain adjustment, switches for hardware integration, AA filtering, and triggering. This logic can be implemented on a series of CPLD chips strategically placed for the task at hand. A single, large CPLD requires long circuit routes, which allows for greater density within a single, large CPLD. In an embodiment, distributed CPLDs address these concerns while also providing significant flexibility. A bus is created, with each CPLD having its own device address, with fixed assignments. In an embodiment, multiplexers and DAQs are stacked to provide additional input / output channels to the system. Multiple boards (e.g., multiple Mux boards) have jumpers available to set multiple addresses. In another example, up to eight boards can be jumper-programmable with three bits. In an embodiment, a bus protocol is defined so that each CPLD on the bus can be addressed individually or as a group.

[0596] Typical multiplexers may be limited to collecting only sensors from the same bank. This can be limiting, as there is great value in being able to simultaneously view data from sensors on the same machine for detailed analysis. Systems using traditional fixed-bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) assigned to specific groups during installation. Flexibility can only be achieved by overlapping channels or by incorporating a large amount of redundancy into the system, both of which come at a significant cost (and in some cases, the cost can increase exponentially with the flexibility). The simplest mux design selects one of many inputs and routes it to a single output line. A banked design consists of a group of these simple building blocks, each handling a fixed group of inputs and routing them to their own outputs. Inputs are typically non-overlapping, meaning that inputs from one mux group cannot be routed to another mux group. While traditional mux chips typically switch selected channels from a fixed group or bank to a single output (e.g., groups of 2, 4, 8, etc.), crosspoint muxes allow users to assign any input to any output. Traditionally, crosspoint multiplexers have been used for specialized applications such as RGB digital video and were too noisy for analog applications such as vibration analysis, but recent advances have made them feasible. Another advantage of crosspoint muxes is that their outputs can be disabled by putting them into a high-impedance state. This feature is ideal for output buses, where multiple mux cards can be stacked to combine output buses without the need for a bus switch.

[0597] In an embodiment, this problem can be addressed by using analog crosspoint switches to collect variable groups of vibration input channels and providing a matrix circuit that allows the system to access any set of eight channels from the total number of input sensors.

[0598] In some embodiments, multiple multiplexers can be controlled using a distributed CPLD chip with dedicated buses for logic control and data acquisition of multiple muxes. The section is enhanced with a hierarchical multiplexer, allowing multiple DAQs to collect data from multiple muxes. The hierarchical multiplexer can modularly output 16, 24, or more channels to multiple 8-channel card sets. This allows for faster data acquisition and the ability to simultaneously acquire more channels for more complex analysis. In some embodiments, the muxes can be made portable with minor configuration, and the data acquisition parking feature can be used to turn the SV3X DAQ into a protected system.

[0599] In embodiments, once the signal leaves the multiplexer and hierarchical Mux, it travels to the analog board where other enhancements occur. In embodiments, power-saving techniques such as: powering down analog channels when not in use, powering down component boards, powering down analog signal processing op-amps for unselected channels, and powering down channels on mother and daughter analog boards may be used. Because the DAQ system's low-level firmware can power down component boards and other hardware, sophisticated application control over power-saving features is relatively easy. Explicit control of the hardware is always possible, but not required by default. In embodiments, this power-saving benefit may be valuable in protected systems, especially those that are battery- or solar-powered.

[0600] In an embodiment, to maximize the signal-to-noise ratio and provide the best possible data, the autoscaling peak detector routes the highest peak in each data set to a separate A / D converter, allowing the system to quickly scale the data to that peak. For vibration analysis, the A / D converters built into many microprocessors are often insufficient in terms of bit count, channel count, and sampling frequency, making it necessary to avoid significantly slowing down the microprocessor. Despite these limitations, using them for autoscaling purposes is useful. In an embodiment, a separate, less powerful, and less expensive A / D converter may be used. For each input channel, after the signal is buffered (usually with appropriate AC or DC coupling) but before signal conditioning, it is fed directly to a microprocessor or a low-cost A / D converter. Unlike the conditioned signal, where range, gain, and filter switches are engaged, these switches are not changed. This allows the autoscaling data to be sampled simultaneously while the input data is processed and fed to a more robust external A / D converter, and then routed to onboard memory using Direct Memory Access (DMA) without CPU intervention. This significantly simplifies the autoscaling process by eliminating the need for settling time after throwing a switch, which significantly slows down the autoscaling process. Furthermore, data can be collected simultaneously, ensuring the highest signal-to-noise ratio. A reduction in the number of bits is typically more than sufficient for autoscaling purposes. In embodiments, improved integration using both analog and digital methods creates an innovative hybrid integration that also improves or maintains the highest possible signal-to-noise ratio.

[0601] In embodiments, sections of the analog board can route raw or buffered trigger channels to other analog channels. This may allow users to route triggers to either channel for analysis or troubleshooting. Systems can have trigger channels for purposes such as determining the relative phase between various input data sets or acquiring important data without repeating unnecessary inputs. In embodiments, digitally controlled relays can be used to switch raw or buffered trigger signals to either of the input channels. It may be desirable to verify the quality of the trigger pulse, as it may be corrupted for a variety of reasons, including improper placement of the trigger sensor, wiring issues, or dirty reflective tape when using optical sensors. Being able to view either the raw or buffered signal allows for better diagnostics and debugging. Recorded data signals can also be used with various signal processing techniques, such as variable-speed filtering algorithms, to improve phase analysis capabilities.

[0602] In embodiments, once the signal leaves the analog board, it passes to the delta-sigma board, where a precise voltage reference for the A / D zero reference provides more accurate DC sensor data. The high speed of delta-sigma allows the delta-sigma A / D to have a higher input oversampling setting and output at a lower sampling rate to minimize the need for anti-aliasing filters. For higher sampling rates, a lower oversampling rate can be used. For example, a third-order AA filter set to the lowest sampling condition of 256 Hz (Fmax 100 Hz) is appropriate for Fmax ranges of 200 Hz and 500 Hz. For Fmax above 1 kHz, a higher cutoff AA filter can be used (using a second-order filter 2.56 times the highest sampling rate of 128 kHz). In embodiments, a CPLD can be used as a clock divider for the delta-sigma A / D to achieve lower sampling rates without the need for digital resampling. In embodiments, a CPLD can be used as a programmable clock divider to divide a high-frequency crystal reference to a lower frequency. The precision of the divided low frequencies is greater relative to the longer time period of the original source, and the need for resampling by the delta-sigma A / D is minimized.

[0603] The data then travels from the Delta Sigma board to the Jennie board, where an onboard timer is used to digitally derive the phase relative to the input and trigger channels. The Jennie board also has the ability to store calibration data and historical system maintenance data on a set of cards. The Jennie board also has the ability to capture long blocks of data at a high sampling rate, rather than multiple data acquisitions at different sampling rates, allowing for streaming data or capturing long blocks of data for advanced analysis in the future.

[0604] In embodiments, the signal can be transmitted to a computer after passing through the Jennie board. In embodiments, computer software is used to add intelligence to the system, starting with an expert system GUI. This GUI is a graphical expert system with a simple user interface for defining smart bands and diagnostics, allowing anyone to easily perform complex analyses. In embodiments, this user interface revolves around the smart band, a simple approach for general users to perform complex yet flexible analyses. In embodiments, the smart band may be combined with a self-learning neural network for a more advanced analytical approach. In embodiments, the system may use machine hierarchy for further analytical insights. One key element of predictive maintenance is the ability to learn from known information during repairs and inspections. In embodiments, a graphical approach for back-calculation can improve smart bands and correlations based on known failures and problems.

[0605] In embodiments, there is a smart route to adapt which sensors are collected simultaneously to gain additional correlative intelligence. In embodiments, a smart operational data store (“ODS”) allows the system to select data collection for operational deflection shape analysis to further examine the condition of the machine. In embodiments, adaptive scheduling techniques allow the system to modify the data scheduled to be collected for full spectrum analysis across multiple (e.g., eight) correlated channels. In embodiments, the system can provide data enabling expanded statistical capabilities for continuous monitoring, as well as ambient local vibration, for combined analysis of changes in ambient and local temperature and vibration levels to identify machine issues.

[0606] In embodiments, the data acquisition device may be controlled by a personal computer (PC) to implement desired data acquisition commands. In embodiments, the DAQ box may be self-sufficient, capable of acquisition, processing, analysis, and monitoring independent of external PC control. Embodiments may include Secure Digital (SD) card storage. In embodiments, utilizing an SD card can provide significant additional storage capabilities. This may prove important for monitoring applications where important data may be permanently stored. Also, in the event of a power outage, the most recent data may be preserved even if it has not been offloaded to another system.

[0607] The current trend is to make DAQ systems as communicative as possible with the outside world, usually through a network, including wireless. In the past, it was common to use a dedicated bus to control the DAQ system with either a microprocessor or a microcontroller / microprocessor paired with a PC. In some embodiments, a DAQ system may consist of one or more microprocessors / microcontrollers, specialized microcontrollers / microprocessors, or dedicated processors focused primarily on the communication aspects with the outside world. Examples include USB, Ethernet, wireless, and the ability to provide an IP address for hosting a web page. All communication with the outside world is accomplished through simple text-based menus. Typical commands (over 100 in fact) are available, such as InitializeCard, AcquireData, StopAcquisition, and RetrieveCalibration Info.

[0608] In embodiments, intensive signal processing activities, including resampling, weighting, filtering, and spectral processing, may be performed by a dedicated processor, such as a field programmable gate array ("FPGA"), digital signal processor ("DSP"), microprocessor, microcontroller, or combinations thereof. In embodiments, this subsystem may communicate with a communications processor via a dedicated hardware bus, facilitated by dual-port memory, semaphore logic, and the like. This embodiment significantly improves efficiency and processing capabilities, including data streaming and other high-end analysis techniques. This eliminates the need to constantly interrupt key processes, such as controlling signal conditioning circuits, triggering, acquiring raw data through the A / D, directing the A / D output to the appropriate onboard memory, and processing the data.

[0609] Embodiments may include sensor overload identification. A need exists for a monitoring system that identifies when a sensor is overloaded. There may be situations involving high frequency inputs that saturate the standard 100mv / g sensors most commonly used in the industry, and having the ability to sense an overload improves data quality for better analysis. While a monitoring system can identify when a system is overloaded, in embodiments, the system looks at the sensor voltage to determine if the overload is due to the sensor, allowing the user to obtain another sensor that is more appropriate for the situation or collect the data again.

[0610] In embodiments, the sensors may be equipped with RFID (Radio Frequency Identification), inclinometers, or accelerometers to indicate which machine or bearing the sensor is attached to and in what orientation, allowing the software to automatically store the data without user input. In embodiments, a user can simply attach the system to any machine, and the system will automatically configure and be ready to collect data within seconds.

[0611] Embodiments may include ultrasonic online monitoring by placing ultrasonic sensors inside transformers, motor control centers, breakers, etc., and continuously monitoring the sound spectrum for patterns that identify electrical issues such as arcing, corona, etc., that indicate faults or problems. Embodiments may include providing continuous ultrasonic monitoring of rotating elements and bearings of energy production equipment. In embodiments, an analytical engine may combine ultrasonic data with other parameters such as vibration, temperature, pressure, heat flux, magnetic field, electric field, current, voltage, capacitance, inductance, and combinations thereof (e.g., simple ratios) for use in identifying faults beyond ultrasonic online monitoring.

[0612] Embodiments of the methods and systems disclosed herein can include the use of analog crosspoint switches to collect variable groups of vibration input channels. For vibration analysis, it is useful to simultaneously acquire multiple channels from vibration transducers mounted in multiple orientations on different parts of a machine. Simultaneous acquisition allows, for example, comparing the relative phase of the inputs to diagnose various machine faults. Other types of cross-channel analysis can also be performed, such as cross-correlation, transfer function, and operating deflection shape (ODS).

[0613] Embodiments of the methods and systems disclosed herein may include a precision voltage reference for the A / D zero reference. Some A / D chips provide their own internal zero voltage reference, which is used as a midscale value for external signal conditioning circuitry to ensure that both the A / D and the external op amp use the same reference. While this sounds reasonable in principle, practical complications arise. Often, these references are inherently based on the supply voltage using a resistor divider. In many current systems, especially those powered by a PC via a bus such as USB, the supply voltage often varies significantly with load, resulting in an unreliable reference. This is particularly true for delta-sigma A / D chips that require signal processing. While offsets vary with load, problems arise when digitally calibrating readings. To compensate for DC drift, it is common to digitally correct the voltage offset, expressed in counts, output by the A / D. However, in this case, even if an appropriate calibration offset is determined for one load condition, it will not apply to other conditions. This makes the absolute DC offset, expressed in counts, inapplicable. This results in the need to calibrate for all load conditions, which becomes complex, unreliable, and ultimately unmanageable. In an embodiment, an external voltage reference that is simply independent of the power supply voltage is used for use as the zero offset.

[0614] In an embodiment, the system provides a phase-locked loop bandpass tracking filter method for obtaining low-speed RPM and phase for balancing purposes, and provides additional analysis from that data, for remote balancing of low-speed machinery, such as paper mills. Balancing may require balancing at very slow speeds. A typical tracking filter may be built based on a phase-locked loop or PLL design, where stability and speed range are paramount. In an embodiment, a number of digitally controlled switches are used to select appropriate RC and damping constants. This can all be done automatically after measuring the frequency of the incoming tach signal. Method and system embodiments disclosed herein may include digital derivation of phase for input and trigger channels using onboard timers. In an embodiment, the digital phase derivation uses a digital timer to determine the precise delay from the trigger event to the exact start of data acquisition. This delay or offset is applied to the analytically determined phase of the acquired data, with interpolation used to obtain a more precise offset. This phase results in an absolute phase with precise mechanical meaning, useful for one-shot balancing, alignment analysis, and more.

[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 a high sampling rate, as opposed to multiple data sets acquired at different sampling rates. Generally, modem route collection for vibration analysis involves collecting data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary at different points along the route, depending on the needs of the mechanical analysis. For example, a motor may require high resolution at a relatively low sampling rate to distinguish between harmonics of the running speed and harmonics of the line frequency. However, the practical tradeoff is that more collection time is required to achieve this increased resolution. In contrast, high-speed compressors and gearsets may require less precise resolution, but may require a much higher sampling rate to measure the amplitude of relatively high-frequency data. Ideally, however, data with a very long sample length would be collected at a very high sampling rate. When digital acquisition devices first became widespread in the early 1980s, A / D sampling, digital storage, and computing power were not at current levels, so compromises were made between data acquisition time and the required resolution and accuracy. These limitations led some field analysts to stick with analog tape recording systems, which lacked the drawbacks of digitalization. Some hybrid systems employed digitizing and playing back recorded analog data at multiple desired sampling rates and lengths, but these systems were not automated. A more common approach, as previously mentioned, balances data acquisition time and analysis capabilities by digitally acquiring blocks of data at multiple sampling rates and lengths and then digitally storing these blocks separately. In this embodiment, the highest practical sampling rate (e.g., 102.4 kHz) was used.Long data lengths can be collected and stored at a high sampling rate (corresponding to an Fmax of 40 kHz). These long data blocks can be acquired in the same time as the shorter lengths at the lower sampling rates utilized in a priori methods, so there is no effective delay added to sampling at measurement points, which is always a concern in route collection. In embodiments, analog tape recording of data is digitally simulated with such accuracy that it can be considered substantially continuous or "analog" for many purposes, including for purposes of embodiments of the present disclosure, unless the context dictates otherwise.

[0616] Embodiments of the methods and systems disclosed herein can include storing calibration data and maintenance history for the onboard card set. Many data acquisition devices that rely on interfacing with a PC to function store their calibration coefficients on the PC. This is especially true for complex data acquisition devices with many signal paths and therefore potentially large calibration tables. In embodiments, the calibration coefficients can be stored in flash memory, allowing this and other data to be stored. This critical information is therefore permanently stored for all practical purposes. This information includes nameplate information such as serial numbers for individual components, firmware or software version numbers, maintenance history, and calibration tables. In embodiments, the DAQ box remains calibrated and retains all of this critical information, regardless of which computer the box is ultimately connected to. PCs and external devices can poll this information at any time for portability or information exchange purposes.

[0617] Method and system embodiments disclosed herein can include rapid route creation using hierarchical templates. Similar to general parametric monitoring, vibration monitoring requires the existence of data monitoring points to be established in a database or equivalent. These points have various associated attributes, including transducer attributes, data acquisition settings, machine parameters, and operational parameters. Transducer attributes include probe type, probe mounting type, and probe mounting direction or axis orientation. Measurement-related data acquisition attributes include sampling rate, data length, power and coupling requirements for integrated electronic piezoelectric probes, hardware integration requirements, 4-20 or voltage interface, range and gain settings (if applicable), and filter requirements. Machine parameter requirements associated with a particular point include items such as operating speed, bearing type, and bearing parameter data (e.g., pitch diameter, number of balls, inner race, and outer race diameter for rolling element bearings). For tilting pad bearings, this includes the number of pads. For measurement points on equipment such as gearboxes, required parameters may include, for example, the number of teeth on each gear. For induction motors, this includes the number of rotor bars or poles; for compressors, the number of blades or vanes; and for fans, the number of blades. For belt / pulley systems, the pulley dimensions and center-to-center distance allow for the calculation of the number of belts and the associated belt-pass frequency. Measurements near the coupling require the type of coupling and the number of teeth for geared couplings. Operating parametric data includes the operating load in megawatts, flow rate (air or fluid), rate, horsepower, and feet per minute. Other relevant parameters include ambient and operating temperatures, pressure, and humidity. Thus, the amount of configuration information required for each measurement point is significant. This information is also critical for valid data analysis. Machine-, equipment-, and bearing-specific information is essential for identifying failure frequencies and predicting the various types of specific failures that may be expected.Transducer attributes and data acquisition parameters are essential for properly interpreting the data and for defining the types of analysis techniques appropriate. Traditional methods for entering this data are manual and tedious. Typically, data is entered at the lowest hierarchical level (e.g., bearing level for machine parameters) and at the transducer level for data acquisition configuration information. However, the importance of the hierarchical relationships required to organize data, not only for data storage and transfer but also for analysis and interpretation, cannot be overemphasized. This section focuses on data storage and transfer. While the aforementioned configuration information is, by its nature, highly redundant at the lowest hierarchical level, its strong hierarchical nature allows it to be stored quite efficiently in this format. In embodiments, the hierarchy can be exploited when copying data in the form of templates. As an example, a hierarchical storage structure suitable for many purposes, from general to specific, is defined, including company, plant, site, unit, process, machine, equipment, shaft element, bearing, and transducer. Copying data related to a specific machine, equipment, shaft element, or bearing is much easier than copying only at the lowest transducer level. In embodiments, the system not only stores data in this hierarchical manner, but also provides robust support for rapid copying of data using these hierarchical templates. The similarity of elements at certain hierarchical levels lends itself to effective data storage in a hierarchical format. For example, many machines share common elements such as motors, gearboxes, compressors, belts, and fans. Specifically, many motors can be easily categorized as induction machines, DC machines, fixed speed, or variable speed. Many transmissions fall into commonly occurring groups such as input / output, input pinion / intermediate pinion / output pinion, and four-post. Within factories and companies, many similar types of equipment are purchased and standardized for cost and maintenance reasons. This results in significant overlap between similar types of equipment, creating a prime opportunity to utilize a hierarchical template approach.

[0618] Embodiments of the methods and systems disclosed herein can include a smart band. A smart band refers to processed signal characteristics obtained from any dynamic input or group of inputs for the purpose of analyzing the data to achieve a correct diagnosis. Furthermore, a smart band can even include mini or relatively simple diagnostics for the purpose of achieving more robust and complex diagnostics. Historically, in the field of mechanical vibration analysis, alarm bands have been used to define spectral frequency bands of interest for the purpose of analyzing and / or trending important vibration patterns. Alarm bands consist of the spectral (amplitude plotted against frequency) region between low- and high-frequency boundaries. The amplitudes between these boundaries are summed in the same manner to calculate the overall amplitude. Smart bands are more flexible in that they can refer not only to specific frequency bands but also to groups of spectral peaks such as single-peak harmonics, true peak levels or crest factors obtained from time waveforms, wholes obtained from vibration envelope spectra or other specialized signal analysis techniques, or logical combinations (e.g., AND, OR, XOR) of these signal attributes. Additionally, countless other parametric data, including system load, motor voltage and phase information, bearing temperatures, flow rates, etc., can similarly be used as the basis for forming additional smart bands. In embodiments, smart band symptoms can be used as components of an expert system that utilizes these inputs to perform diagnoses. Some of these mini-diagnoses can be used as smart band symptoms (smart bands can also include diagnoses) for more general diagnoses.

[0619] Embodiments of the methods and systems disclosed herein can include a neural net expert system using a smart band. Typical vibration analysis engines are rule-based (i.e., they use a list of expert rules that, when satisfied, trigger a specific diagnosis). In contrast, neural approaches weight multiple input stimuli into small analysis engines (neurons), which then provide simplified, weighted outputs to other neurons. The outputs of these neurons are classified as smart bands and provided to other neurons. This creates a more layered approach to expert diagnosis, as opposed to the one-shot approach of rule-based systems. In this embodiment, the expert system utilizes this neural approach with the smart band, but nothing prevents rule-based diagnoses from being reclassified as smart bands to serve as additional stimuli for the expert system. From this perspective, it can be viewed as a hybrid approach, although at the highest level it is essentially neural.

[0620] Embodiments of the methods and systems disclosed herein can include the use of a database hierarchy in which analytical smart band symptoms and diagnoses may be assigned to various hierarchical database levels. For example, a smart band can call "Looseness" at the bearing level, trigger "Looseness" at the equipment level, and trigger "Looseness" at the machine level. Another example could generate a smart band diagnosis of "Horizontal Plane Phase Reversal" across the coupling, and a smart band diagnosis of "Vertical Coupling Misalignment" at the machine level.

[0621] Embodiments of the methods and systems disclosed herein may include an expert system GUI. In embodiments, the system employs a graphical approach to defining smart bands and diagnostics for the expert system. Entering symptoms, rules, or more generally smart bands to create specific machine diagnoses can be tedious and time-consuming. Providing a graphical means of wiring can make the process easier and more efficient. The proposed graphical interface consists of four major components: a symptom parts bin, a diagnostics bin, a tool bin, and a graphical wiring area ("GW A"). In embodiments, the symptom parts bin contains various spectra, waveforms, envelopes, and any type of signal processing characteristic or group of characteristics, such as spectral peaks, spectral harmonics, waveform true peaks, waveform crest factors, and spectral alarm bands. Additional characteristics can be assigned to each part. For example, parts of a spectral peak may be assigned orders of frequency or road speed. Some parts are predefined or user defined, such as lx, 2x, 3x running speed, lx, 2x, 3x gear mesh, lx, 2x, 3x blade path, number of motor rotor bars x running speed.

[0622] In embodiments, diagnostic bins contain a variety of predefined and user-defined diagnoses, such as misalignment, unbalance, looseness, and bearing faults. Like parts, diagnoses can also be used as building blocks to build more complex diagnoses. In embodiments, tool bins contain other methods of combining the various parts listed above, such as logical operations like AND, OR, and XOR, and statistical operations like Find Max, Find Min, Interpolate, Average, and other statistical operations. In embodiments, the graphical wiring area contains parts from part bins or diagnoses from diagnostic bins, which may be combined using tools to create a diagnosis. The various parts, tools, and diagnoses are represented by icons that are simply graphically wired in a 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 an opportunity for the system to learn. If a specific set of stimuli or smart band is already known to correspond to a particular fault or diagnosis, it is possible to back-calculate a set of coefficients that, when applied to a future set of similar stimuli, will arrive at the same diagnosis. In embodiments, a best-fit approach can be used when there are multiple data sets. Unlike the smart band GUI, in this embodiment, the wiring diagram is self-generated. In embodiments, the user may adjust settings in the back-propagation approach and use a database browser to match a particular data set with the desired diagnosis. In embodiments, the desired diagnosis may be created or custom-tuned in the smart band GUI. In embodiments, the user may then press a "GENERATE" button, which may display a dynamic wiring from symptoms to diagnoses as it works through the algorithm to find the best fit. In embodiments, upon completion, various statistics are presented detailing how well the mapping process progressed. In some cases, mapping may not be achieved, for example, if the input data is all zeros or incorrect (misassigned) data. Embodiments of the methods and systems disclosed herein may include bearing analysis methods, which may be used in combination with computer-aided design ("CAD"), predictive deconvolution, minimum variance distortion-free response ("MVDR"), and spectral sum of harmonics.

[0624] In recent years, there has been a strong demand for power conservation, resulting in an influx of variable frequency drives and variable speed machines. In embodiments, a bearing analysis method is provided. In embodiments, torsional vibration detection and analysis using transient signal analysis is provided to provide advanced torsional vibration analysis for a more comprehensive approach to diagnosing torsional force-related machines (e.g., machines with rotating parts). Due to the declining cost of motor speed control systems and increased awareness of energy consumption, exploiting the significant energy-saving potential of load control has become economically justified. However, an often overlooked aspect of this problem is the issue of vibration. When a machine is designed to operate at only one speed, it is much easier to design the physical structure to avoid structural and torsional mechanical resonances, each of which dramatically reduces the machine's health. This includes structural characteristics such as the type of material used, its weight, stiffening requirements and placement, bearing type, bearing location, and base support constraints. Even for machines that operate at one speed, designing the structure to minimize vibration is a challenging task requiring computer modeling, finite element analysis, field testing, and more. Add variable speeds to the mix, and it is often impossible to design for all desired speeds. The challenge then becomes minimizing or avoiding speeds. This is why many modern motor controllers are programmed to skip or quickly pass through certain speed ranges or bands. Embodiments can include identifying speed ranges in a vibration monitoring system. Non-torsional structural resonances are fairly easy to detect using traditional vibration analysis techniques. This is not the case for torsional resonances. Currently, particular attention is being paid to the increasing problem of torsional resonances. This is evident not only due to the operation of equipment at torsional resonance speeds, but also due to increased torsional stresses caused by speed changes. Unlike non-torsional structural resonances, which typically manifest themselves as a dramatic increase in casing and external vibrations, torsional resonances typically do not exhibit such effects.In the case of shaft torsional resonance, the torsional motion caused by the resonance can only be identified by looking at changes in speed and / or phase. Current standard methods for analyzing torsional vibration require the use of specialized equipment. The methods and systems presented herein enable the analysis of torsional vibration without the use of such specialized equipment. This can be achieved by shutting down the machine and using specialized fixtures such as strain gauges or speed encoder plates and gears. Friction wheels are one example, but this typically requires manual implementation and specialized analysts. These techniques are generally very expensive and inconvenient. Due to reduced costs and increased convenience (e.g., remote access), continuous vibration monitoring systems are becoming more common. In embodiments, there is the ability to identify torsional speed and / or phase fluctuations from the vibration signal alone. In embodiments, transient analysis techniques can be used to distinguish torsional-induced vibration from simple speed changes due to process control. In embodiments, factors for identification may focus on one or more of the following aspects: The rate of speed change due to variable speed motor control is relatively slow, sustained, and intentional. Torsional speed changes tend to be short, impulsive and not sustained. Additionally, the small speed changes associated with torsion compared to the rotational speed of the shaft suggests that monitoring phase behavior will show quick or transient bursts of speed, as opposed to the slower phase changes (typical of Bode and Nyquist plots) historically associated with speeding up and slowing down machines.

[0625] Embodiments of the methods and systems disclosed herein can include improved integration using both analog and digital methods. When signals are digitally integrated using software, the spectral low-frequency data is essentially multiplied by a function that expands as its amplitude approaches zero, creating what is known in the industry as a "ski slope" effect. This ski slope amplitude essentially becomes the noise floor of the instrument. A simple solution to this requires a traditional hardware integrator. Also, the amplification factor can be kept to a reasonable level, essentially prohibiting multiplication by very large numbers. However, at higher frequencies, the original amplitude, well above the noise floor, is multiplied by a very small number (1 / f) that is well below the noise floor. Hardware integrators have a fixed noise floor, and although it is low, it does not scale down with low-amplitude high-frequency data. In contrast, if a digitized high-frequency signal is digitally multiplied in the same way, the noise floor also decreases proportionally. In embodiments, for ideal results, hardware integration may be used below the unity gain point (typically at a value determined by gain-based units and / or the desired signal-to-noise ratio) and software integration may be used above unity gain. In embodiments, this integration is performed in the frequency domain. In embodiments, the resulting hybrid data can be converted back into a waveform that should have a much better signal-to-noise ratio when compared to either the hardware-integrated or software-integrated data. In embodiments, the strengths of hardware integration are combined with the strengths of digital-software integration to achieve the highest signal-to-noise ratio. In embodiments, the high-pass filter and curve fitting of the first-order staged hardware integrator can pass relatively low-frequency data while reducing or eliminating noise, salvaging highly useful analytical data that would otherwise be lost with a steep filter.

[0626] Embodiments of the methods and systems disclosed herein can include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is often performed using an upfront mux, which selects a few channels of data from a large number of data sources to feed the hardware signal processing, A / D, and processing components of a DAQ system. This is primarily driven by practical cost considerations. The tradeoff is that not all points are monitored continuously (although they may be monitored to some extent via alternative hardware methods). Embodiments provide multiple scheduling levels. In embodiments, at the lowest, mostly continuous level, all measurement points are cycled through in a round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 measurement points, each measurement point may be serviced once every 15 minutes, but if a measurement point should alarm due to user-selected criteria, that measurement point may be prioritized and serviced more frequently. Just as there are multiple grades of severity for each alarm, there are also multiple priorities for monitoring. In embodiments, more serious alarms are monitored more frequently. In embodiments, many additional high-level signal processing techniques may be applied less frequently. In embodiments, the increased processing power of the PC can be utilized, allowing the PC to temporarily suspend the round-robin path collection process (with its multiple layer collections) and stream the required amount of data for a selected point. In embodiments, various advanced processing techniques such as envelope processing, wavelet analysis, and many other signal processing techniques can be included. In embodiments, after acquiring this data, the DAQ card set continues its route at the interrupted point. In embodiments, the scheduled data acquisition of the various PCs follows its own schedule, which occurs less frequently than the DAQ card routes.They may be set hourly, daily, or per root cycle (e.g., once every 10 cycles), and may also increase on a scheduled basis based on alarm severity priority or measurement type (e.g., motors may be monitored differently from fans).

[0627] Embodiments of the methods and systems disclosed herein may include a data acquisition parking function. In embodiments, a data collection box used for path collection, real-time analysis, and generally as an acquisition device may be detached from its PC (e.g., a tablet) and powered by an external power source or a suitable battery. In embodiments, the data collection device retains continuous monitoring capabilities, and its on-board firmware may implement dedicated monitoring functions for extended periods of time or be remotely controlled for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical functions for continuous monitoring.

[0628] Embodiments of the methods and systems disclosed herein may include ambient sensing + local sensing + vibration for analysis. In embodiments, ambient environmental temperature and pressure, sensed temperature and pressure, may be combined with long-term / medium-term vibration analysis to predict any set of conditions or characteristics. In variations, infrared sensing, infrared thermography, ultrasound, and many other types of sensors and input types may be added in combination with vibration or with each other. Embodiments of the methods and systems disclosed herein may include smart routes. In embodiments, the software of the continuous monitoring system will adapt / adjust the data collection order based on statistics, analytics, data alarms, and dynamic analysis. Typically, routes are set based on the channel to which the sensor is attached. In embodiments, a crosspoint switch allows the Mux to couple any input Mux channel to (e.g., eight) output channels. In embodiments, when a channel goes into alarm or the system identifies a significant deviation, the normal route set in the software will be suspended in order to collect specific concurrent data from channels that share significant statistical changes for more advanced analysis. Embodiments include implementing a smart ODS or smart transfer function.

[0629] Embodiments of the methods and systems disclosed herein can include a smart ODS and one or more transfer functions. In embodiments, the system's multiplexer and crosspoint switch can perform ODS, transfer function, or other specialized tests on all vibration sensors attached to a machine / structure to accurately show how machine points are moving in relation to one another. In embodiments, data can be streamed at 40-50 kHz for longer data lengths (e.g., at least one minute), revealing information different from that shown by typical ODS or transfer functions. In embodiments, the system can determine a smart route function based on the data / statistics / analysis used to deviate from the standard route and implement ODS on a machine, structure, or across multiple machines and structures where conditions / data may show correlations to indicate. In embodiments, a transfer function can use an impact hammer on one channel and compare it to other vibration sensors on the machine. In embodiments, the system can implement a transfer function using changes in conditions such as load, speed, temperature, or other changes in the machine or system. In embodiments, different transfer functions may be compared to each other over time. In embodiments, different transfer functions may be stitched together like a movie that may show how a machine failure changes, such as a bearing that may show how a bearing goes through four stages of failure. Embodiments of the methods and systems disclosed herein may include hierarchical Muxes.

[0630] Referring to FIG. 8 , the present disclosure generally involves digitally collecting or streaming waveform data 2010 from a machine 2020 whose operating speeds can vary from relatively slow rotational or vibration speeds to much higher speeds under different circumstances. The waveform data 2010 can include, in at least one machine, data from a single-axis sensor 2030 mounted at a fixed reference location 2040 and data from a three-axis sensor 2050 mounted at varying locations (or at multiple locations), including location 2052. In an embodiment, the waveform data 2010 can be vibration data acquired simultaneously in a gap-free format from each sensor 2030, 2050 over a duration of multiple minutes at a maximum resolvable frequency large enough to capture periodic and transient impact events. By this example, the waveform data 2010 can include vibration data that can be used to create operational deflection shapes. Vibrations can also be diagnosed and machine repair solutions can be prescribed therefrom, if desired.

[0631] In an embodiment, the machine 2020 may further include a housing 2100 that may include a drive motor 2110 that may drive a shaft 2120. The shaft 2120 may 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 may be connected to (or resident on) the machine 2020. In one example, the data collection module 2160 is located and accessible via a cloud network facility 2170 and may collect waveform data 2010 from the machine 2020 and distribute the waveform data 2010 to a remote location. The working end 2180 of the drive shaft 2120 of the machine 2020 may drive a windmill, fan, pump, drill, gear system, drive system, or other operating element, such that the techniques described herein are applicable to a wide range of machines, devices, tools, and the like, including rotating or vibrating elements. In another example, a generator can be used in place of the motor 2110, and the working end of the drive shaft 2120 can generate electrical power for the generator rather than consuming rotational energy.

[0632] In an embodiment, the waveform data 2010 can be acquired using a predetermined path format based on the layout of the machine 2020. The waveform data 2010 can include data from a single-axis sensor 2030 and a three-axis sensor 2050. The single-axis sensor 2030 can function as a reference probe with one channel of data and can be fixed at a fixed position 2040 on the machine being surveyed. The three-axis sensor 2050 can function as a three-axis 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. In one example, both sensors 2030, 2050 can be manually attached to the machine 2020 or, in certain service examples, can be connected to a separate portable computer. The reference probe can remain in one location while a user can move the three-axis vibration probe along a predetermined route, such as from bearing to bearing on the machine. In this example, the user is instructed to place sensors at predetermined locations to complete a survey (or portion thereof) of the machine.

[0633] Referring to FIG. 9, there is shown a portion of an exemplary machine 2200 having a three-axis sensor 2210 mounted at a location 2220 associated with a motor bearing of the machine 2200 having an output shaft 2230 and an output member 2240 in accordance with the present disclosure.

[0634] In further examples, the sensor and data acquisition modules and devices can be integrated into or resident on the rotating machinery. By these examples, the machine can include many single-axis sensors and many three-axis sensors at predetermined locations. The sensors can be original equipment installed and provided by the original equipment manufacturer, or installed at a different time in a retrofit application. A data collection module 2160 or the like can select and use one single-axis sensor to acquire data exclusively from during collection of waveform data 2010 while moving to each of the three-axis sensors. The data collection module 2160 can be resident on the machine 2020 and / or connected via a cloud network facility 2170.

[0635] Referring to FIG. 8 , various embodiments include collecting waveform data 2010 by digitally recording locally or streaming via cloud network facility 2170. The waveform data 2010 can be collected in an uninterrupted, gap-free manner and in some respects can be similar to analog recording of waveform data. Waveform data 2010 from all channels can be collected for between one and two minutes, depending on the rotational or vibration speed of the machine being monitored. In embodiments, the data sampling rate can be relatively high relative to the operating frequency of the machine 2020.

[0636] In an embodiment, a second reference sensor can be used to collect data for a fifth channel. In this manner, a single-axis sensor can be the first channel, and triaxial vibration can occupy the second, third, and fourth data channels. This second reference sensor, like the first reference sensor, can be a single-axis sensor, such as an acceleration sensor. In an embodiment, the second reference sensor, like the first reference sensor, can remain in the same location on the machine throughout the vibration study of that machine. The location of the first reference sensor (i.e., the single-axis sensor) can be different from the location of the second reference sensor (i.e., another single-axis sensor). In a particular example, a second reference sensor can be used when a machine has two shafts with different operating speeds and two reference sensors are located on two different shafts. Following this example, additional single-axis reference sensors can be employed in additional, but different, permanent locations related to the rotating machine.

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

[0638] In embodiments, sampling, band selection, and filtering techniques can be used to undersample or oversample one or more portions of a long data stream (i.e., one to two minutes in duration) to achieve different effective sampling rates. To this end, interpolation and decimation can be used to further achieve different effective sampling rates. For example, oversampling can be applied to a frequency band near the rotational or vibration operating speed of the sampled machine, or its harmonics, because vibration effects tend to be more pronounced at these frequencies in the machine's operating range. In embodiments, the digitally sampled data set can be decimated to achieve a lower sampling rate. It will be understood in light of this disclosure that decimation in this context can be the opposite of interpolation. 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] As an example, a 100 Hz sampled waveform can be undersampled by undersampling every 10th point of the digital waveform to produce an effective sampling rate of 10 Hz, but the remaining 9 points of that portion of the waveform are effectively discarded and not included in the modeling of the sampled waveform. Furthermore, such naked undersampling can introduce ghost frequencies due to the high undersampling rate (i.e., 10 Hz) relative to the 100 Hz sampled waveform.

[0640] Most hardware for analog-to-digital conversion uses sample-and-hold circuits that can charge a capacitor for a predetermined period of time so that the average value of the waveform over a particular time period can be determined. In light of this disclosure, it will be appreciated that the value of a waveform over a particular time period is not linear, but more closely resembles a cardinal sinusoidal ("sine") function. Therefore, the exponential decay of a cardinal sinusoidal signal occurs from its center, indicating that more importance can be placed on the waveform data at the center of the sampling interval.

[0641] Using the above example, a waveform sampled at 100 Hz can be hardware sampled at 10 Hz, and therefore each sample point can be averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz can have each point averaged over 10 milliseconds). As opposed to effectively discarding 9 out of 10 data points of a sampled waveform, as described above, the present disclosure can include weighing adjacent data. Adjacent data can refer to the previously discarded sample point and the remaining point that is retained. In one example, a low-pass filter can linearly average adjacent sample data, i.e., determine the sum of every 10 points and then divide the sum by 10. In a further example, adjacent data can be weighted with a sinusoidal function. The process of weighting the original waveform with a sinusoidal function is referred to as an impulse function, or, in the time domain, as convolution.

[0642] The present disclosure is applicable not only to digitizing waveform signals based on detected voltages, but also to digitizing waveform signals based on image processing signals, including current waveforms, vibration waveforms, and rasterization of video signals. 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 itself can be shown to be insufficient. Thus, oversampling or upsampling itself can be shown to be insufficient as well, and, like decimation, interpolation can be used instead of undersampling itself.

[0643] It will be understood in light of this disclosure that interpolation in this context can refer to first applying a low-pass filter to digitally sampled waveform data and then upsampling the waveform data. It will be understood in light of this disclosure that real-world examples may often require using non-integer factors for decimation, interpolation, or both. Therefore, this disclosure includes sequential interpolation and decimation to achieve non-integer factor ratios for interpolation and decimation. In one example, sequential interpolation and decimation may 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, vibration data can be looped to intentionally emulate a traditional tape recorder loop, and digital filtering techniques can be used for efficient splicing to facilitate longer analysis. It will be understood in light of this disclosure that the above techniques do not preclude waveforms, spectra, and other types of analysis from being processed and displayed in a user GUI as they are collected. It will be appreciated in light of this disclosure that newer systems may allow this function to be performed in parallel with high performance acquisition of raw waveform data.

[0644] Regarding the issue of acquisition time, older systems use a compromised approach of improving data resolution by acquiring at different sampling rates and data lengths, which can actually reduce the expected time savings. This creates latency issues every time the data acquisition hardware is stopped and started, especially if the hardware is auto-scaling. Similarly, data retrieval for route information (test locations), which is often in database format, can be very time-consuming. Storing raw data in bursts to disk (solid-state or not) can also be undesirably slow.

[0645] In contrast, many embodiments, as disclosed herein, involve digitally streaming the waveform data 2010, further benefiting from the need to load path parameter information only once while configuring the data collection hardware. Because the waveform data 2010 is streamed to only one file, there is no need to open and close files or switch between read and write operations on a storage medium. Collecting and storing waveform data 2010 as described herein can be shown to produce relatively meaningful data in significantly less time than traditional batch data collection approaches. An example of this is an electric motor. For electric motors, waveform data can be collected at a data length of 4K points (i.e., 4096) with a sufficiently high resolution, especially to distinguish electrical sideband frequencies. For fans and blowers, a resolution as low as IK (1,024) can be used. In some cases, IK may be the minimum waveform data length requirement. The sampling rate is 1,280 Hz, which corresponds to an Fmax of 500 Hz. The time to acquire this waveform data is 1,024 points at 1,280 Hz, or 800 milliseconds, which satisfies the 2x oversampling required by the Nyquist criterion.

[0646] To improve accuracy, waveform data can be averaged. Eight averages can be used, for example, with 50% overlap. This extends the acquisition time from 800 ms to 3.6 seconds: 800 ms x 8 averages x 0.5 (overlap rate) + 0.5 x 800 ms (non-overlapping head and tail ends). After acquiring waveform data at Fmax = 500 Hz, a higher sampling rate can be used. In one example, 10 times the previous sampling rate (lOx) can be used, resulting in Fmax = 10 kHz. In this example, eight averages with 50% overlap can be used to acquire waveform data at this higher rate, resulting in an acquisition time of 360 ms, or 0.36 seconds. It will be appreciated that, in accordance with this disclosure, it may be necessary to read the hardware acquisition parameters for the higher sampling rate from the route list and perform hardware autoscaling, or reset other necessary hardware acquisition parameters, or both. This can add a few seconds of latency to accommodate the change in sampling rate. Another example is the latency to accommodate hardware autoscaling and changes to hardware acquisition parameters that are necessary when using the lower sampling rates disclosed herein. In addition to accommodating the sample rate changes, additional time is required to read route point information from the database (i.e., where to monitor, where to monitor next), display the route information, and process the waveform data. Furthermore, displaying the waveform data and / or associated spectra also consumes significant time. Therefore, approximately 15 to 20 seconds is required to acquire waveform data at each measurement point.

[0647] In further examples, additional sampling rates can be added, potentially extending the total time for a vibration survey due to the additional time required to switch from one sampling rate to another and to acquire additional data at different sampling rates. In some examples, lower sampling rates are used. For example, if Fmax = 50 Hz, a 128 Hz sampling rate is used. In this example, acquiring the initial average data at this sampling rate requires an additional 36 seconds, in addition to the other data previously described, further significantly increasing the total time at each measurement point. Furthermore, this embodiment allows for the use of gap-free digital streaming of waveform data as disclosed herein for wind turbines and other machines with relatively slow rotating or vibrating systems. In many examples, the collected waveform data can include long samples of data at relatively high sampling rates. In one example, the sampling rate can be 100 kHz, and the sampling time can be two minutes for all channels being recorded. In many examples, one channel can be for a single-axis reference sensor and three additional data channels can be for a three-axis, three-channel sensor. It will be appreciated in light of the present disclosure that longer data lengths can prove to facilitate the detection of extremely low-frequency phenomena. Long data lengths can also accommodate the inherent speed variations in wind turbine operation. Furthermore, it has been shown that long data lengths provide the opportunity to use multiple averages as described herein to achieve very high spectral resolution, making tape loops feasible for certain spectral analyses. Many advanced analysis techniques are now available because they can use the long, uninterrupted lengths of waveform data available in accordance with the present disclosure.

[0648] It will also be appreciated in light of the present disclosure that collecting waveform data from multiple channels simultaneously facilitates performing transfer functions between the multiple channels. Additionally, collecting waveform data from multiple channels simultaneously facilitates establishing phase relationships across the machine, allowing for more sophisticated correlations to be utilized by relying on the fact that waveforms from each channel are collected simultaneously. In another example, increasing the number of channels of data collection allows waveform data from multiple sensors to be acquired simultaneously, thereby reducing the time it takes to complete an entire vibration survey, rather than having to move from sensor to sensor during the vibration survey.

[0649] The present disclosure includes using at least one single-axis reference probe in one of the channels to enable relative phase comparison between channels. The reference probe can be an accelerometer or other type of transducer that does not move during a vibration study of a machine and is therefore fixed in a fixed position. Multiple reference probes can be deployed in suitable locations, each fixed in a predetermined position (i.e., in a fixed location) during vibration data acquisition during the vibration study. In a specific example, up to seven reference probes can be deployed depending on the capacity of the data acquisition module 2160. Transfer function or similar techniques can be used to compare the relative phase of all channels to each other at all selected frequencies. It can be shown that by keeping one or more reference probes fixed in a fixed position while moving or monitoring other three-axis vibration sensors, the entire machine can be mapped in terms of amplitude and relative phase. This is true even when the number of measurement points is greater than the number of data acquisition channels. This information can be used to create a dynamic deflection profile that can show the dynamic movement of the machine in 3D, making it an invaluable diagnostic tool. In embodiments, one or more reference probes may provide relative phase rather than absolute phase. In light of the present disclosure, it will be appreciated that while relative phase may be less valuable than absolute phase for some purposes, relative phase information can still prove very useful.

[0650] In embodiments, the sampling rate used during a vibration study can be digitally synchronized to a predetermined operating frequency that can be related to an appropriate parameter of the machine, such as rotation or vibration speed. This allows for even more information to be extracted using synchronous averaging techniques. It will be appreciated in light of the present disclosure that this can be done without the use of key phases or reference pulses from the rotating shaft. In this way, asynchronous signals can be removed from a complex signal without the need to deploy synchronous averaging using key phases. This can prove very useful when analyzing a specific pinion in a gearbox or when applied to every component within a complex mechanical mechanism. Often, key phases or reference pulses are rarely available in route-collected data, but the techniques disclosed herein can overcome this absence. In embodiments, there can be multiple shafts operating at different speeds within the machine being analyzed. In some embodiments, there can be a single-axis reference probe on each axis. In other examples, 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 constant position. In embodiments, variable-speed equipment can be more easily analyzed with relatively long data durations compared to single-speed equipment. Vibration studies can be performed at several machine speeds within the same continuous set of vibration data using the same techniques disclosed herein, and these techniques allow for the investigation of changes in the relationship between vibration and speed changes that were previously not available.

[0651] In embodiments, because raw waveform data can be captured in a gap-free digital format as disclosed herein, numerous analysis techniques arise from it. The gap-free digital format can facilitate numerous paths for post-hoc analysis of the waveform data in numerous ways to identify specific problems. Vibration data collected in accordance with the techniques disclosed herein can provide analysis of transient, semi-periodic, and infrasonic phenomena. Waveform data acquired in accordance with the present disclosure can include a relatively long stream of raw gap-free waveform data that can be conveniently replayed as needed, upon which many different sophisticated analysis techniques can be performed. Many such techniques can provide various forms of filtering to extract low-amplitude modulations from the transient impulse data that can be contained in the relatively long stream of raw gap-free waveform data. It will be appreciated in light of the present disclosure that in past data collection methods, these types of phenomena were typically lost through the averaging process of spectral processing algorithms because the objective of previous data collection modules was purely periodic signals, or through file size reduction methods because much of the content from the original raw signal was discarded knowing that it would not typically be used.

[0652] In one embodiment, there is a method for monitoring vibrations in a machine having at least one shaft supported by a set of bearings. The method includes monitoring a first data channel assigned to a single-axis sensor at a fixed location relative to the machine. The method also includes monitoring second, third, and fourth data channels assigned to a three-axis sensor. The method further includes simultaneously recording gap-free digital waveform data from all data channels during operation of the machine and determining relative phase changes based on the digital waveform data. The method also includes disposing a three-axis sensor at multiple locations relative to the machine while acquiring the digital waveforms. In one embodiment, the second, third, and fourth channels are jointly assigned to a set of three-axis sensors each positioned at a different location relative to the machine. In one embodiment, data is received simultaneously on all channels from all sensors.

[0653] The method also includes determining an operational deflection shape based on the change in relative phase information and the waveform data. In an embodiment, the invariant position of the reference sensor is a position relative to the shaft of the machine. In an embodiment, each of the three-axis sensors in the sequence of three-axis sensors is located at a different location and each is associated with a different bearing of the machine. In an embodiment, the invariant position is a position relative to the shaft of the machine, where each of the three-axis sensors in the sequence of three-axis sensors is located at a different location and each is associated with a different bearing supporting the shaft within the machine. Various embodiments include a method for simultaneously sequentially monitoring vibration or similar process parameters and signals of a rotating or vibrating machine or similar process machine from multiple channels, which may be known as an ensemble. In various examples, an ensemble may include one to eight channels. In a further example, an ensemble may represent a logical grouping of measurements on the equipment being monitored, regardless of whether the measurement locations are temporary for the measurement, provided by the original equipment manufacturer, retrofitted at a later date, or one or more combinations thereof.

[0654] In one example, the ensemble can monitor bearing vibration in a single direction. In a further example, the ensemble can monitor three different directions (e.g., orthogonal directions) using a three-axis sensor. In yet another example, the ensemble can monitor four or more channels, with a first channel monitoring a single-axis vibration sensor and second, third, and fourth channels monitoring each of the three directions of the three-axis sensor. In another example, the ensemble can be fixed to a group of adjacent bearings on the same equipment or associated shafts. Various embodiments provide a method including a strategy for collecting waveform data from various ensembles deployed, such as for a vibration study, in a relatively efficient manner. The method also includes simultaneously monitoring a reference channel assigned to a constant reference location associated with the ensemble monitoring the machine. Coordination with the reference channel can be shown to support more complete correlation of waveforms collected from the ensembles. The reference sensor on the reference channel can be a single-axis vibration sensor or a phased reference sensor that can be triggered at a reference location, such as a rotating shaft. As disclosed herein, the method can further include simultaneously recording gap-free digital waveform data from all channels of each ensemble at a relatively high sampling rate to include all frequencies deemed necessary for proper analysis of the monitored machine during operation. Data from the ensembles can be streamed gap-free to a storage medium connectable to a cloud network facility, a local data link, a Bluetooth connection, a cellular data connection, etc., for subsequent processing.

[0655] In embodiments, the methods disclosed herein include strategies for collecting data from various ensembles, including digital signal processing techniques that can be subsequently applied to data from the ensembles to highlight or better isolate specific frequencies or waveform phenomena. This contrasts with current methods that collect multiple data sets at different sampling rates or use different hardware filtering configurations, including integration. The commitment to these configurations (known as a priori hardware configurations) results in relatively little flexibility in post-processing. These same hardware configurations have also been shown to increase vibration investigation times due to latency delays associated with configuring the hardware for each independent test. In embodiments, the methods for collecting data from various ensembles include data marker techniques that can be used to classify sections of stream data as homogeneous and belonging to a specific ensemble. In one example, the classification can be defined as operating speed. This allows for the creation of multiple ensembles, where conventional systems could only collect one ensemble. Many embodiments include post-processing analysis techniques for comparing the relative phase of all frequencies of interest not only between each channel in the collected ensemble, but also, when applicable, across all channels in the ensemble being monitored.

[0656] The present disclosure may include markers that can be applied to time marks or sample lengths within raw waveform data. Markers generally fall into two categories: preset or dynamic. Preset markers can be correlated to preset or existing operating conditions (e.g., load, head pressure, airflow cubic feet per minute, ambient temperature, RPM, etc.). These preset markers can be input directly into a data acquisition system. In certain examples, preset markers can be collected in a data channel in parallel with waveform data (e.g., vibration, current, voltage, etc.). Preset marker values ​​can also be entered manually.

[0657] For dynamic markers, such as trend data, it may be important to compare similar data, such as comparing vibration amplitudes or patterns, across a repeatable set of operating parameters. One example of this disclosure includes one of the parallel channel inputs being a key phasor trigger pulse from a moving shaft, which can provide RPM information at the instantaneous time of collection. In this example dynamic marker, a section of the collected waveform data displays the appropriate speed or speed range.

[0658] The present disclosure may also include dynamic markers that can be correlated to data obtained from post-processing and analysis performed on sampled waveforms. In further embodiments, dynamic markers may also be correlated to post-collection derived parameters, including RPM, as well as other operationally derived metrics, such as alarm conditions, such as maximum RPM. In a specific example, many modern devices that are candidates for vibration investigations using the portable data collection systems described herein do not include tachometer information. This is because, although measuring rotational speed is important for vibration investigations and analysis, adding a tachometer is not always practical or cost-justified. For fixed-speed machines, it will be appreciated that obtaining an accurate RPM measurement is less important, especially if the machine's approximate speed can be determined in advance. In light of the present disclosure, it will also be appreciated that various signal processing techniques can be used to derive RPM from raw data without the need for a dedicated tachometer signal.

[0659] In many embodiments, RPM information can be used to mark segments of raw waveform data over its collection history. Additionally, embodiments include techniques for collecting equipment data along a predetermined route in a vibration study. Dynamic markers allow analysis and trending software to utilize multiple segments (e.g., 2 minutes) of the collection interval indicated by the marker as multiple historical collection ensembles. This can be extended to any other operating parameter, such as load setting or ambient temperature, as previously mentioned. However, placing dynamic markers in an index file that points to a raw data stream allows for the classification of portions of the stream into homogenous entities, facilitating comparison with portions of previously collected raw data streams.

[0660] Many embodiments include a hybrid relational metadata-binary storage approach that can use the best of existing technologies for both relational and raw data streams. In embodiments, the hybrid relational metadata-binary storage approach can combine them using various marker linkages. Marker linkages enable: This enables many features not possible with traditional database technologies. This means that many features, integrations, compatibility, and extensibility not available with traditional database technologies are available.

[0661] MarkerLink can also store raw data quickly and efficiently using traditional binary storage and data compression techniques. This demonstrates that it can take advantage of many of the features, integrations, compatibility, and extensions offered by traditional raw data technologies, such as TMDS (National Instruments) and UFF (Universal File Format, such as UFF58). Furthermore, MarkerLink enables the collection of vast, rich datasets from ensembles in the same collection time as traditional systems. Rich datasets from ensembles can store data snapshots associated with predetermined collection criteria, and the proposed system can utilize MarkerLink to derive multiple snapshots from the collected data stream. This demonstrates that relatively rich analysis of the collected data can be achieved. One such benefit includes the ability to collect many more trend points for vibrations at specific frequencies or in the order of running speed vs. RPM, load, operating temperature, flow rate, and so on, in a similar amount of time compared to the time spent collecting data with traditional systems.

[0662] In embodiments, the platform 100 may include a local data collection system 102 deployed in an environment 104 to monitor signals from machinery, elements of machinery, and the machinery's environment, including heavy machinery deployed at a local job site or distributed job sites under common control. Heavy industrial machinery may include earthmoving machinery, heavy industrial on-road industrial vehicles, heavy industrial off-road industrial vehicles, turbines, turbomachinery, generators, pumps, pulley systems, manifolds, and valve systems deployed in various settings, and the like. In embodiments, heavy industrial machinery may also include earthmoving equipment, earth compaction equipment, transporting equipment, conveying equipment, aggregate manufacturing equipment, equipment used in concrete construction, and piling equipment. In examples, earthmoving 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 dump trucks, tankers, tippers, and trailers. In examples, material handling equipment may include cranes, conveyors, forklifts, and hoists. In examples, construction machinery may include tunneling and handling equipment, road rollers, concrete mixers, hot mix plants, road compactors, stone crushers, pavers, slurry sealers, spraying and plastering machines, and heavy industrial pumps. Further examples of heavy industrial equipment may include different systems such as implement traction, structures, powertrains, controls, and information. Heavy industrial equipment may include many different powertrains and combinations thereof to provide power for movement and to power accessories and onboard functionality. In each of these examples, the platform 100 deploys a local data collection system 102 in the environment 104 in which these machines, motors, pumps, etc. operate, and may be integrated and directly connected to each of the machines, motors, pumps, etc.

[0663] In an embodiment, the platform 100 may include a local data acquisition system 102 deployed in the environment 104 to monitor signals from operating and under construction machinery, such as turbines and generator sets, such as Siemens SGT6-5000F gas turbines, SST-900 steam turbines, SGen6-1000A generators, and SGen6-100A generators. In an embodiment, the local data acquisition system 102 may be deployed to monitor a steam turbine that rotates in an electrical current driven by high-temperature steam that may be directed through the turbine or otherwise originating from different sources, such as gas-fired burners, cores, or molten salt loops. In these systems, the local data acquisition system 102 may monitor the turbine and water or other fluid in a closed-loop cycle in which the water condenses and then heats up until it evaporates again. The local data acquisition system 102 may monitor the steam turbine separately from a fuel source deployed to heat the water to steam. In an example, the operating temperature of the steam turbine may be between 500 and 650°C. In many embodiments, an array of steam turbines may be arranged and configured for high, intermediate, and low pressures to optimally convert each steam pressure into rotary motion.

[0664] The local data acquisition system 102 may also be deployed in a gas turbine arrangement, thus monitoring not only the operating turbine, but also the supply of hot combustion gases to the turbine, which may exceed 1,500°C. Because these gases are much hotter than those of a steam turbine, the blades may be cooled with air that may escape through small openings to form a protective film or boundary layer between the exhaust gases and the blades. This temperature profile may be monitored by the local data acquisition system 102. Unlike a typical steam turbine, a gas turbine engine includes a compressor, a combustion chamber, and a turbine, all of which are journaled for rotation on a rotating shaft. The structure and operation of each of these components may be monitored by the local data acquisition system 102.

[0665] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from a water turbine, which functions as a rotary engine that harvests energy from moving water and potentially uses it to generate electricity. The type of water turbine or hydroelectric plant selected for a project may be based on the height of standing water, often called head, and the flow (or volume of water) at the site. In this example, a generator is installed at the end of a shaft that connects to the water turbine. As the water turbine rotates, its blades catch the naturally moving water, sending rotational power to the generator, generating electrical energy. In doing so, the platform 100 may monitor signals from the generator, turbine, local water systems, and flow control devices such as dam windows and gates. Additionally, the platform 100 may monitor local conditions on the electrical grid, including load, predicted demand, frequency response, and the like, and include such information in the monitoring and control deployed by the platform 100 in these hydroelectric settings.

[0666] In embodiments, platform 100 may include a local data collection system 102 deployed in an environment 104 to monitor signals from energy-producing environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuel, and hybrid-renewable energy plants. Many of these plants may use multiple forms of energy harvesting equipment, such as wind turbines, hydroelectric turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt sources. In embodiments, elements in such systems may include power lines, heat exchangers, desulfurization scrubbers, pumps, chillers, condensers, and coolers. In embodiments, specific implementations of turbomachinery, turbines, scroll compressors, and the like may be configured with arrayed controls to monitor large-scale facilities that create electricity for consumption, provide refrigeration, and create steam for local manufacturing and heating, and the arrayed control platform may be provided by industrial equipment providers, such as Honeywell and their Experion PKS platform. In embodiments, platform 100 may specifically communicate with and integrate with local manufacturer-specific controls, allowing equipment from one manufacturer to communicate with other equipment. Additionally, platform 100 allows local data collection systems 102 to collect information across systems from many different manufacturers. In an embodiment, platform 100 may include local data collection systems 102 deployed in environment 104 to monitor signals from marine industrial equipment, marine diesel engines, shipbuilding, oil and gas plants, refineries, petrochemical plants, ballast water treatment solutions, marine pumps and turbines, etc.

[0667] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from heavy industrial equipment and processes, including monitoring one or more sensors. By way of example, a sensor may be a device that can be used to detect or respond to any type of input from the physical environment, such as an electrical, thermal, or optical signal. In an embodiment, the local data collection system 102 may include multiple sensors, such as, but not limited to, 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, and an air temperature sensor. The torque sensor may include a magnetic torsion angle sensor. In one example, the torque and speed sensors of the local data collection system 102 may be similar to those discussed in U.S. Patent No. 8,352,149 to Meachem, issued January 8, 2013, and incorporated by reference as if fully set forth herein. In an embodiment, one or more sensors may be provided, such as a tactile sensor, a biosensor, a chemical sensor, an image sensor, a humidity sensor, an inertial sensor, or the like.

[0668] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from sensors that may provide signals for fault detection, including excessive vibration, improper materials, improper material properties, improper size, improper shape, improper weight, improper balance, etc. Additionally, there may be sensors for inventory control, inspection to ensure parts are packaged as planned, parts are within tolerances, packaging is damaged or stressed, and sensors indicating impact or damage during shipping. Additional fault sensors may also be used to detect lack of lubrication, over-lubrication, the need to clean a sensor window, the need for maintenance due to lack of lubrication, and the need for maintenance due to blocked or reduced flow in a lubricated area.

[0669] In an embodiment, platform 100 may include a local data collection system 102 deployed in an environment 104 that includes aircraft operations and manufacturing, including monitoring signals from special-purpose sensors, such as gyroscopes, accelerometers, and magnetometers, such as sensors used in an aircraft's attitude and heading reference system (AHRS). In an embodiment, platform 100 may include a local data collection system 102 deployed in environment 104 to monitor signals from image sensors, such as semiconductor charge-coupled devices (CCDs) with complementary metal-oxide semiconductor (CMOS) or negative-type metal-oxide semiconductor (NMOS, Live MOS) technology, active pixel sensors, etc. In an embodiment, platform 100 may include a local data collection system 102 deployed in environment 104 to monitor signals from sensors, such as infrared (IR) sensors, ultraviolet (UV) sensors, touch sensors, and proximity sensors. In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for optical character recognition (OCR), barcode reading, surface acoustic wave detection, transponder detection, communication with home automation systems, medical diagnostics, health monitoring, etc.

[0670] In an embodiment, platform 100 includes a local data collection system 102 deployed in environment 104 and may monitor signals from sensors such as MEMS (Micro-Electro-Mechanical Systems) sensors, such as ST Microelectronic's LSM303AH smart MEMS sensor, which includes an ultra-low power, high performance system-in-package featuring a 3D digital linear acceleration sensor and a 3D digital magnetic sensor.

[0671] In an embodiment, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines, such as turbines, windmills, industrial vehicles, and robots. These large machines include multiple components and elements that provide multiple subsystems for each machine. To that end, the platform 100 may include a local data collection system 102 deployed in the environment 104 to monitor signals from individual elements, such as axles, bearings, belts, buckets, gears, shafts, gearboxes, cams, carriages, camshafts, clutches, brakes, drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets, sleeves, valves, wheels, actuators, motors, and servomotors. Many of the machines and their elements may include servomotors. The local data collection system 102 may monitor motors, rotary encoders, and potentiometers in servomechanisms to provide three-dimensional det...

Claims

1. 1. A computer-implemented method comprising: receiving import data from one or more data sources, wherein the import data is data collected in 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; obtaining data relating to the one or more industrial entities within the industrial environment; generating a set of discrete digital twins representing the one or more industrial entities in the industrial environment based on the data related to the one or more industrial entities; Embedding the set of discrete digital twins within the environmental digital twin; establishing a connection with a sensor system of said 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 environmental digital twin and the set of discrete digital twins based on the real-time sensor data.

2. 10. The method of claim 1, wherein the connection with the sensor system is established via one of a webhook and an application programming interface (API).

3. 10. The method of claim 1, wherein the environmental digital twin and the set of discrete digital twins are visual digital twins configured to be rendered in a visual manner.

4. 4. The method of claim 3, further comprising outputting the visual digital twin to a client application that displays the visual digital twin through a virtual reality headset.

5. 4. The method of claim 3, further comprising outputting the visual digital twin to a client application that displays the visual digital twin via a display device of a user device.

6. 4. The method of claim 3, further comprising outputting the visual digital twin to a client application that displays the visual digital twin via an augmented reality enabled device.

7. receiving user input regarding one or more steps to be performed in an industrial process associated with the industrial environment; and 10. The method of claim 1, further comprising generating a process digital twin that defines the steps of the industrial process with respect to one or more of the industrial environment and the set of industrial entities.

8. 10. The method of claim 1, further comprising instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes includes data defining the environment digital twin, and wherein one or more entity nodes each include respective data defining a respective discrete digital twin of the set of discrete digital twins.

9. 10. The method of claim 8, wherein each of the edges represents a relationship between two respective digital twins.

10. 10. The method of claim 9, wherein embedding discrete digital twins includes connecting entity nodes corresponding to each discrete digital twin to the first node with edges representing respective relationships between each industrial entity represented by the respective discrete digital twin and the industrial environment.

11. 10. The method of claim 9, wherein each of the edges represents a spatial relationship between the two respective digital twins and an operational relationship between the two respective digital twins.

12. 10. The method of claim 9, wherein each of the edges stores metadata corresponding to the relationship between the two respective digital twins.

13. 9. The method of claim 8, wherein each entity node of the one or more entity nodes includes one or more properties of a respective property of a respective industry entity represented by the entity node.

14. 10. The method of claim 8, wherein each entity node of the one or more entity nodes includes one or more behaviors of a respective property of a respective industry entity represented by the entity node.

15. 10. The method of claim 8, wherein the first node comprises one or more properties of the industrial environment.

16. 10. The method of claim 8, wherein the first node comprises one or more behaviors of the industrial environment.

17. 10. The method of claim 1, further comprising: performing a simulation based on the environmental digital twin and the one or more discrete digital twins.

18. 20. The method of claim 17, wherein the simulation simulates one of the operation of a machine in the industrial environment that generates an output based on a set of inputs, and the movement of a worker in the industrial environment.

19. The method of claim 1 , wherein the imported data comprises a three-dimensional scan of the industrial environment.

20. The method of claim 1 , wherein the imported data comprises a LIDAR scan of the industrial environment.

21. 2. The method of claim 1, wherein generating the environment digital twin of the industrial environment comprises one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment.

22. 2. The method of claim 1, 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, the predefined digital twin including properties and behaviors of the industrial entity.

23. 2. The method of claim 1, wherein generating the set of discrete digital twins comprises classifying industrial entities in the imported data of the industrial environment and generating discrete digital twins corresponding to the classified industrial entities.

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