Abstract: A method includes using at least one processor to detect that a tool coupled to an end effector of a robot having multiple joints is contacting a surface. The robot includes multiple joint motors configured to control multiple motions of the multiple joints. One or more control systems are configured to control each of the joint motors in a joint position mode. The method also includes identifying, via the at least one processor, a first joint of the multiple joints in response to detecting that the tool is contacting the surface. The method also includes sending, via the at least one processor, a command to at least one of the one or more control systems associated with a first joint motor of the multiple joint motors that corresponds to the first joint. The command is configured to cause the at least one of the one or more control systems to operate in a torque mode. The method also includes sending, via the at least one processor, a joint torque value to the at least one of the one or more control systems.

Abstract: A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

Abstract: An industrial integrated development environment (IDE) comprises a development interface that affords a user a great deal of control over the editing tools, workspace canvases, and project information rendered at a given time. The industrial IDE system automatically filters the tools, panels, and information available for selection based on a current project development task, such that a focused subset of editing tools relevant to a current development task or context are made available for selection while other tools are hidden. The development interface also allows the user to selectively render or hide selected tools or information from among the relevant, filtered set of tools. This can reduce or eliminate unnecessary clutter and aid in quickly and easily locating and selecting a desired editing function. The IDE's development interface can also conform to a structured organization of workspace canvases and panels that facilitates intuitive workflow.

Abstract: A computing device for collecting and preserving data acquired from various devices in a data model with the respective context of acquired data. The computing device may provide a user interface to receive the context or information model associated with a dataset. By providing data with its context, different software platforms may synthesize or analyze the retrieved data more efficiently. Moreover, the computing device may include transaction conditions to define a workflow for transferring the datasets using data model associated with one or more datasets for transmission of data to a destination. The transaction conditions may detail a custom workflow for data communication through an industrial automation system using the data model.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to visualize machine learning model status in an industrial automation environment. In some examples, a machine learning component receives process inputs associated with industrial devices in the industrial automated environment. The machine learning component processes the inputs to generate machine learning outputs and transfers the machine learning outputs to influence one or more functions of the industrial devices. The machine learning component reports operational data characterizing the machine learning outputs. A Human Machine Interface (HMI) component displays a visualization of the machine learning component and receives the operational data from the machine learning component.

Abstract: An industrial integrated development environment (IDE) supports the use of graphical device profiles to configure device parameters as part of an industrial control project. To allow an edit to first device represented by a first device profile to be applied easily to other device profiles, the industrial IDE system can record a user's interactions with the first device profile during a session of editing the device's parameters. These interactions are recorded as a sequence of cursor movements, mouse-click selections, keystrokes, and other such interactions. The user interactions are stored as a reusable interaction record which can be selectively applied to other device profiles to recreate or replay the user interactions on those other profiles.

Abstract: A power supply system includes modular power supply modules interconnected via a wire harness. The power supply system can be installed in one or more electrical cabinets in a manufacturing facility, for example, among other possible applications. The wire harness includes a fault bus and a synchronization bus, and the individual power supply modules detect and report faults as well as synchronize operation, such as pre-charging, via communication using the wire harness.

Abstract: A system may include a control system for controlling one or more operations of one or more industrial devices in an industrial system. The control system may receive streaming data comprising one or more visualizations representative of one or more live operational parameters associated with one or more industrial devices. The streaming data may include multiple image frames. The control system may also identify multiple datasets associated with the streaming data and generate multiple machine-readable images based on the multiple datasets. In addition, the control system may embed the multiple machine-readable images within the multiple image frames of the streaming data to generate updated streaming data and send the updated streaming data to a computing system that may extract the multiple machine-readable images from the updated streaming data.

Abstract: A cloud-based edge-as-as-service (EaaS) system allows edge gateways to be easily configured and deployed on the cloud for collection, contextualization, and egress of industrial data to downstream applications, including analytic applications, work order management systems, or visualization systems. The EaaS system uses predefined device profiles to automatically discover relevant data items on plant floor devices and present these data items to a user. Model configuration interfaces served by the EaaS system allow the user to map selected data items to predefined models for organizing or contextualizing the selected data, and for egressing the contextualized data to the target applications. These model configurations can be deployed on the cloud platform as edge gateways that use the resulting information models to collect and contextualize relevant items of device data during runtime and to export the modeled data to the target application.

Abstract: A system and method for monitoring and controlling assets to mitigate predicted future faults. The method includes receiving, by a processing circuit, data describing an asset from one or more data sources; generating, by the processing circuit, an asset data model based on the received data; receiving, by the processing circuit, an extensible data model describing an organizational structure of an enterprise associated with the asset; extending, by the processing circuit, the extensible data model to include the asset models executing, by the processing circuit, the extensible data model including the asset models to determine one or more key performance indicators for the asset; predicting, by the processing circuit, a future fault for the asset based on the key performance indicators; sending, by the processing circuit, an informed and prioritized notification to plant personnel regarding the predicted fault; and taking a corrective action to mitigate the predicted future fault.

Abstract: A system and method for monitoring and controlling an industrial process using a data model extensible to different industry applications. The system is configured to: receive data describing the industrial process from one or more data sources in a first format; contextualize and transform the data by determining one or more tags for the data, the one or more tags comprising context information describing characteristics of the entities involved in the industrial process; generate a data model describing the industrial process based on the one or more tags; receive a first indication from a user indicating a first industry application to which the data model will be applied; and extend the data model to include a second plurality of nodes representing entities associated with the first industry application and a second plurality of edges connecting the second plurality of nodes and describing relationships between the second plurality of nodes.

Abstract: A system and method for monitoring and controlling energy use in an industrial process. The method includes: receiving, by a processing circuit, data describing energy use in an industrial process from one or more data sources; contextualizing, by the processing circuit, the data describing the energy use in an industrial process; generating, by the processing circuit, an energy data model based on the contextualized data; executing, by the processing circuit, the energy data model to determine key performance indicators for the energy use in an industrial process; displaying, by the processing circuit, the key performance indicators to a user; determining, by the processing circuit, if the key performance indicators are above one or more pre-determined thresholds; and taking a corrective action in response to the key performance indicators being above the one or more pre-determined thresholds.

Abstract: A system and method for monitoring and controlling production of batches of products in an industrial process, the method comprising: receiving, by a processing circuit, data describing a batch of products generated in an industrial process from one or more data sources; contextualizing, by the processing circuit, the data describing the batch of products generated in the industrial process; generating, by the processing circuit, a batch data model based on the contextualized data; executing, by the processing circuit, the batch data model to determine key performance indicators for the batch of products; comparing, by the processing circuit, the key performance indicators to pre-determined key performance indicators; and performing an automated action based on a result of the comparison.

Abstract: The present technology relates to health metrics corresponding to industrial automation devices and a user experience for viewing and configuring health metrics. Health metrics of a device can be produced by obtaining performance metrics of the device and contextualizing the performance metrics based on contextualization information. The health metrics can be categorized based on applying rule sets to the health metrics. The rule sets can be selectively applied to the health metrics based on a type of a respective health metric. The health metrics and health metric categories can be instantiated in a user interface of a user device based on a request for device health information from the user device.

Abstract: The present technology relates to health metrics corresponding to industrial automation devices and a user experience for configuring calculation devices to produce the health metrics. Health metrics of a device can be produced by obtaining performance metrics of the device and contextualizing the performance metrics based on contextualization information. The health metrics can be categorized based on applying rule sets to the health metrics. Contextualization and application of rule sets can be selectively performed by one or more calculation devices, such as one or more servers, industrial devices, user devices, or the like based on configuration settings. The calculation devices can obtain the performance metrics, operations, and rule sets to produce the health metrics for instantiation on a user interface.

Abstract: The present technology relates to health metrics corresponding to industrial automation devices and a user experience for connecting to devices in an industrial automation environment to configure and view health metrics. Health metrics of a device can be obtained from a server, an industrial device, a controller coupled to the industrial device, or another source, and provided to a user interface device. The user interface device can display indications of the health metrics on a user interface. The user interface device can also establish a connection with a user device and provide the indications of the health metrics to the user device for display on a user interface of the user device.

Abstract: A latch system for an electrical equipment enclosure includes a chassis and a latch arm, engaged with a circuit breaker, that moves between first, second, and intermediate arm positions. A handle operatively connected to the circuit breaker is movable between ON, TRIP, OFF, and RESET handle positions. The handle is operatively connected to the latch arm such that the latch arm is positioned in the second arm position when the handle is located in said RESET handle position. The latch arm spring biases the latch arm into the first or intermediate positions when the handle is in the ON, TRIP, or OFF handle position. A lockout plunger is connected to the handle to rotate with the handle and is movable between disengaged and engaged positions. The lockout plunger prevents movement of the handle from the OFF to the ON handle position when the lockout plunger is in its engaged position.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.

Abstract: A tangible, non-transitory, computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive user input indicative of a selection of a user experience of a plurality of user experiences. The plurality of user experiences include a first user experience associated with a first event that occurred in an industrial automation system at a first time prior to receiving the user input and a second user experience associated with a second event expected to occur in the industrial automation system at a second time after receiving the user input. When executed, the instructions also cause the processing circuitry to determine, based on the user input, output representative data associated with the industrial automation system and instruct an extended reality device to present the output representative data.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.