Graphical user interface (GUI) based systems and methods are disclosed for regionizing full-size process plant displays for rendering on mobile user interface devices. A regionizer application receives a full-size process plant display that graphically represents at least a portion of a process plant that includes graphic representations of a plurality of process plant entities. The regionizer app determines display region(s) of the full-size process plant display that define corresponding view portions of the full-size process plant display. The display regions are transmitted to a mobile user interface device for rendering by a mobile display navigation app. The GUI based systems and methods can also automatically detect graphical process control loop display portions within full-size process plant displays for rendering on mobile user interface devices. The GUI based systems and methods can further refactor full-size process plant displays at various zoom and detail levels for visualization on mobile user interface devices.

Graphical user interface (GUI) based systems and methods are disclosed for regionizing full-size process plant displays for rendering on mobile user interface devices. A regionizer application receives a full-size process plant display that graphically represents at least a portion of a process plant that includes graphic representations of a plurality of process plant entities. The regionizer app determines display region(s) of the full-size process plant display that define corresponding view portions of the full-size process plant display. The display regions are transmitted to a mobile user interface device for rendering by a mobile display navigation app. The GUI based systems and methods can also automatically detect graphical process control loop display portions within full-size process plant displays for rendering on mobile user interface devices. The GUI based systems and methods can further refactor full-size process plant displays at various zoom and detail levels for visualization on mobile user interface devices.

A system for monitoring and controlling a manufacturing environment is disclosed. Aspects discussed include beacons broadcasting identifying information for aspects of the manufacturing environment such as workstations, manufactured items, machines, equipment, and people. The identifying information may be used to generate reports about the operation of these and possibly other aspects, as well as provide control options for changing the manufacturing environment. Employees may move about the environment to visually monitor activities taking place, while the system may automatically query for additional information about the tools, equipment, people encountered in the environment. Control options for changing aspects of the manufacturing processes may also be automatically displayed as well.

A visual prototyping and assembly system includes a work surface configured to hold one or more work-in-progress units during an assembly operation. The visual prototyping and assembly system may receive the one or more images of the work-in-progress units; determine a current assembly state of the work-in-progress units based on the one or more images of the work-in-progress units; generate a current digital twin model of the -in-progress units corresponding to the current assembly state of the work-in-progress units and display the current digital twin model a user interface; determine a subsequent assembly state of the work-in-progress units; and generate a subsequent digital twin model of the work-in-progress units corresponding to the subsequent assembly state of the work-in-progress units and display the subsequent digital twin model on the user interface.

A (GUI) for designing an industrial automation system includes a design window and a first accessory window. The GUI presents a library visualization representative of a plurality of objects within the first accessory window, each object is represented by an icon and corresponds to a respective industrial automation device. The GUI receives inputs indicative of a selection of one or more objects of the plurality of objects from the library, presents the one or more objects in the design window, determines that the one or more inputs do not comply with a set of industrial automation system rules comprising one or more relationships between a plurality of industrial automation devices, and displays a warning message that the one or more inputs do not comply with the set of industrial automation system rules.

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.

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 of the present technology 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 identify a variable that is available to be utilized in control logic for controlling an industrial automation environment. The machine learning-based analysis engine is further configured to determine that the variable is not utilized in the control logic. A recommendation component of the system is configured to, in an industrial programming environment, surface a recommendation to add the variable to the control logic.

In some implementations, a method includes receiving data characterizing an operating parameter of a machine, a time period of detection of the operating parameter, and one or more operating states associated with the machine. The method also includes rendering, in a graphical user interface (GUI) display space, a graph including a plot of the received data characterizing the operating parameter. A first axis of the graph is indicative of the time period and a second axis of the graph is indicative of the operating parameter. The method further includes receiving data characterizing user interaction with a first interactive graphical object located in the GUI display space. The first interactive graphical object indicative of a first operating state of the machine over a first sub-period of the time period. The method further includes replacing the first interactive graphical object with a first expanded interactive graphical object. The first expanded interactive graphical object overlays a first portion of the plot of the operating parameter detected during the first sub-period.

A method may include obtaining acquired process data regarding a plant process that is performed by a plant system. The method may further include obtaining from a process model, simulated process data regarding the plant process. The method may further include determining drift data for the process model based on a difference between the acquired process data and the simulated process data. The drift data may correspond to an amount of model drift associated with the process model. The method may further include determining whether the drift data satisfies a predetermined criterion. The method further includes determining, in response to determining that the drift data fails to satisfy the predetermined criterion, a model update for the process model.

In one aspect, a method includes receiving data characterizing an alarm event report associated with a first industrial machine generated by a first user via a first web-based graphical user interface (GUI). The first web-based GUI associated with an enterprise monitoring system of an industrial enterprise that includes the first industrial machine. The alarm event report includes at least a portion of an event dataset including information of an alarm event associated with the first industrial machine, and an identity of a second user assigned to work on the alarm event report by the first user. The method further includes providing the alarm event report to a second web-based graphical user interface (GUI) associated with the enterprise monitoring system. The method also includes receiving, via a first GUI of a first monitoring system, data characterizing additional information associated with the alarm event and/or edits to the event dataset.