Abstract: Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Abstract: Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Abstract: Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Abstract: Techniques to facilitate automatic discovery of industrial assets and associated data in an industrial automation environment are disclosed herein. In at least one implementation, a computing system iteratively scans a communication network to automatically detect at least one industrial device on the communication network. The computing system determines a unique identifier of a device type associated with the at least one industrial device detected on the communication network. The computing system determines an identity object for the industrial device and maps the identity object to the unique identifier of the device type associated with the industrial device. Based on the identity object for the industrial device, the computing system establishes communication with the industrial device on the communication network and receives operational data from the industrial device.

Abstract: Techniques to facilitate predictive maintenance for industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives a plurality of industrial automation process variables associated with at least one industrial asset employed in an industrial automation process. The industrial automation process variables are fed into a machine learning model associated with the at least one industrial asset to generate a future maintenance event prediction for the at least one industrial asset. The future maintenance event prediction for the at least one industrial asset is provided to an industrial controller that controls the at least one industrial asset.

Abstract: Techniques to facilitate synchronization of industrial assets in an industrial automation environment are disclosed herein. In at least one implementation, a computing system receives time-series industrial process data associated with a plurality of process subsystems of an industrial automation process. The time-series industrial process data is fed into a machine learning model associated with the industrial automation process to dynamically generate a process duration prediction for a first one of the process subsystems and responsively determine an updated set point for a second one of the process subsystems based on the process duration prediction for the first one of the process subsystems. The updated set point for the second one of the process subsystems is provided to an industrial controller associated with the second one of the process subsystems.

Abstract: Techniques to facilitate automatic discovery of industrial assets and associated data in an industrial automation environment are disclosed herein. In at least one implementation, a computing system iteratively scans a communication network to automatically detect at least one industrial device on the communication network. The computing system determines a unique identifier of a device type associated with the at least one industrial device detected on the communication network. The computing system determines an identity object for the industrial device and maps the identity object to the unique identifier of the device type associated with the industrial device. Based on the identity object for the industrial device, the computing system establishes communication with the industrial device on the communication network and receives operational data from the industrial device.

Abstract: A system for illuminating a light emitting panel for backlighting a visual display positions the panel behind the visual display at a front wall of a processor cabinet. The light emitting panel has at least one layer of optical fibers which extend from the panel and are bundled and terminated in one or more receptors which are mounted in the cabinet remote from the panel and visual display. A cover is hinged to the cabinet and mounts a light source in operative alignment with the receptors when the cover is closed to illuminate the panel and display. The cover can be swung open to position the light source in a maintenance position in which light bulbs of the source can be removed and replaced.