Abstract: Systems and methods described herein relate to an asset risk predictor that may receive data corresponding to one or more industrial assets and generate a device model corresponding to the one or more industrial assets. The device model may indicate a relationship between a normal operation of a first industrial asset and data acquired by a second industrial asset. The device model may be trained while the first industrial asset and the second industrial asset are in situ and operating within a desired industrial automation process. An alert score may be generated based on the relationship indicated by the device model, where the alert score may be an indication of a likelihood of an event occurring at the first industrial asset. A maintenance work order may be generated based on alert scores and existing work orders.

Abstract: A work order management system leverages generative artificial intelligence (AI) to provide dynamic maintenance guidance to assist with execution of scheduled or reactive maintenance tasks. The work order management system can process technicians' natural language requests for assistance in performing a maintenance task on an industrial asset, and formulate guidance and recommendations based on the nature of the request, knowledge of the asset, learned optimal workflows for successfully performing the task, and other such information. The system can also prompt a generative AI model for supplemental information that can assist in formulating accurate maintenance guidance and recommendations. In some embodiments, the system can also monitor maintenance actions being performed by a technician and provide proactive guidance when the technician's behaviors indicate that assistance with a current maintenance task is required.

Abstract: A work order tracking system analyzes asset and work order data and provides a range of insights relating to asset performance and maintenance, including asset downtime statistics, maintenance efficiency, asset-specific financial information, and other such insights. Embodiments of the work order tracking system can also provide proactive recommendations and guidance for carrying out open work orders in a manner that improves or optimizes maintenance efficiency and effectiveness.

Abstract: A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

Abstract: Systems and methods described herein relate to an asset risk predictor that may receive data corresponding to one or more industrial assets and generate a device model corresponding to the one or more industrial assets. The device model may indicate a relationship between a normal operation of a first industrial asset and data acquired by a second industrial asset. The device model may be trained while the first industrial asset and the second industrial asset are in situ and operating within a desired industrial automation process. An alert score may be generated based on the relationship indicated by the device model, where the alert score may be an indication of a likelihood of an event occurring at the first industrial asset. A maintenance work order may be generated based on alert scores and existing work orders.

Abstract: Disclosed are various embodiments for a multilayered ergonomic handheld controller grip. The multilayered aspect allows for flexibility and functionality in providing the user a moisture wicking, texturized, durable, antimicrobial, cushioned, and re-attachable customized grip for a handheld controller. The removable embodiment allows for customization and optimization to a user's own physical characteristics eliminating standard fit designs and providing unique customization for enhanced user performance.

Abstract: Disclosed are various embodiments for a multilayered ergonomic handheld controller grip. The multilayered aspect allows for flexibility and functionality in providing the user a moisture wicking, texturized, durable, antimicrobial, cushioned, and re-attachable customized grip for a handheld controller. The removable embodiment allows for customization and optimization to a user's own physical characteristics eliminating standard fit designs and providing unique customization for enhanced user performance.