Edge-Driven Predictive Maintenance Architecture for High-Stakes Industrial Networks
Keywords:
Distributed cognition,Cognitive maintenance,Mission-critical networks,Industrial network resilience,Predictive maintenance systems,Autonomous fault diagnosis,Edge intelligence,Industrial IoT reliability,Adaptive network orchestration,Cyber-physical system maintenance.Abstract
A distributed cognitive maintenance framework for mission-critical networks is proposed that integrates existing automation and supervisory systems in order to improve their maintenance. Maintenance is neither an end-user activity nor an operation, but a supported and automated activity that enhances the operation of the mission-critical systems. Maintenance activities include predictive and proactive maintenance, real-time fault detection and diagnosis, resource-aware scheduling and allocation, and human-actor collaboration with supervisory control. To achieve this, data-sensing systems issue data in a sentient fashion to cognitive agents, and reasoning agents send information for collective decision making. Control systems, especially for closed-loop operations, are taken into consideration.
Mission-critical networks serve production and product operations in real time. They are deployed and operated in an automation mode that may also ensure adequate redundancy for fault tolerance. Nevertheless, systems and facilities remainebt without any of fundamental principles on how to perform obsolescence management, preventive maintenance or even fault detection and diagnosis. Communication between such systems is commonly limited to supervisory control and the exchange of operational parameters. A different view considers maintenance as a mission-supported activity closely integrated with the operation.
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