Towards adaptive scheduling of maintenance for Cyber-Physical Systems

Abstract

Scheduling and control of Cyber-Physical Systems (CPS) are becoming increasingly complex, requiring the development of new techniques that can effectively lead to their advancement. This is also the case for failure detection and scheduling component replacements. The large number of factors that influence how failures occur during operation of a CPS may result in maintenance policies that are time-monitoring based, which can lead to suboptimal scheduling of maintenance. This paper investigates how to improve maintenance scheduling of such complex embedded systems, by means of monitoring in real-time the critical components and dynamically adjusting the optimal time between maintenance actions. The proposed technique relies on machine learning classification models in order to classify component failure cases vs. non-failure cases, and on real-time updating of the maintenance policy of the sub-system in question. The results obtained from the domain of printers show that a model that is responsive to the environmental changes can enable consumable savings, while keeping the same product quality, and thus be relevant for industrial purposes.