A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems

Abstract

The development of product-service-system (PSS) urges the emergence of service-oriented maintenance mode (SOMA), which leads to the shift of maintenance manners from traditional high-cost in-house maintenance to the performance-based proactive service offerings for complex multi-component systems. In this case, a novel and adaptive maintenance grouping strategy that considers service responses, service interactions and dynamic prediction requirements is desired to implement the predictive maintenance planning. Thus, this paper presents a service-oriented dynamic multi-level predictive maintenance grouping strategy. It involves component level, dynamic grouping level and system level to fulfill the individual optimization and grouping optimization. Firstly, the individual service time based on real-time remaining useful life (RUL) distribution information is optimized and obtained with the minimum average cost. Secondly, considering the existing economic and resource dependence in predictive and corrective services, this paper dynamically groups the predicted optimal individual services from rolling planning horizon at each calculated service planning time. The penalty costs and grouping service costs are both constructed. Then, a modified k-means method is designed to find out the optimal short-term grouping execution strategy from medium-term prediction information where the average cost savings and relative availability improvement degree are regarded as the optimization objectives. Finally, some numerical examples with three predictive grouping scenarios show that our grouping strategy could provide a feasible and effective long-term dynamic maintenance planning for proactive OEM or PSS providers. It is adapted to the SOMA on complex multi-component systems.