Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence

Abstract

This paper presents a dynamic opportunistic condition-based maintenance strategy for multi-component systems. The strategy is based on real-time predictions of the remaining useful life under the simultaneous consideration of economic and stochastic dependence. First, the effect of a component’s degradation level on the remaining useful life of other components is considered. The remaining useful life of components that have a stochastic dependence on one another is predicted using stochastic filtering theory. Given the condition monitoring history data, we model the effect of a component’s degradation level on the remaining useful life of other components. And a penalty cost evaluates the additional cost of shifting the maintenance time. This allows us to determine the optimal trade-off between reducing the remaining useful life of some components and decreasing the set-up cost of maintenance. An optimization model is then established by choosing the dynamic opportunistic maintenance zone and optimal group structure that minimizes the long-term average maintenance cost of the system. A numerical example including three multi-component systems is presented. The results show that our proposed method maximizes production efficiency on the premise of ensuring system reliability, and reduces the system operation and maintenance costs.