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

Remaining useful life (RUL) is predicted considering stochastic dependence.Model the degradation level of some components effect on the RUL of other component.An dynamic opportunistic maintenance decision is presented based on the RUL.The trade-off between reducing the RUL and the set-up cost is considered.The strategy is adaptive through updating maintenance zone and grouping structure. 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.