Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance

With the development of sensor and communication technology, condition-based maintenance (CBM) attracts increasing attention, especially for multi-component systems. This paper aims to investigate the optimal CBM policy under periodic inspection for a K-out-of-N: G system, where economic dependency, stochastic dependency and imperfect maintenance are emphasized. The objective is to minimize the expected long-run discounted cost. In the model, the cumulative degradation of each component is modeled by heterogeneous stochastic processes, the dependence among all components is characterized by a copula function, and the imperfect maintenance is represented by a reduction in the degradation level. Since the system has Markov property, we solve the CBM optimization problem based on Markov decision process (MDP) framework. To ease the computation burden, we discretize the continuous state space and then use the value iteration algorithm with Monte Carlo simulation to find the optimal inspection interval and the optimal CBM policy. Numerical studies for a 1-out-of-2: G system are conducted to systematically examine the impacts of degradation processes, copula functions and imperfect maintenance on the optimal maintenance decisions, which provides insights for multi-component system maintenance. A sensitivity analysis of cost-related parameters is also performed.