Condition-based maintenance policy for systems under dynamic environment

This paper addresses the challenge of optimizing maintenance for systems with varying degradation characteristics in dynamic environment. The focus is on repairable systems, where degradation is characterized by a Wiener process that incorporates the dynamic influence of the environment. We investigate a condition-based maintenance model aiming at minimizing the expected cost over a finite and infinite horizon. Firstly, the model considers the evolution of environment as a Markov process, which influences the drift parameter in the Wiener process. The choice between a corrective and preventive replacement rests on the periodic inspections on the system and environment. Subsequently, the maintenance model is formulated in the framework of Markov decision process. Meanwhile, the backward dynamic programming algorithm and value iteration algorithm are employed to gain the optimal maintenance policies over the finite and the infinite planning horizons, respectively. Finally, the proposed model is further explained with concrete examples and a comprehensive sensitivity analysis. Numerical results clearly demonstrate notable variations in the optimal maintenance strategies in different periods and underscore the importance of taking proactive maintenance measures in harsh environmental conditions, even if the degradation level of the system is relatively low.