Selective Maintenance Optimization for Multi-State Systems Operating in Dynamic Environments

This paper deals with selective maintenance of a multistate series system working under time-varying environmental (or operational) conditions. The environmental conditions are evolving dynamically during the mission and influence the degradation rate of each component and the whole system. We assume that the environmental conditions vary as a continuous-time Markov chain. The components are maintained during the maintenance break between two consecutive missions performing maintenance actions: do-nothing, imperfect, and perfect maintenance. The selective maintenance optimization problem is used to find the optimal maintenance strategy in order to maximize the expected system reliability in the next mission subjected to maintenance time and budget limitations. Monte Carlo simulation is used to evaluate the reliability of the system at the end of the next mission considering variable environmental/operational conditions. An example is provided to demonstrate the importance of considering the uncertainty in environmental (or operational) conditions.