Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions

This paper presents a selective maintenance optimization problem for complex systems composed of stochastically dependent components. The components of a complex system degrade during mission time, and their degradation states vary from perfect functioning to complete failure states. The degradation rate of each component not only depends on its intrinsic degradation but also on the state of other dependent components of the system. The proposed approach captures the two-way interactions between components through system performance rates and uses Monte Carlo simulation to compute the reliability of the system in the next operational mission. Different maintenance actions such as do-nothing, perfect, and stochastic imperfect maintenance are considered during the maintenance break to improve the reliability of the system. The selective maintenance bi-objective optimization problem is modelled considering both the expected value and variance of the system reliability as objective functions. Time and budget are considered as constraints for finding the optimal maintenance strategy. Two illustrative examples are provided for a better understanding of the proposed approach and for demonstrating its effectiveness.