Selective maintenance of multi‐state systems with the repairperson fatigue effect and stochastic break duration

Abstract

Systems in the industry are often required to execute a sequence of missions, and the state of the multi‐state systems may deteriorate with the increasing of running time. To improve the system reliability of the next mission, selective maintenance is applied in the limited break. Due to the fatigue effect, the work rate of the repairperson decays with time. However, the existing selective maintenance studies do not consider the impact of fatigue effect on work rate, which leads to underestimating the duration of maintenance actions and overestimating the system reliability. To overcome the above problems, a new selective maintenance model for multi‐state systems is developed to maximize the system reliability of the next mission. First, the decay process of the work rate of a repairperson is described as a piecewise continuous decreasing exponential function of working time. Next, a homogenous discrete‐state continuous‐time Markov process is used to describe the performance capacity degradation of components. Considering the stochastic break duration, based on the state probability distribution of each component obtained by the Markov model, the universal generation function (UGF) method is applied to evaluate the multi‐state system reliability. Then, taking the selected maintenance actions and maintenance priority as decision variables, the resulting selective maintenance optimization problem is solved by a tailored genetic algorithm. Finally, two computational experiments are carried out to demonstrate the effectiveness of the proposed method.

Selective maintenance decision optimization for systems executing multi-mission under stochastic mission duration

“…Xu et al [36] developed a hybrid algorithm based on deep Q-network and discrete differential evolution to make selective maintenance for large multi-component systems to minimize future expected costs. Based on deep learning, Hesabi et al [37] used a long and short-term memory network for optimal solutions under the time constraint of the breaks to minimize the total cost and demonstrated the superiority of the deep learning approach with the comparison of the model-based approach. Liu et al [38] established a model of selective maintenance to maximize the system reliability and the constraint of maintenance cost and used an improved GA to find the optimal decision solution.…”