Selective Maintenance of Multistate Systems Considering the Random Uncertainty of the System Mission Period and Mission Breaks

作     者：Wang, Hai-Peng Duan, Fu-Hai Wang, Xiao-Long He, Yu-Ling 

作者机构：North China Elect Power Univ Dept Mech Engn Baoding 071003 Hebei Peoples R China North China Elect Power Univ Hebei Key Lab Elect Machinery Hlth Maintenance Fa Baoding 071003 Hebei Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Liaoning Peoples R China 

出 版 物：《ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING》 (Arab. J. Sci. Eng.)

年 卷 期：2023年第48卷第5期

页      面：7059-7075页

核心收录：

基　　金：National Natural Science Foundation of China Natural Science Foundation of Hebei Province, China [E2020502032] Fundamental Research Funds for the Central Universities [2022MS095] 3rd Top Youth Talent Support Program of Hebei Province [-27] 

主　　题：Selective maintenance Multistate system Random uncertainty Particle swarm optimization algorithm 

摘      要：A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is proposed. Unlike traditional studies, the proposed model not only considers the random uncertainty of the mission period and mission breaks and the requirements of different system performance levels but also effectively manages the selective maintenance problems of multistate systems by using a heuristic algorithm. In this model, random variables that conform to a verified distribution are employed to characterize the randomness of the mission period and mission breaks, while the service age reduction model is utilized to describe the effective age of the system components after maintenance. To construct this method, the quantitative relationships between the system maintenance cost and the service age reduction factor and between the maintenance time and the service age reduction factor are established. Then, based on the multistate system reliability and general generating function technology, the mission completion rate models of the components and the system are established. Finally, the solution approach for making selective maintenance decisions for multistate systems based on the particle swarm optimization (PSO) algorithm is presented. To validate the effectiveness of the proposed model as well as the solution algorithm, a coal transportation system at a thermal power station is studied, and satisfactory results are obtained.