Condition-based Maintenance Optimization of Degradable Systems

被引:2
作者
Wei, Shuaichong [1 ]
Nourelfath, Mustapha [1 ]
Nahas, Nabil [2 ]
机构
[1] Univ Laval, Mech Engn Dept, Quebec City, PQ, Canada
[2] Univ Moncton, Dept Adm, Moncton, NB, Canada
关键词
Maintenance; Degradation side effects; Multi-state systems; Markov chains; Optimization; (X)OVER-BAR CONTROL CHART; PREVENTIVE MAINTENANCE; JOINT OPTIMIZATION; OPTIMAL REPLACEMENT; MODEL; COST; DECISIONS; POLICIES;
D O I
10.33889/IJMEMS.2022.7.1.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper develops a mathematical model for condition-based maintenance optimization of multi-state systems. The majority of the existing literature on maintenance optimization assume that there is no additional cost incurred because of side effects of equipment degradation. Nevertheless, as the operating cost increases with equipment age and degradation, it is important to consider the degradation side effects in the maintenance decision-making process. An important feature of the proposed model lies in the fact that it incorporates side effect of degradation process into condition-based preventive maintenance optimization. We develop a continuous-time discrete-state Markov chain model describing the deterioration stochastic process of a single component. The component is modeled as a multi-state system, where each discrete state is characterized by a degradation level. Numerical examples show the importance of considering such side effect costs when optimizing the choice of maintenance policy. The proposed model is extended to deal with multi-state series systems. Using an example of a series system with two components, it is shown that preventive maintenance and side effect costs should not be optimized for each component individually, but from the perspective of the series system as a whole.
引用
收藏
页码:1 / 15
页数:15
相关论文
共 50 条
  • [1] Joint condition-based maintenance and condition-based production optimization
    Broek, Michiel A. J. Uit Het
    Teunter, Ruud H.
    de Jonge, Bram
    Veldman, Jasper
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 214
  • [2] Optimization of condition-based maintenance for degradation systems under imperfect maintenance
    Li, Q. (licheng0001@hotmail.com), 1600, Chinese Society of Astronautics (34): : 316 - 324
  • [3] Condition-based inspection scheme for condition-based maintenance
    Golmakani, Hamid Reza
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2012, 50 (14) : 3920 - 3935
  • [4] Joint optimization of condition-based maintenance and condition-based reallocation for a system with multiple degrading components
    Wang, Jun
    Yang, Yating
    Fu, Yuqiang
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2024, 40 (01) : 339 - 361
  • [5] Opportunistic condition-based maintenance optimization for electrical distribution systems
    Wang, Yifei
    He, Rui
    Tian, Zhigang
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 236
  • [6] Condition-based Maintenance Optimization Using Neural Network-based Health Condition Prediction
    Wu, Bairong
    Tian, Zhigang
    Chen, Mingyuan
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2013, 29 (08) : 1151 - 1163
  • [7] Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions
    Chen Chuang
    Lu Ningyun
    Jiang Bin
    Xing Yin
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2020, 31 (04) : 841 - 851
  • [8] Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay
    Zhang, Nan
    Cai, Kaiquan
    Deng, Yingjun
    Zhang, Jun
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 241
  • [9] Scenario Reduction Method based on Output Performance for Condition-Based Maintenance Optimization
    Qian, Xinbo
    Tang, Qiuhua
    MECHANIKA, 2017, 23 (05): : 743 - 749
  • [10] Condition-based maintenance in the cyclic patrolling repairman problem
    Havinga, Maik J. A.
    de Jonge, Bram
    INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2020, 222