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Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts

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  • Dinh, Duc-Hanh
  • Do, Phuc
  • Iung, Benoit

Abstract

For maintenance optimization of multi-component systems, opportunistic maintenance has been addressed in many studies since it allows considering the advantages of dependences between components in maintenance decision-making process. In the literature, economic dependence, which implies that joint maintenance of several components can reduce the maintenance cost, has been widely studied in the framework of opportunistic maintenance. There are however very few existing studies considering the advantages of structural dependence, whereby maintenance of a component requires disassembly of other components, in maintenance optimization. To face this issue, the objective of this paper is to propose a multi-level opportunistic predictive maintenance approach considering both economic and structural dependence. In that way, the economic and structural dependences between components are firstly formulated. A degradation model considering disassembly impacts is then developed. For opportunistic maintenance decision-making, two opportunistic thresholds are introduced. When corrective/preventive maintenance occurs, the first opportunistic threshold (eRo) is defined to select non-disassembled components for opportunistic maintenance. This first opportunistic decision allows considering the economic dependence between components. In addition, the maintenance of the selected components may require disassembly of other components which could be also good candidates to be opportunistically maintained. So, the second opportunistic threshold, sRo(sRo≥eRo), is then developed to select one or several disassembled components to be opportunistically maintained. To evaluate the performance of the proposed opportunistic maintenance approach, a cost model is developed. Particle swarm optimization algorithm is then applied to find the optimal decision variables. Finally, the proposed opportunistic maintenance approach is illustrated through a conveyor system to show its feasibility and added value in maintenance optimization framework.

Suggested Citation

  • Dinh, Duc-Hanh & Do, Phuc & Iung, Benoit, 2022. "Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:reensy:v:217:y:2022:i:c:s0951832021005573
    DOI: 10.1016/j.ress.2021.108055
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    1. L. C. Thomas, 1985. "Replacement of Systems and Components in Renewal Decision Problems," Operations Research, INFORMS, vol. 33(2), pages 404-411, April.
    2. Liang, Zhenglin & Parlikad, Ajith Kumar, 2020. "Predictive group maintenance for multi-system multi-component networks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Han, Xiao & Wang, Zili & Xie, Min & He, Yihai & Li, Yao & Wang, Wenzhuo, 2021. "Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Van Horenbeek, Adriaan & Pintelon, Liliane, 2013. "A dynamic predictive maintenance policy for complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 39-50.
    6. Zhou, Xiaojun & Huang, Kaimin & Xi, Lifeng & Lee, Jay, 2015. "Preventive maintenance modeling for multi-component systems with considering stochastic failures and disassembly sequence," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 231-237.
    7. Kallen, M.J. & van Noortwijk, J.M., 2005. "Optimal maintenance decisions under imperfect inspection," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 177-185.
    8. Linkan Bian & Nagi Gebraeel, 2014. "Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions," IISE Transactions, Taylor & Francis Journals, vol. 46(5), pages 470-482.
    9. Do, Phuc & Bérenguer, Christophe, 2020. "Conditional reliability-based importance measures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    10. Olde Keizer, Minou C.A. & Flapper, Simme Douwe P. & Teunter, Ruud H., 2017. "Condition-based maintenance policies for systems with multiple dependent components: A review," European Journal of Operational Research, Elsevier, vol. 261(2), pages 405-420.
    11. Nguyen, Khanh T.P. & Fouladirad, Mitra & Grall, Antoine, 2018. "Model selection for degradation modeling and prognosis with health monitoring data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 105-116.
    12. Do, Phuc & Vu, Hai Canh & Barros, Anne & Bérenguer, Christophe, 2015. "Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 56-67.
    13. Nguyen, Kim-Anh & Do, Phuc & Grall, Antoine, 2015. "Multi-level predictive maintenance for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 83-94.
    14. Dao, Cuong D. & Zuo, Ming J., 2017. "Selective maintenance of multi-state systems with structural dependence," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 184-195.
    15. Vu, Hai Canh & Do, Phuc & Barros, Anne & Bérenguer, Christophe, 2014. "Maintenance grouping strategy for multi-component systems with dynamic contexts," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 233-249.
    16. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    Full references (including those not matched with items on IDEAS)

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