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Optimal selective maintenance for multi-state systems in variable loading conditions

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  • Dao, Cuong D.
  • Zuo, Ming J.

Abstract

This paper studies the selective maintenance problem for multi-state series systems working in variable loading conditions in the next mission. In the mission, a component's degradation depends on its current state and the load applied on it. A load-dependent degradation model is proposed for multi-state components operating in variable loading conditions. This model is inspired by the load-sharing model where many components share a common workload and the failure rate of a component depends on the state of other components. A Monte-Carlo simulation method is presented to simulate the multi-state component's degradation and to evaluate the system reliability. The final objective is to determine the best selective maintenance strategy to maximize the expected system reliability in the next mission within available resources. An illustrative example, reliability estimation results, and analysis of optimal selective maintenance scenarios for different levels of budget limitation are provided.

Suggested Citation

  • Dao, Cuong D. & Zuo, Ming J., 2017. "Optimal selective maintenance for multi-state systems in variable loading conditions," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 171-180.
    • Handle: RePEc:eee:reensy:v:166:y:2017:i:c:p:171-180
    DOI: 10.1016/j.ress.2016.11.006
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    1. Nourelfath, Mustapha & Yalaoui, Farouk, 2012. "Integrated load distribution and production planning in series-parallel multi-state systems with failure rate depending on load," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 138-145.
    2. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    3. You, Ming-Yi & Li, Hongguang & Meng, Guang, 2011. "Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 590-598.
    4. Richard Cassady, C. & Paul Murdock, W. & Pohl, Edward A., 2001. "Selective maintenance for support equipment involving multiple maintenance actions," European Journal of Operational Research, Elsevier, vol. 129(2), pages 252-258, March.
    5. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, December.
    6. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    7. Schneider, Kellie & Richard Cassady, C., 2015. "Evaluation and comparison of alternative fleet-level selective maintenance models," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 178-187.
    8. XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.
    9. Dao, Cuong D. & Zuo, Ming J. & Pandey, Mayank, 2014. "Selective maintenance for multi-state series–parallel systems under economic dependence," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 240-249.
    10. D Lin & D Banjevic & A K S Jardine, 2006. "Using principal components in a proportional hazards model with applications in condition-based maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(8), pages 910-919, August.
    11. Pandey, Mayank & Zuo, Ming J. & Moghaddass, Ramin & Tiwari, M.K., 2013. "Selective maintenance for binary systems under imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 42-51.
    12. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    13. Mayank Pandey & Ming Zuo & Ramin Moghaddass, 2013. "Selective maintenance modeling for a multistate system with multistate components under imperfect maintenance," IISE Transactions, Taylor & Francis Journals, vol. 45(11), pages 1221-1234.
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    Cited by:

    1. Liu, Lujie & Yang, Jun & Kong, Xuefeng & Xiao, Yiyong, 2022. "Multi-mission selective maintenance and repairpersons assignment problem with stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
    3. Diallo, Claver & Venkatadri, Uday & Khatab, Abdelhakim & Liu, Zhuojun, 2018. "Optimal selective maintenance decisions for large serial k-out-of-n: G systems under imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 234-245.
    4. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Wenbin Cao & Xisheng Jia & Yu Liu & Qiwei Hu & Jianmin Zhao, 2019. "Selective maintenance optimisation considering random common cause failures and imperfect maintenance," Journal of Risk and Reliability, , vol. 233(3), pages 427-443, June.
    6. Noppada Teera-achariyakul & Dulpichet Rerkpreedapong, 2022. "Optimal Preventive Maintenance Planning for Electric Power Distribution Systems Using Failure Rates and Game Theory," Energies, MDPI, vol. 15(14), pages 1-19, July.
    7. Jiang, Tao & Liu, Yu, 2020. "Selective maintenance strategy for systems executing multiple consecutive missions with uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    8. Tang Tang & Lijuan Jia & Jin Hu & Yue Wang & Cheng Ma, 2022. "Reliability analysis and selective maintenance for multistate queueing system," Journal of Risk and Reliability, , vol. 236(1), pages 3-17, February.
    9. Shahraki, Ameneh Forouzandeh & Yadav, Om Prakash & Vogiatzis, Chrysafis, 2020. "Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    10. A. Khatab & C. Diallo & E.-H. Aghezzaf & U. Venkatadri, 2022. "Optimization of the integrated fleet-level imperfect selective maintenance and repairpersons assignment problem," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 703-718, March.
    11. Zhong, Jilong & Sanhedrai, Hillel & Zhang, FengMing & Yang, Yi & Guo, Shu & Yang, Shunkun & Li, Daqing, 2020. "Network endurance against cascading overload failure," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    12. Chaabane, K. & Khatab, A. & Diallo, C. & Aghezzaf, E.-H. & Venkatadri, U., 2020. "Integrated imperfect multimission selective maintenance and repairpersons assignment problem," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    13. Xisheng Jia & Wenbin Cao & Qiwei Hu, 2019. "Selective maintenance optimization for random phased-mission systems subject to random common cause failures," Journal of Risk and Reliability, , vol. 233(3), pages 379-400, June.
    14. Ji Hwan Cha & Maxim Finkelstein, 2019. "Optimal preventive maintenance for systems having a continuous output and operating in a random environment," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 327-350, July.
    15. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    16. Levitin, Gregory & Finkelstein, Maxim & Xiang, Yanping, 2021. "Optimal mission abort policies for repairable multistate systems performing multi-attempt mission," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    17. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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