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A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems

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  • Guo, Chunhui
  • Liang, Zhenglin

Abstract

Optimizing both inspection and maintenance strategies for multi-state systems is challenging, especially when the inspected conditions contain uncertainties. One classic approach for addressing this issue is the Partially Observable Markov Decision Process (POMDP). However, the POMDP often considers the system is periodically inspected, resulting in a waste of inspection resources (cost and manpower) in the early stage of the system. To predictively optimize the inspection strategies, we formulate a new model-Predictive Markov Decision Process (PMDP). It extends the POMDP by embedding the Forward algorithm for inspection timing prediction and the Baum–Welch algorithm for model parameters estimation. Therefore, it could harvest the inspection information for predicting the successive inspection timing in an online updating scheme based on the new observation. In this manner, maintenance actions can take place at the predicted inspection timing that reduces unnecessary inspections. The PMDP manifests the power of predictive maintenance. As illustrated by the case study, the PMDP outperforms the POMDP under routine inspection by saving 26.3% of the cost on average.

Suggested Citation

  • Guo, Chunhui & Liang, Zhenglin, 2022. "A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003167
    DOI: 10.1016/j.ress.2022.108683
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    1. Sheu, Shey-Huei & Chang, Chin-Chih & Chen, Yen-Luan & George Zhang, Zhe, 2015. "Optimal preventive maintenance and repair policies for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 78-87.
    2. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    3. Li, Ruopu & Arzaghi, Ehsan & Abbassi, Rouzbeh & Chen, Diyi & Li, Chunhao & Li, Huanhuan & Xu, Beibei, 2020. "Dynamic maintenance planning of a hydro-turbine in operational life cycle," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Nguyen, Khanh T. P. & Do, Phuc & Huynh, Khac Tuan & Bérenguer, Christophe & Grall, Antoine, 2019. "Joint optimization of monitoring quality and replacement decisions in condition-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 177-195.
    5. Do, Phuc & Voisin, Alexandre & Levrat, Eric & Iung, Benoit, 2015. "A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 22-32.
    6. Eslam Mohammed Abdelkader & Tarek Zayed & Mohamed Marzouk, 2019. "Modelling the Deterioration of Bridge Decks Based on Semi-Markov Decision Process," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 10(1), pages 23-45, January.
    7. Koutras, V.P. & Malefaki, S. & Platis, A.N., 2017. "Optimization of the dependability and performance measures of a generic model for multi-state deteriorating systems under maintenance," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 73-86.
    8. Liu, Xingchen & Sun, Qiuzhuang & Ye, Zhi-Sheng & Yildirim, Murat, 2021. "Optimal multi-type inspection policy for systems with imperfect online monitoring," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    9. Chen, Zhen & Li, Yaping & Xia, Tangbin & Pan, Ershun, 2019. "Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 123-136.
    10. Barker, C.T. & Newby, M.J., 2009. "Optimal non-periodic inspection for a multivariate degradation model," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 33-43.
    11. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    12. Song, Chaolin & Zhang, Chi & Shafieezadeh, Abdollah & Xiao, Rucheng, 2022. "Value of information analysis in non-stationary stochastic decision environments: A reliability-assisted POMDP approach," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    13. Soleimani, Morteza & Campean, Felician & Neagu, Daniel, 2021. "Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    14. Compare, Michele & Baraldi, Piero & Marelli, Paolo & Zio, Enrico, 2020. "Partially observable Markov decision processes for optimal operations of gas transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    15. 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.
    16. Michael Jong Kim, 2016. "Robust Control of Partially Observable Failing Systems," Operations Research, INFORMS, vol. 64(4), pages 999-1014, August.
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    Cited by:

    1. Finkelstein, Maxim & Cha, Ji Hwan & Bedford, Tim, 2023. "Optimal preventive maintenance strategy for populations of systems that generate outputs," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Wang, Siqi & Zhao, Xian & Wu, Congshan & Wang, Xiaoyue, 2023. "Joint optimization of multi-stage component reassignment and preventive maintenance for balanced systems considering imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Gámiz, M.L. & Navas-Gómez, F. & Raya-Miranda, R. & Segovia-García, M.C., 2023. "Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. Arcieri, Giacomo & Hoelzl, Cyprien & Schwery, Oliver & Straub, Daniel & Papakonstantinou, Konstantinos G. & Chatzi, Eleni, 2023. "Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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