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Optimization of the dependability and performance measures of a generic model for multi-state deteriorating systems under maintenance

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  • Koutras, V.P.
  • Malefaki, S.
  • Platis, A.N.

Abstract

In this paper, a general model for multi-state deteriorating systems with condition based preventive maintenance is introduced and analyzed extensively. The system experiences various levels of deterioration and at each stage, an inspection is carried out at constant time intervals in order to identify what kind of preventive maintenance, the system should undergo. When the system fails, despite preventive maintenance, a repair procedure is carried out and the system is restored to its initial fully operational state. The proposed model incorporates also imperfect maintenance, either minimal or major, failed maintenance and sudden failures that may occur mostly due to external factors at any deterioration state as well. Moreover, the sojourn times are assumed to be generally distributed. The main dependability and performance measures of the proposed model are computed while the corresponding transient measures are estimated using Monte Carlo simulation. Our endmost aim is to distinguish inspection and consequently maintenance policies that optimize multi-state deteriorating system's dependability and/or performance. Additionally, multi-objective optimization problems are formulated and solve in order to distinguish preventive maintenance policies that optimize simultaneously both the dependability and performance measures.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:166:y:2017:i:c:p:73-86
    DOI: 10.1016/j.ress.2017.01.002
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    References listed on IDEAS

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    Cited by:

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    2. Vlad Stefan Barbu & Nicolas Vergne, 2019. "Reliability and Survival Analysis for Drifting Markov Models: Modeling and Estimation," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1407-1429, December.
    3. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
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    5. Faddoul, R. & Raphael, W. & Chateauneuf, A., 2018. "Maintenance optimization of series systems subject to reliability constraints," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 179-188.
    6. Fang, Chen & Cui, Lirong, 2021. "Reliability evaluation for balanced systems with auto-balancing mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    7. 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).
    8. 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).
    9. Khaled Guerraiche & Latifa Dekhici & Eric Chatelet & Abdelkader Zeblah, 2021. "Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm," Energies, MDPI, vol. 14(13), pages 1-19, July.
    10. Liu, Yu & Liu, Qinzhen & Xie, Chaoyang & Wei, Fayuan, 2019. "Reliability assessment for multi-state systems with state transition dependency," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 276-288.

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