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Modeling past-dependent partial repairs for condition-based maintenance of continuously deteriorating systems

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  • Huynh, K.T.

Abstract

We are interested in the stochastic modeling of a condition-based maintained system subject to continuous deterioration and maintenance actions such as inspection, partial repair and replacement. The partial repair is assumed dependent on the past in the sense that it cannot bring the system back into a deterioration state better than the one reached at the last repair. Such a past-dependency can affect (i) the selection of a type of maintenance actions, (ii) the maintenance duration, (iii) the deterioration level after a maintenance, and (iv) the restarting system deterioration behavior. In this paper, all these effects are jointly considered in an unifying condition-based maintenance model on the basis of restarting deterioration states randomly sampled from a probability distribution truncated by the deterioration levels just before a current repair and just after the last repair/replacement. Using results from the semi-regenerative theory, the long-run maintenance cost rate is analytically derived. Numerous sensitivity studies illustrate the impacts of past-dependent partial repairs on the economic performance of the considered condition-based maintained system.

Suggested Citation

  • Huynh, K.T., 2020. "Modeling past-dependent partial repairs for condition-based maintenance of continuously deteriorating systems," European Journal of Operational Research, Elsevier, vol. 280(1), pages 152-163.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:1:p:152-163
    DOI: 10.1016/j.ejor.2019.07.007
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    Citations

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    Cited by:

    1. Huynh, K.T., 2021. "An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Hu, Jiawen & Shen, Jingyuan & Shen, Lijuan, 2020. "Opportunistic maintenance for two-component series systems subject to dependent degradation and shock," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. Bautista, Lucía & Castro, Inma T. & Landesa, Luis, 2022. "Condition-based maintenance for a system subject to multiple degradation processes with stochastic arrival intensity," European Journal of Operational Research, Elsevier, vol. 302(2), pages 560-574.
    4. Navarro, Jorge & Fernández-Martínez, Pedro, 2021. "Redundancy in systems with heterogeneous dependent components," European Journal of Operational Research, Elsevier, vol. 290(2), pages 766-778.
    5. Castro, Inma T. & Basten, Rob J.I. & van Houtum, Geert-Jan, 2020. "Maintenance cost evaluation for heterogeneous complex systems under continuous monitoring," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    6. Volodymyr Grudz & Yaroslav Grudz & Ivan Pavlenko & Oleksandr Liaposhchenko & Marek Ochowiak & Vasyl Pidluskiy & Oleksandr Portechyn & Mykola Iakymiv & Sylwia Włodarczak & Andżelika Krupińska & Magdale, 2023. "Ensuring the Reliability of Gas Supply Systems by Optimizing the Overhaul Planning," Energies, MDPI, vol. 16(2), pages 1-13, January.
    7. Renxi Gong & Siqiang Li & Weiyu Peng, 2020. "Research on Multi-Attribute Decision-Making in Condition-Based Maintenance for Power Transformers Based on Cloud and Kernel Vector Space Models," Energies, MDPI, vol. 13(22), pages 1-11, November.
    8. Huynh, K.T. & Vu, H.C. & Nguyen, T.D. & Ho, A.C., 2022. "A predictive maintenance model for k-out-of-n:F continuously deteriorating systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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