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A condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems

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  • Khac Tuan Huynh
  • Antoine Grall

Abstract

Most condition-based imperfect maintenance models developed over the last few decades are memoryless in the sense that maintenance efficiency is completely s -independent of previous interventions. However, many maintenance activities exhibit their past dependency in engineering practice, and this significant property should not be ignored in maintenance modeling. In this spirit, our aim is to develop a condition-based maintenance model for continuously deteriorating systems subject to a special kind of past-dependent imperfect repairs. Such a repair can put the system back to a deterioration level better than the one at just before the current repair, but worse than the one reached at the last repair. Besides, inspection and replacement are memoryless actions available for the system. They result in different effects on the system deterioration and incur different costs. To achieve high economic performances in the long term, these actions are coordinated into a control-limit deterioration-based maintenance policy. Its long-run maintenance cost rate is analytically evaluated using the semi-regenerative process theory. Numerous sensitivity studies to maintenance costs and to system characteristics give a thorough understanding about the policy behavior. Furthermore, comparisons with more classical policies justify the importance of incorporating the past dependency in maintenance modeling.

Suggested Citation

  • Khac Tuan Huynh & Antoine Grall, 2020. "A condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems," Journal of Risk and Reliability, , vol. 234(2), pages 333-358, April.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:2:p:333-358
    DOI: 10.1177/1748006X19884210
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).

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