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Condition-based maintenance strategy optimization of meta-action unit considering imperfect preventive maintenance based on Wiener process

Author

Listed:
  • Xinlong Li

    (Chongqing University)

  • Yan Ran

    (Chongqing University)

  • Fangming Wan

    (Chongqing University)

  • Hui Yu

    (Chongqing University)

  • Genbao Zhang

    (Chongqing University
    Chongqing University of Arts and Sciences)

  • Yan He

    (Chongqing University)

Abstract

With the popularization and application of technologies such as monitoring and sensing, the application of condition-based maintenance has become increasingly widespread. However, most traditional condition-based maintenance strategies focus on a component or system, which is too large for fault diagnosis and location. Moreover, imperfect maintenance is seldom considered and most of them only optimize the single variable in condition-based maintenance decision. In this paper, the meta-action unit is taken as the research object, and the Wiener process is used to describe the performance degradation of the meta-action unit. Considering that the maintenance quality deteriorates with the increase of the number of maintenance, and has randomness, the beta distribution is used to establish an imperfect preventive maintenance quality model. A new maintenance optimization strategy is proposed, with the long-term maintenance cost rate as the goal, to obtain the best preventive maintenance threshold, inspection cycle and the optimal number of maintenance. Finally, a Monte Carlo simulation was used to solve numerically and analyze the influence of each parameter on the optimal decision. It can provide a reference for the decision-making when developing maintenance policies.

Suggested Citation

  • Xinlong Li & Yan Ran & Fangming Wan & Hui Yu & Genbao Zhang & Yan He, 2022. "Condition-based maintenance strategy optimization of meta-action unit considering imperfect preventive maintenance based on Wiener process," Flexible Services and Manufacturing Journal, Springer, vol. 34(1), pages 204-233, March.
    • Handle: RePEc:spr:flsman:v:34:y:2022:i:1:d:10.1007_s10696-021-09407-w
    DOI: 10.1007/s10696-021-09407-w
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    References listed on IDEAS

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    Cited by:

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    2. Panagiotis D. Paraschos & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2024. "A reinforcement learning/ad-hoc planning and scheduling mechanism for flexible and sustainable manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 714-736, September.

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