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Safety constraints applied to an adaptive Bayesian condition-based maintenance optimization model

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  • Flage, Roger
  • Coit, David W.
  • Luxhøj, James T.
  • Aven, Terje

Abstract

A model is described that determines an optimal inspection and maintenance scheme for a deteriorating unit with a stochastic degradation process with independent and stationary increments and for which the parameters are uncertain. This model and resulting maintenance plans offers some distinct benefits compared to prior research because the uncertainty of the degradation process is accommodated by a Bayesian approach and two new safety constraints have been applied to the problem: (1) with a given subjective probability (degree of belief), the limiting relative frequency of one or more failures during a fixed time interval is bounded; or (2) the subjective probability of one or more failures during a fixed time interval is bounded. In the model, the parameter(s) of a condition-based inspection scheduling function and a preventive replacement threshold are jointly optimized upon each replacement and inspection such as to minimize the expected long run cost per unit of time, but also considering one of the specified safety constraints. A numerical example is included to illustrate the effect of imposing each of the two different safety constraints.

Suggested Citation

  • Flage, Roger & Coit, David W. & Luxhøj, James T. & Aven, Terje, 2012. "Safety constraints applied to an adaptive Bayesian condition-based maintenance optimization model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 16-26.
    • Handle: RePEc:eee:reensy:v:102:y:2012:i:c:p:16-26
    DOI: 10.1016/j.ress.2012.01.006
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Zhao, Yunfei & Smidts, Carol, 2022. "Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Bismut, Elizabeth & Pandey, Mahesh D. & Straub, Daniel, 2022. "Reliability-based inspection and maintenance planning of a nuclear feeder piping system," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    5. Mosayebi Omshi, E. & Grall, A. & Shemehsavar, S., 2020. "A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters," European Journal of Operational Research, Elsevier, vol. 282(1), pages 81-92.
    6. Truong-Ba, Huy & Cholette, Michael E. & Borghesani, Pietro & Ma, Lin & Kent, Geoff, 2021. "Condition-based inspection policies for boiler heat exchangers," European Journal of Operational Research, Elsevier, vol. 291(1), pages 232-243.
    7. Fritzsche, R. & Gupta, J.N.D. & Lasch, R., 2014. "Optimal prognostic distance to minimize total maintenance cost: The case of the airline industry," International Journal of Production Economics, Elsevier, vol. 151(C), pages 76-88.
    8. Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Roger Flage, 2013. "On risk reduction principles in the context of maintenance optimisation modelling," Journal of Risk and Reliability, , vol. 227(3), pages 241-250, June.
    10. 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.
    11. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    12. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    13. Cai, Yue & Teunter, Ruud H. & de Jonge, Bram, 2023. "A data-driven approach for condition-based maintenance optimization," European Journal of Operational Research, Elsevier, vol. 311(2), pages 730-738.

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