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Joint optimization of monitoring quality and replacement decisions in condition-based maintenance

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  • Nguyen, Khanh T. P.
  • Do, Phuc
  • Huynh, Khac Tuan
  • Bérenguer, Christophe
  • Grall, Antoine

Abstract

The quality of condition monitoring is an important factor affecting the effectiveness of a condition-based maintenance program. It depends closely on implemented inspection and instrument technologies, and eventually on investment costs, i.e., a more accurate condition monitoring information requires a more sophisticated inspection, hence a higher cost. While numerous works in the literature have considered problems related to condition monitoring quality, (e.g., imperfect inspection models, detection and localization techniques, etc.) few of them focus on adjusting condition monitoring quality for condition-based maintenance optimization. In this paper, we investigate how such an adjustment can help to reduce the total cost of a condition-based maintenance program. The condition monitoring quality is characterized by the observation noises on the system degradation level returned by an inspection. A dynamic condition-based maintenance and inspection policy adapted to such a observation information is proposed and formulated based on Partially Observable Markov Decision Processes. The use and advantages of the proposed joint inspection and maintenance model are numerically discussed and compared to several inspection-maintenance policies through numerical examples.

Suggested Citation

  • Nguyen, Khanh T. P. & Do, Phuc & Huynh, Khac Tuan & Bérenguer, Christophe & Grall, Antoine, 2019. "Joint optimization of monitoring quality and replacement decisions in condition-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 177-195.
    • Handle: RePEc:eee:reensy:v:189:y:2019:i:c:p:177-195
    DOI: 10.1016/j.ress.2019.04.034
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    References listed on IDEAS

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    1. William S. Lovejoy, 1991. "Computationally Feasible Bounds for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 39(1), pages 162-175, February.
    2. Khac Tuan Huynh & Anne Barros & Christophe Bérenguer & Inma T. Castro, 2011. "A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events," Post-Print hal-00790728, HAL.
    3. Huynh, K.T. & Barros, A. & Bérenguer, C. & Castro, I.T., 2011. "A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 497-508.
    4. B. Castanier & C. Bérenguer & A. Grall, 2003. "A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(4), pages 327-347, October.
    5. Do, Phuc & Voisin, Alexandre & Levrat, Eric & Iung, Benoit, 2015. "A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 22-32.
    6. Bouvard, K. & Artus, S. & Bérenguer, C. & Cocquempot, V., 2011. "Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 601-610.
    7. Nguyen, Kim-Anh & Do, Phuc & Grall, Antoine, 2015. "Multi-level predictive maintenance for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 83-94.
    8. Fouladirad, Mitra & Grall, Antoine, 2011. "Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 611-618.
    9. Wenbin Wang, 2008. "Condition-based Maintenance Modelling," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 5, pages 111-131, Springer.
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    Cited by:

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