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A predictive maintenance policy with imperfect monitoring

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  • Curcurù, Giuseppe
  • Galante, Giacomo
  • Lombardo, Alberto

Abstract

For many systems, failure is a very dangerous or costly event. To reduce the occurrence of this event, it is necessary to implement a preventive maintenance policy to replace the critical elements before failure. Since elements do not often exhibit incipient faults, they are replaced before a complete exploiting of their useful life. To conjugate the objective of exploiting elements for almost all their useful life with the objective to avoid failure, condition based and, more recently, predictive maintenance policies have been proposed. This paper deals with this topic and proposes a procedure for the computation of the maintenance time that minimizes the global maintenance cost. By adopting a stochastic model for the degradation process and by hypothesizing the use of an imperfect monitoring system, the procedure updates by a Bayesian approach, the a-priori information, using the data coming from the monitoring system. The convenience in adopting the proposed policy, with respect to the classical preventive one, is explored by simulation, showing how it depends on some parameters characterizing the problem.

Suggested Citation

  • Curcurù, Giuseppe & Galante, Giacomo & Lombardo, Alberto, 2010. "A predictive maintenance policy with imperfect monitoring," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 989-997.
  • Handle: RePEc:eee:reensy:v:95:y:2010:i:9:p:989-997
    DOI: 10.1016/j.ress.2010.04.010
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    Cited by:

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    2. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    3. Shengjin Tang & Xiaosong Guo & Zhijie Zhou, 2014. "Mis-specification analysis of linear Wiener process–based degradation models for the remaining useful life estimation," Journal of Risk and Reliability, , vol. 228(5), pages 478-487, October.
    4. Lee, Juseong & Mitici, Mihaela, 2022. "Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Tang, Diyin & Makis, Viliam & Jafari, Leila & Yu, Jinsong, 2015. "Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 198-207.
    6. Yiwei Wang & Christian Gogu & Nicolas Binaud & Christian Bes & Raphael T Haftka & Nam-Ho Kim, 2018. "Predictive airframe maintenance strategies using model-based prognostics," Journal of Risk and Reliability, , vol. 232(6), pages 690-709, December.
    7. Michele Compare & Luca Bellani & Enrico Zio, 2017. "Availability Model of a PHM-Equipped Component," Post-Print hal-01652232, HAL.
    8. Shi, Yan & Lu, Zhenzhou & Huang, Hongzhong & Liu, Yu & Li, Yanfeng & Zio, Enrico & Zhou, Yicheng, 2022. "A new preventive maintenance strategy optimization model considering lifecycle safety," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Kim, Seokgoo & Choi, Joo-Ho & Kim, Nam Ho, 2022. "Inspection schedule for prognostics with uncertainty management," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.

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