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Optimization of maintenance policy using the proportional hazard model

Author

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  • Samrout, M.
  • Châtelet, E.
  • Kouta, R.
  • Chebbo, N.

Abstract

The evolution of system reliability depends on its structure as well as on the evolution of its components reliability. The latter is a function of component age during a system's operating life. Component aging is strongly affected by maintenance activities performed on the system. In this work, we consider two categories of maintenance activities: corrective maintenance (CM) and preventive maintenance (PM). Maintenance actions are characterized by their ability to reduce this age. PM consists of actions applied on components while they are operating, whereas CM actions occur when the component breaks down. In this paper, we expound a new method to integrate the effect of CM while planning for the PM policy. The proportional hazard function was used as a modeling tool for that purpose. Interesting results were obtained when comparison between policies that take into consideration the CM effect and those that do not is established.

Suggested Citation

  • Samrout, M. & Châtelet, E. & Kouta, R. & Chebbo, N., 2009. "Optimization of maintenance policy using the proportional hazard model," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 44-52.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:1:p:44-52
    DOI: 10.1016/j.ress.2007.12.006
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    Cited by:

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    6. Liu, Bin & Xu, Zhengguo & Xie, Min & Kuo, Way, 2014. "A value-based preventive maintenance policy for multi-component system with continuously degrading components," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 83-89.
    7. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    8. Kosztyán, Zsolt T. & Pribojszki-Németh, Anikó & Szalkai, István, 2019. "Hybrid multimode resource-constrained maintenance project scheduling problem," Operations Research Perspectives, Elsevier, vol. 6(C).
    9. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2019. "Reliability-based measures and prognostic analysis of a K-out-of-N system in a random environment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1120-1131.
    10. Briš, Radim & Byczanski, Petr, 2013. "Effective computing algorithm for maintenance optimization of highly reliable systems," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 77-85.
    11. Bobrowski, Sebastian & Chen, Hong & Döring, Maik & Jensen, Uwe & Schinköthe, Wolfgang, 2015. "Estimation of the lifetime distribution of mechatronic systems in the presence of a covariate: A comparison among parametric, semiparametric and nonparametric models," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 105-112.
    12. Shuyuan Gan & Bolun Wang & Zhifang Song, 2021. "A Combined Maintenance Strategy Considering Spares, Buffer, and Quality," Journal of Risk and Reliability, , vol. 235(3), pages 431-445, June.
    13. XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.
    14. Briš, Radim & Byczanski, Petr & Goňo, Radomír & Rusek, Stanislav, 2017. "Discrete maintenance optimization of complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 80-89.
    15. P-E Labeau & M-C Segovia, 2011. "Effective age models for imperfect maintenance," Journal of Risk and Reliability, , vol. 225(2), pages 117-130, June.
    16. Chrianna I Bharat & Kevin Murray & Edward Cripps & Melinda R Hodkiewicz, 2018. "Methods for displaying and calibration of Cox proportional hazards models," Journal of Risk and Reliability, , vol. 232(1), pages 105-115, February.

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