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Kernel estimator of maintenance optimization model for a stochastically degrading system under different operating environments

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  • Sidibé, I.B.
  • Khatab, A.
  • Diallo, C.
  • Adjallah, K.H.

Abstract

This paper investigates the preventive age replacement policy (ARP) for a system subject to random failures. Unlike most maintenance models in the literature, our model considers a system that is exploited under different operating environments each characterized by its own degree of severity. The system lifetimes follow a different distribution depending on the environment it is operating under. Furthermore, the system lifetimes distribution is assumed unknown and therefore estimated from field reliability data. The reliability of the system is calculated using two kernel estimators. This method offers the advantage of non-parametric estimation methods and completely determined by two parameters, namely the smoothing parameter and the kernel function. First, a probability maintenance cost model is derived and conditions under which an optimal preventive maintenance age exists are provided. Then, a statistical maintenance cost model is developed using two kernel estimators. The impact of the variability of the kernel smoothing parameter on the cost model is also investigated. Numerical experiments are provided to illustrate the proposed approach. Results obtained demonstrate the accuracy of the proposed statistical maintenance cost model.

Suggested Citation

  • Sidibé, I.B. & Khatab, A. & Diallo, C. & Adjallah, K.H., 2016. "Kernel estimator of maintenance optimization model for a stochastically degrading system under different operating environments," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 109-116.
    • Handle: RePEc:eee:reensy:v:147:y:2016:i:c:p:109-116
    DOI: 10.1016/j.ress.2015.11.001
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    References listed on IDEAS

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    1. Finkelstein, Maxim, 2009. "Virtual age of non-repairable objects," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 666-669.
    2. Nakagawa, T. & Mizutani, S., 2009. "A summary of maintenance policies for a finite interval," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 89-96.
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    5. Toshio Nakagawa, 2008. "Advanced Reliability Models and Maintenance Policies," Springer Series in Reliability Engineering, Springer, number 978-1-84800-294-4, March.
    6. Jones, M. C. & Sheather, S. J., 1991. "Using non-stochastic terms to advantage in kernel-based estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 11(6), pages 511-514, June.
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    Cited by:

    1. Sidibe, I.B. & Khatab, A. & Diallo, C. & Kassambara, A., 2017. "Preventive maintenance optimization for a stochastically degrading system with a random initial age," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 255-263.
    2. Xia, Tangbin & Xi, Lifeng & Pan, Ershun & Ni, Jun, 2017. "Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 87-98.
    3. Carpitella, Silvia & Certa, Antonella & Izquierdo, Joaquín & La Fata, Concetta Manuela, 2018. "A combined multi-criteria approach to support FMECA analyses: A real-world case," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 394-402.
    4. Gan, Shuyuan & Hu, Hengheng & Coit, David W., 2023. "Maintenance optimization considering the mutual dependence of the environment and system with decreasing effects of imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.

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