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System repairs: When to perform and what to do?

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  • Lugtigheid, Diederik
  • Banjevic, Dragan
  • Jardine, Andrew K.S.

Abstract

In this paper an indicator is used that reflects the “state†of a system as a function of the ages of the (groups of) subsystems of which it consists. The contribution of the ages of the subsystems to the state of the system is defined by their weights. The indicator can be interpreted as the virtual age of the system, and can therefore be used to define age-reduction factors of different types of repair in a virtual age or age-reduction process. The state indicator is used as the time scale in a proportional intensity model. In this way, the joint impact of different repair strategies and covariates on the system failure intensity can be evaluated. This relationship is then used to address the question of which subsystems to replace whenever a system comes in for repair and when to set the preventive inspection/repair interval, in order to minimize the expected costs per unit time until the next inspection and/or repair. A numerical and a practical example are given.

Suggested Citation

  • Lugtigheid, Diederik & Banjevic, Dragan & Jardine, Andrew K.S., 2008. "System repairs: When to perform and what to do?," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 604-615.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:4:p:604-615
    DOI: 10.1016/j.ress.2007.03.023
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    References listed on IDEAS

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    1. P J Vlok & J L Coetzee & D Banjevic & A K S Jardine & V Makis, 2002. "Optimal component replacement decisions using vibration monitoring and the proportional-hazards model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 193-202, February.
    2. Rommert Dekker & Ralph Wildeman & Frank Duyn Schouten, 1997. "A review of multi-component maintenance models with economic dependence," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 45(3), pages 411-435, October.
    3. Kordonsky, Kh. B. & Gertsbakh, I. B., 1993. "Choice of the best time scale for system reliability analysis," European Journal of Operational Research, Elsevier, vol. 65(2), pages 235-246, March.
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    Citations

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    Cited by:

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    2. Lam, Ji Ye Janet & Banjevic, Dragan, 2015. "A myopic policy for optimal inspection scheduling for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 1-11.
    3. Balakrishnan, N. & Kamps, U. & Kateri, M., 2009. "Minimal repair under a step-stress test," Statistics & Probability Letters, Elsevier, vol. 79(13), pages 1548-1558, July.
    4. Lingxiao Wang & Barry I. Graubard & Hormuzd A. Katki & Yan Li, 2022. "Efficient and robust propensity‐score‐based methods for population inference using epidemiologic cohorts," International Statistical Review, International Statistical Institute, vol. 90(1), pages 146-164, April.
    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.
    6. Tanwar, Monika & Rai, Rajiv N. & Bolia, Nomesh, 2014. "Imperfect repair modeling using Kijima type generalized renewal process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 24-31.
    7. Moura, Márcio das Chagas & Santana, João Mateus & Droguett, Enrique López & Lins, Isis Didier & Guedes, Bruno Nunes, 2017. "Analysis of extended warranties for medical equipment: A Stackelberg game model using priority queues," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 338-354.
    8. Tian, Zhigang & Liao, Haitao, 2011. "Condition based maintenance optimization for multi-component systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 581-589.

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