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A multivariate CBM model with a random and time-dependent failure threshold

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  • Jiang, R.

Abstract

In a condition-based maintenance setting, the degradation of an item is usually represented by several condition variables, and they can be combined into a composite condition variable. In this case, the functional failure threshold associated with the composite condition variable is usually not a fixed and known constant. It is an open issue to model the failure threshold and accordingly determine a threshold of preventive maintenance (PM). This paper addresses this issue. The condition variables are combined using a weighted power model, the failure threshold is represented by the Gaussian process model, and the PM threshold is determined by two approaches. Based on the gamma process and stress–strength interference models, the distributions of time to failure and to the PM threshold are derived, respectively. The appropriateness of the approach is illustrated by a real-world example.

Suggested Citation

  • Jiang, R., 2013. "A multivariate CBM model with a random and time-dependent failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 178-185.
    • Handle: RePEc:eee:reensy:v:119:y:2013:i:c:p:178-185
    DOI: 10.1016/j.ress.2013.05.023
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    References listed on IDEAS

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    1. Jiang, R. & Jardine, A.K.S., 2006. "Composite scale modeling in the presence of censored data," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 756-764.
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    3. Jiang, R. & Jardine, A.K.S., 2008. "Health state evaluation of an item: A general framework and graphical representation," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 89-99.
    4. Wang, Wenbin, 2012. "A simulation-based multivariate Bayesian control chart for real time condition-based maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 218(3), pages 726-734.
    5. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    6. Jiang, R. & Murthy, D.N.P., 2011. "A study of Weibull shape parameter: Properties and significance," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1619-1626.
    7. Jiang, R., 2010. "Optimization of alarm threshold and sequential inspection scheme," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 208-215.
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    Cited by:

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    2. Caballé, N.C. & Castro, I.T. & Pérez, C.J. & Lanza-Gutiérrez, J.M., 2015. "A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 98-109.
    3. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    4. Li, Ting & He, Shuguang & Zhao, Xiujie, 2022. "Optimal warranty policy design for deteriorating products with random failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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    6. Jiang, R., 2018. "Performance evaluation of seven optimization models of age replacement policy," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 302-311.

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