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Composite scale modeling in the presence of censored data

Author

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  • Jiang, R.
  • Jardine, A.K.S.

Abstract

A composite scale modeling approach can be used to combine several scales or variables into a single scale or variable. A typical application is to combine age and usage together to form a composite timescale model. The combined scale is expected to have better failure prediction capability than individual scales. Two typical models are the linear and multiplicative models. Their parameters are determined by minimizing the sample coefficient of variation of the composite scale. The minimum coefficient of variation is hard to apply in the presence of censored data. Another open issue is how to identify key variables when a number of variables are combined. This paper develops methods to handle these two issues. A numerical example is also included to illustrate the proposed methods.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:7:p:756-764
    DOI: 10.1016/j.ress.2005.07.001
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    References listed on IDEAS

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    1. Gertsbakh, I. & Kordonsky, Kh. B., 1997. "Choice of the best time scale for preventive maintenance in heterogeneous environments," European Journal of Operational Research, Elsevier, vol. 98(1), pages 64-74, April.
    2. 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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    Cited by:

    1. Diaz, Nicole & Pascual, Rodrigo & Ruggeri, Fabrizio & López Droguett, Enrique, 2017. "Modelling age replacement policy under multiple time scales and stochastic usage profiles," International Journal of Production Economics, Elsevier, vol. 188(C), pages 22-28.
    2. Jiang, R., 2010. "Optimization of alarm threshold and sequential inspection scheme," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 208-215.
    3. 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.
    4. 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.

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