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Uncertainty analysis in degradation modeling for maintenance policy assessment

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  • Piero Baraldi
  • Michele Compare
  • Enrico Zio

Abstract

We consider the problem of the evaluation of the maintenance policy of a component by means of degradation modeling. We assume that the stochastic laws governing the degradation process are uncertain, and so are the related parameters. We assume that the information available is in the form of qualitative judgment by an expert. We develop a representation framework based on possibility theory and the concept of fuzzy random variables. An example of application is given with reference to a medium-voltage circuit-breaker test facility.

Suggested Citation

  • Piero Baraldi & Michele Compare & Enrico Zio, 2013. "Uncertainty analysis in degradation modeling for maintenance policy assessment," Journal of Risk and Reliability, , vol. 227(3), pages 267-278, June.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:3:p:267-278
    DOI: 10.1177/1748006X13488479
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    References listed on IDEAS

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    1. Dubois, Didier, 2006. "Possibility theory and statistical reasoning," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 47-69, November.
    2. Piero Baraldi & Enrico Zio, 2010. "A Comparison Between Probabilistic and Dempster‐Shafer Theory Approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories," Risk Analysis, John Wiley & Sons, vol. 30(7), pages 1139-1156, July.
    3. Shapiro, Arnold F., 2009. "Fuzzy random variables," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 307-314, April.
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    Cited by:

    1. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & Mc Donnell, D., 2017. "Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 25-40.

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