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System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?

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  • Wang, Fan
  • Li, Heng

Abstract

Many reliability problems involve correlated random variables. However, the probabilistic specification of random variables is commonly given in terms of marginals and correlations, which is actually incomplete because the data dependency needed for distribution modeling is not characterized. The implicitly assumed Gaussian dependence structure is not necessarily true and may bias the reliability result. To investigate the effect of correlations on system reliability under non-Gaussian dependence structures, a general approach to the probability distribution model construction based on the pair-copula decomposition is proposed. Numerical examples have highlighted the importance of dependence modeling in system reliability since large deviation in failure probabilities under different dependencies is observed. The method for identifying the best fit data dependency from data is later provided and illustrated with a retaining wall. It is demonstrated that the reliability result can be accurately estimated if the qualitative dependence structure is complemented to the available quantitative statistical information.

Suggested Citation

  • Wang, Fan & Li, Heng, 2018. "System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 94-104.
    • Handle: RePEc:eee:reensy:v:173:y:2018:i:c:p:94-104
    DOI: 10.1016/j.ress.2017.12.018
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    References listed on IDEAS

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    Cited by:

    1. Sun, Fuqiang & Fu, Fangyou & Liao, Haitao & Xu, Dan, 2020. "Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Wang, Fan & Li, Heng & Dong, Chao, 2021. "Understanding near-miss count data on construction sites using greedy D-vine copula marginal regression," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Zhang, Yi & Gomes, António Topa & Beer, Michael & Neumann, Ingo & Nackenhorst, Udo & Kim, Chul-Woo, 2019. "Reliability analysis with consideration of asymmetrically dependent variables: Discussion and application to geotechnical examples," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 261-277.
    4. Tong, Ming-Na & Zhao, Yan-Gang & Lu, Zhao-Hui, 2021. "Normal transformation for correlated random variables based on L-moments and its application in reliability engineering," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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