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Local reliability based sensitivity analysis with the moving particles method

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  • Proppe, Carsten

Abstract

Local reliability sensitivity methods aim at determining the partial derivatives of the failure probability or the reliability index with respect to model parameters. For efficient local reliability based sensitivity analysis, it is important to avoid repeated evaluations of the performance function. To this end, an extension of the moving particles method to local reliability based sensitivity analysis is presented that is completely based on the already evaluated samples for the reliability estimate and thus avoids repeated evaluations of the performance function. In order to further reduce the variance of the estimator and to increase the efficiency, a multilevel variant of the estimator is proposed. The method is discussed in detail and illustrated by means of examples.

Suggested Citation

  • Proppe, Carsten, 2021. "Local reliability based sensitivity analysis with the moving particles method," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020307675
    DOI: 10.1016/j.ress.2020.107269
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    References listed on IDEAS

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    1. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    2. Michael B. Giles, 2008. "Multilevel Monte Carlo Path Simulation," Operations Research, INFORMS, vol. 56(3), pages 607-617, June.
    3. O. H. Galal, 2013. "A Proposed Stochastic Finite Difference Approach Based on Homogenous Chaos Expansion," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-9, July.
    4. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
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    Cited by:

    1. Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.

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