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Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

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  • Yan Shi
  • Zhenzhou Lu
  • Ruyang He

Abstract

Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

Suggested Citation

  • Yan Shi & Zhenzhou Lu & Ruyang He, 2020. "Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model," Journal of Risk and Reliability, , vol. 234(4), pages 588-600, August.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:4:p:588-600
    DOI: 10.1177/1748006X20901981
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    References listed on IDEAS

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    1. Wang, Zequn & Chen, Wei, 2016. "Time-variant reliability assessment through equivalent stochastic process transformation," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 166-175.
    2. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
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    Cited by:

    1. Guo, Hongyuan & Zhang, Jiaxin & Dong, You & Frangopol, Dan M., 2024. "Probability-informed neural network-driven point-evolution kernel density estimation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    2. Jian Wang & Xiang Gao & Zhili Sun, 2021. "An Importance Sampling Framework for Time-Variant Reliability Analysis Involving Stochastic Processes," Sustainability, MDPI, vol. 13(14), pages 1-16, July.

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