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A single-loop reliability analysis strategy for time-dependent problems with small failure probability

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  • Cao, Runan
  • Sun, Zhili
  • Wang, Jian
  • Guo, Fanyi

Abstract

In recent years, many time-dependent reliability methods have been proposed. However, these kinds of methods are barely applied in the field of small failure probability events. This paper proposes an efficient time-dependent reliability method based on the Kriging model and the importance sampling (IS) method. The new method is a single-loop strategy, which can obtain the failure probability varying with time. For complex reliability problems, the failure region usually includes multiple sub-regions, which is challenging to deal with though the traditional IS methods (such as the first-order reliability method-based IS method (FORM-IS) and the kernel-density-estimation-based IS method (KDE-IS)). In this paper, we improve the KDE-IS method to ensure that the sample points cover all the failure sub-regions as far as possible. Aiming at selecting a sample point that can improve the accuracy of failure probability effectively to refresh the Kriging model, this paper proposes a new criterion for selecting the best training point. For accuracy, a new stopping criterion is also defined. Finally, the efficiency and accuracy of the new method are verified by four examples.

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  • Cao, Runan & Sun, Zhili & Wang, Jian & Guo, Fanyi, 2022. "A single-loop reliability analysis strategy for time-dependent problems with small failure probability," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021007080
    DOI: 10.1016/j.ress.2021.108230
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    References listed on IDEAS

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    1. Ling, Chunyan & Lu, Zhenzhou & Zhu, Xianming, 2019. "Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 23-35.
    2. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Roubos, Alfred A. & Allaix, Diego L. & Schweckendiek, Timo & Steenbergen, Raphael D.J.M. & Jonkman, Sebastiaan N., 2020. "Time-dependent reliability analysis of service-proven quay walls subject to corrosion-induced degradation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    5. Sudret, B. & Mai, C.V., 2015. "Computing derivative-based global sensitivity measures using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 241-250.
    6. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    7. Hawchar, Lara & El Soueidy, Charbel-Pierre & Schoefs, Franck, 2017. "Principal component analysis and polynomial chaos expansion for time-variant reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 406-416.
    8. Fauriat, W. & Gayton, N., 2014. "AK-SYS: An adaptation of the AK-MCS method for system reliability," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 137-144.
    9. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
    10. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    11. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.
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    Cited by:

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    2. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2024. "A Subdomain uncertainty-guided Kriging method with optimized feasibility metric for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Ouyang, Linhan & Che, Yushuai & Park, Chanseok & Chen, Yuejian, 2024. "A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Zhao, Zhao & Lu, Zhao-Hui & Zhang, Xuan-Yi & Zhao, Yan-Gang, 2022. "A nested single-loop Kriging model coupled with subset simulation for time-dependent system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
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    6. Zhang, Yang & Xu, Jun & Beer, Michael, 2023. "A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Zhao, Zhao & Zhao, Yan-Gang & Li, Pei-Pei, 2023. "A novel decoupled time-variant reliability-based design optimization approach by improved extreme value moment method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Carlon, André Gustavo & Kroetz, Henrique Machado & Torii, André Jacomel & Lopez, Rafael Holdorf & Miguel, Leandro Fleck Fadel, 2022. "Risk optimization using the Chernoff bound and stochastic gradient descent," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    9. Ramadhani, Adhitya & Khan, Faisal & Colbourne, Bruce & Ahmed, Salim & Taleb-Berrouane, Mohammed, 2022. "Resilience assessment of offshore structures subjected to ice load considering complex dependencies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. Zha, Congyi & Pan, Chenrong & Sun, Zhili & Liu, Qin, 2024. "A single-loop reliability sensitivity analysis strategy for time-dependent rare events with both random variables and stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 251(C).

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