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An efficient and robust Kriging-based method for system reliability analysis

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

Abstract

System reliability analysis involving multiple failure modes is challenging when performance functions are associated with time-consuming codes. This paper aims to enhance the efficiency of system reliability analysis by reducing the number of evaluations of time-consuming models. To achieve that, an adaptive Kriging-based method is proposed. In order to develop the method, a quantificational error measure of Kriging models (i.e. surrogate models of performance functions associated with each failure mode) is first derived. The stepwise accuracy-improvement strategy (SAIS) is then modified to solve system reliability problems, and the modified SAIS is called SAIS-SYS. The method for system reliability analysis is finally developed based on the derived error measure and SAIS-SYS. In the proposed method, Kriging models, i.e. the surrogate models of original performance functions, are adaptively refreshed according to SAIS-SYS until the associated error measure is smaller than a prescribed threshold. After Kriging models meet with accuracy requirement, the system failure probability can be obtained through a random simulation method and no additional evaluations of original performance functions is needed. The accuracy, efficiency and robustness of the proposed method are validated by four examples.

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  • Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s095183202100466x
    DOI: 10.1016/j.ress.2021.107953
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    References listed on IDEAS

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    1. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    2. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    3. Wang, Zeyu & Shafieezadeh, Abdollah, 2019. "REAK: Reliability analysis through Error rate-based Adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 33-45.
    4. Yuan, Kai & Xiao, Ning-Cong & Wang, Zhonglai & Shang, Kun, 2020. "System reliability analysis by combining structure function and active learning kriging model," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    6. Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2016. "The structure function for system reliability as predictive (imprecise) probability," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 180-187.
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    9. Jian, Wang & Zhili, Sun & Qiang, Yang & Rui, Li, 2017. "Two accuracy measures of the Kriging model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 494-505.
    10. 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).
    11. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    12. Yang, Xufeng & Liu, Yongshou & Mi, Caiying & Tang, Chenghu, 2018. "System reliability analysis through active learning Kriging model with truncated candidate region," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 235-241.
    13. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
    14. Sadoughi, Mohammadkazem & Li, Meng & Hu, Chao, 2018. "Multivariate system reliability analysis considering highly nonlinear and dependent safety events," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 189-200.
    15. Zhou, Yicheng & Lu, Zhenzhou & Yun, Wanying, 2020. "Active sparse polynomial chaos expansion for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    16. 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.
    17. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
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