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Parallel adaptive ensemble of metamodels combined with hypersphere sampling for rare failure events

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Listed:
  • Xin, Fukang
  • Wang, Pan
  • Wang, Qirui
  • Li, Lei
  • Cheng, Lei
  • Lei, Huajin
  • Ma, Fangyun

Abstract

In practical engineering, especially in aeronautical engineering, the failure probability is extremely rare due to the incorporation of safety factors in the mechanical design phase. Consequently, a significant challenge is to assess the reliability of mechanical products with implicit functions and rare failure events. To address this issue, this work presents a parallel adaptive ensemble of metamodels (EM) coupled with hypersphere sampling strategy to improve the accuracy and efficiency of reliability analysis. The proposed method consists of three main features. First, a new heuristic ensemble strategy is proposed to provide a powerful and robust metamodel. Second, a n-dimensional uniform sampling technique with better space-filling ability is taken to improve efficiency, which leads to a decrease in the extensive sample size required to capture rare failure events. Third, an effective parallel enrichment strategy is developed by the proposed pseudo-improved U learning function. When parallel computation is possible, the proposed method can select a batch of informative updated points simultaneously to update the EM. Three numerical examples and a planar ten-bar structure are presented to demonstrate the accuracy and efficiency of the proposed method. This method is also applied to the reliability assessment of the aircraft lock mechanism.

Suggested Citation

  • Xin, Fukang & Wang, Pan & Wang, Qirui & Li, Lei & Cheng, Lei & Lei, Huajin & Ma, Fangyun, 2024. "Parallel adaptive ensemble of metamodels combined with hypersphere sampling for rare failure events," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
  • Handle: RePEc:eee:reensy:v:246:y:2024:i:c:s0951832024001649
    DOI: 10.1016/j.ress.2024.110090
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    1. J P C Kleijnen & W C M van Beers, 2004. "Application-driven sequential designs for simulation experiments: Kriging metamodelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 876-883, August.
    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. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Song, Kunling & Zhang, Yugang & Shen, Linjie & Zhao, Qingyan & Song, Bifeng, 2021. "A failure boundary exploration and exploitation framework combining adaptive Kriging model and sample space partitioning strategy for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2018. "Time-variant fatigue reliability assessment of welded joints based on the PHI2 and response surface methods," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 120-130.
    6. Yang Yan & Xiaohong Yu & Naeem Jan, 2021. "Probability Density Evolution Algorithm for Stochastic Dynamical Systems Based on Fractional Calculus," Journal of Mathematics, Hindawi, vol. 2021, pages 1-9, November.
    7. Zhou, Changcong & Zhang, Hanlin & Valdebenito, Marcos A. & Zhao, Haodong, 2022. "A general hierarchical ensemble-learning framework for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. 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.
    9. Dang, Chao & Wei, Pengfei & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2022. "Parallel adaptive Bayesian quadrature for rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    10. Dobric, Jadran & Schmid, Friedrich, 2007. "A goodness of fit test for copulas based on Rosenblatt's transformation," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4633-4642, May.
    11. Zhan, Hongyou & Xiao, Ning-Cong & Ji, Yuxiang, 2022. "An adaptive parallel learning dependent Kriging model for small failure probability problems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Zhou, Tong & Peng, Yongbo, 2022. "Ensemble of metamodels-assisted probability density evolution method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    13. Zhu, Xianming & Lu, Zhenzhou & Yun, Wanying, 2020. "An efficient method for estimating failure probability of the structure with multiple implicit failure domains by combining Meta-IS with IS-AK," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    14. 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.
    15. Shi, Yan & Lu, Zhenzhou & He, Ruyang & Zhou, Yicheng & Chen, Siyu, 2020. "A novel learning function based on Kriging for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    16. Zhou, Tong & Peng, Yongbo, 2020. "Adaptive Bayesian quadrature based statistical moments estimation for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    17. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    18. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Pseudo expected improvement criterion for parallel EGO algorithm," Journal of Global Optimization, Springer, vol. 68(3), pages 641-662, July.
    19. Xiao, Ning-Cong & Zuo, Ming J. & Zhou, Chengning, 2018. "A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 330-338.
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