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Multi-polynomial chaos Kriging-based adaptive moving strategy for comprehensive reliability analyses

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  • Teng, Da
  • Feng, Yun-Wen
  • Chen, Jun-Yu
  • Liu, Jia-Qi
  • Lu, Cheng

Abstract

To effectively evaluate the comprehensive reliability of structural systems, the multi-polynomial chaos Kriging-based adaptive moving strategy (AMS-MPCK) is proposed, by integrating the moving least squares (MLS) method, adaptive equilibrium optimizer (AEO) algorithm, polynomial chaos expansions, Kriging model, synchronous modeling thought and linkage sampling technique. In this strategy, the MLS is adopted to select effective samples from training samples for modeling, the developed AEO algorithm is used to obtain the optimal local compact support region radius of MLS, the polynomial chaos expansions are applied to approximate the global behavior, the Kriging model is suited to manage the local variability of output response, the synchronous modeling thought is employed to realize the synchronous construction of multiple models, and the linkage sampling technology is utilized to obtain comprehensive output response values at the same time for improving the efficiency of reliability analysis. The accuracy and efficiency advantages of the proposed AMS-MPCK are verified by the benchmark functions approximation problems, the landing gear brake system temperature, and aeroengine turbine blisk multi-failures. Besides, the developed AMS-MPCK holds excellent modeling and simulation performance by comparing with different methods. The efforts of this study provide valuable insight into the comprehensive reliability analyses of mechanical structure systems.

Suggested Citation

  • Teng, Da & Feng, Yun-Wen & Chen, Jun-Yu & Liu, Jia-Qi & Lu, Cheng, 2024. "Multi-polynomial chaos Kriging-based adaptive moving strategy for comprehensive reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005719
    DOI: 10.1016/j.ress.2023.109657
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    References listed on IDEAS

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    1. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Lu, Cheng & Teng, Da & Chen, Jun-Yu & Fei, Cheng-Wei & Keshtegar, Behrooz, 2023. "Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Zuhal, Lavi Rizki & Faza, Ghifari Adam & Palar, Pramudita Satria & Liem, Rhea Patricia, 2021. "On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Chen, Jun-Yu & Feng, Yun-Wen & Teng, Da & Lu, Cheng & Fei, Cheng-Wei, 2022. "Support vector machine-based similarity selection method for structural transient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Xiao, Sinan & Oladyshkin, Sergey & Nowak, Wolfgang, 2020. "Reliability analysis with stratified importance sampling based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    6. Valdebenito, M.A. & Jensen, H.A. & Hernández, H.B. & Mehrez, L., 2018. "Sensitivity estimation of failure probability applying line sampling," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 99-111.
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    1. Teng, Da & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi & Chen, Jun-Yu, 2024. "Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Zhu, Chun-Yan & Li, Zhen-Ao & Dong, Xiao-Wei & Wang, Ming & Li, Qing-Da, 2024. "Collaborative modeling-based improved moving Kriging approach for low-cycle fatigue life reliability estimation of mechanical structures," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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