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An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties

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  • He, Jingjing
  • Huang, Min
  • Wang, Wei
  • Wang, Shaohua
  • Guan, Xuefei

Abstract

An asymptotic stochastic response surface method for structural reliability assessment under multi-source heterogeneous uncertainties is proposed in this study. The fatigue reliability of an aeroengine Curvic coupling under the uncertainties from material, contact surface state, and part dimension deviation is used to motivate the methodology development. A randomized block design of experiments for numerical models are employed to obtain the fatigue strain responses under different variations and combinations of the uncertain variables. A stochastic response surface model is constructed, and the analytical forms of the mean and bound predictions of the fatigue strain are derived. By further coupling the fatigue strain into the low-cycle fatigue model, the fatigue life under multi-source uncertainties is obtained. The stochastic moment approximation is used to derive the asymptotic approximation of the fatigue life distribution, allowing for efficient reliability and risk-informed lifetime prediction. The variance of the fatigue life contributed by individual uncertain sources is naturally resolved in the method, providing a quantitative measure for uncertainty control and management. The adequacy of the stochastic response surface model and the accuracy of the asymptotic reliability assessment result are verified using statistical testing and the Monte Carlo method, respectively.

Suggested Citation

  • He, Jingjing & Huang, Min & Wang, Wei & Wang, Shaohua & Guan, Xuefei, 2021. "An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003276
    DOI: 10.1016/j.ress.2021.107804
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    References listed on IDEAS

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    1. Khakifirooz, Marzieh & Fathi, Michel & Lee, I-Chen & Tseng, Sheng-Tsaing, 2023. "Neural ordinary differential equation for sequential optimal design of fatigue test under accelerated life test analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
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    3. Gassab, Adel & Sghaier, Rabi Ben & Fathallah, Raouf, 2023. "Fatigue reliability prediction of shape memory alloy parts based on multi-scale high cycle fatigue criterion," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Jia-Qi, Liu & Yun-Wen, Feng & Da, Teng & Jun-Yu, Chen & Cheng, Lu, 2023. "Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Yu, Ting & Lu, Zhenzhou & Yun, Wanying, 2023. "An efficient algorithm for analyzing multimode structure system reliability by a new learning function of most reducing average probability of misjudging system state," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Zhang, Zheng & Wang, Pan & Hu, Huanhuan & Li, Lei & Li, Haihe & Yue, Zhufeng, 2022. "Efficient reliability-based design optimization for hydraulic pipeline with adaptive sampling region," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Zhou, Daoqing & Sun, C.P. & Du, Yi-Mu & Guan, Xuefei, 2022. "Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. Guan, Xuefei, 2024. "Sparse moment quadrature for uncertainty modeling and quantification," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. 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).

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