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A new approach for reliability analysis with time-variant performance characteristics

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  • Wang, Zequn
  • Wang, Pingfeng

Abstract

Reliability represents safety level in industry practice and may variant due to time-variant operation condition and components deterioration throughout a product life-cycle. Thus, the capability to perform time-variant reliability analysis is of vital importance in practical engineering applications. This paper presents a new approach, referred to as nested extreme response surface (NERS), that can efficiently tackle time dependency issue in time-variant reliability analysis and enable to solve such problem by easily integrating with advanced time-independent tools. The key of the NERS approach is to build a nested response surface of time corresponding to the extreme value of the limit state function by employing Kriging model. To obtain the data for the Kriging model, the efficient global optimization technique is integrated with the NERS to extract the extreme time responses of the limit state function for any given system input. An adaptive response prediction and model maturation mechanism is developed based on mean square error (MSE) to concurrently improve the accuracy and computational efficiency of the proposed approach. With the nested response surface of time, the time-variant reliability analysis can be converted into the time-independent reliability analysis and existing advanced reliability analysis methods can be used. Three case studies are used to demonstrate the efficiency and accuracy of NERS approach.

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  • Wang, Zequn & Wang, Pingfeng, 2013. "A new approach for reliability analysis with time-variant performance characteristics," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 70-81.
    • Handle: RePEc:eee:reensy:v:115:y:2013:i:c:p:70-81
    DOI: 10.1016/j.ress.2013.02.017
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    Cited by:

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    3. 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.
    4. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2020. "Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    6. Zhang, Xuan-Yi & Lu, Zhao-Hui & Wu, Shi-Yu & Zhao, Yan-Gang, 2021. "An Efficient Method for Time-Variant Reliability including Finite Element Analysis," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Xie, Bin & Wang, Yanzhong & Zhu, Yunyi & Liu, Peng & Wu, Yu & Lu, Fengxia, 2024. "Time-variant reliability analysis of angular contact ball bearing considering the coupled effect of rolling contact fatigue damage and wear," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Mattrand, C. & Bourinet, J.-M., 2014. "The cross-entropy method for reliability assessment of cracked structures subjected to random Markovian loads," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 171-182.
    10. Wei, Pengfei & Song, Jingwen & Lu, Zhenzhou & Yue, Zhufeng, 2016. "Time-dependent reliability sensitivity analysis of motion mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 107-120.
    11. Wenxuan Wang & Hangshan Gao & Pengfei Wei & Changcong Zhou, 2017. "Extending first-passage method to reliability sensitivity analysis of motion mechanisms," Journal of Risk and Reliability, , vol. 231(5), pages 573-586, October.
    12. Li, Mingyang & Wang, Zequn, 2022. "LSTM-augmented deep networks for time-variant reliability assessment of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    13. Cheng, Kai & Lu, Zhenzhou, 2019. "Time-variant reliability analysis based on high dimensional model representation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 310-319.
    14. Zhang, Kun & Chen, Ning & Zeng, Peng & Liu, Jian & Beer, Michael, 2022. "An efficient reliability analysis method for structures with hybrid time-dependent uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    15. XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.
    16. 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.
    17. Rougé, Charles & Mathias, Jean-Denis & Deffuant, Guillaume, 2014. "Relevance of control theory to design and maintenance problems in time-variant reliability: The case of stochastic viability," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 250-260.
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    19. Zhou, Di & Pan, Ershun & Zhang, Xufang & Zhang, Yimin, 2020. "Dynamic Model-based Saddle-point Approximation for Reliability and Reliability-based Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).

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