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Time-dependent reliability sensitivity analysis of motion mechanisms

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  • Wei, Pengfei
  • Song, Jingwen
  • Lu, Zhenzhou
  • Yue, Zhufeng

Abstract

Reliability sensitivity analysis aims at identifying the source of structure/mechanism failure, and quantifying the effects of each random source or their distribution parameters on failure probability or reliability. In this paper, the time-dependent parametric reliability sensitivity (PRS) analysis as well as the global reliability sensitivity (GRS) analysis is introduced for the motion mechanisms. The PRS indices are defined as the partial derivatives of the time-dependent reliability w.r.t. the distribution parameters of each random input variable, and they quantify the effect of the small change of each distribution parameter on the time-dependent reliability. The GRS indices are defined for quantifying the individual, interaction and total contributions of the uncertainty in each random input variable to the time-dependent reliability. The envelope function method combined with the first order approximation of the motion error function is introduced for efficiently estimating the time-dependent PRS and GRS indices. Both the time-dependent PRS and GRS analysis techniques can be especially useful for reliability-based design. This significance of the proposed methods as well as the effectiveness of the envelope function method for estimating the time-dependent PRS and GRS indices are demonstrated with a four-bar mechanism and a car rack-and-pinion steering linkage.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:149:y:2016:i:c:p:107-120
    DOI: 10.1016/j.ress.2015.12.019
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    References listed on IDEAS

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    1. Pengfei Wei & Jingwen Song & Zhenzhou Lu, 2016. "Global reliability sensitivity analysis of motion mechanisms," Journal of Risk and Reliability, , vol. 230(3), pages 265-277, June.
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    9. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
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    Cited by:

    1. Zhang, Qian & Pan, Ning & Meloni, Marco & Lu, Dong & Cai, Jianguo & Feng, Jian, 2021. "Reliability analysis of radially retractable roofs with revolute joint clearances," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. 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.
    3. Chang, Qi & Zhou, Changcong & Wei, Pengfei & Zhang, Yishang & Yue, Zhufeng, 2021. "A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Pengfei Wei & Chenghu Tang & Yuting Yang, 2019. "Structural reliability and reliability sensitivity analysis of extremely rare failure events by combining sampling and surrogate model methods," Journal of Risk and Reliability, , vol. 233(6), pages 943-957, December.
    5. Yang, Bin & Yang, Wenyu, 2023. "Modular approach to kinematic reliability analysis of industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. 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.

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