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Extending first-passage method to reliability sensitivity analysis of motion mechanisms

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  • Wenxuan Wang
  • Hangshan Gao
  • Pengfei Wei
  • Changcong Zhou

Abstract

Identifying the parameters that substantially affect the time-dependent reliability is critical for reliability-based design of motion mechanism. The time-dependent local reliability sensitivity and global reliability sensitivity are the two effective techniques for this type of analysis. This work extends the first-passage method and PHI2 method, which are commonly used for estimating the time-dependent reliability, for efficiently estimating the time-dependent local reliability sensitivity and global reliability sensitivity indices of the motion mechanism. Both the local reliability sensitivity and global reliability sensitivity indices are analytically derived based on the Poisson assumption–based first-passage method and the first-order Taylor’s expansion of the motion error function. Compared with the current envelope function method for estimating the time-dependent local reliability sensitivity and global reliability sensitivity indices, the developed method does not need to estimate the second-order derivatives of motion error function, thus is more applicable. The accuracy and effectiveness of the proposed method are demonstrated by a numerical example and a satellite antenna, the direction of which is controlled by a four-bar function generator mechanism.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:risrel:v:231:y:2017:i:5:p:573-586
    DOI: 10.1177/1748006X17717614
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    1. Liu, Qiao & Homma, Toshimitsu, 2009. "A new computational method of a moment-independent uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1205-1211.
    2. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    3. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    4. 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.
    5. Song, Shufang & Lu, Zhenzhou & Qiao, Hongwei, 2009. "Subset simulation for structural reliability sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 658-665.
    6. 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.
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