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Sequential time-dependent reliability analysis for the lower extremity exoskeleton under uncertainty

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  • Yu, Shui
  • Wang, Zhonglai
  • Zhang, Kewang

Abstract

This paper proposes a sequential time-dependent reliability analysis method by considering time sequence and correlation of failure processes for the lower extremity exoskeleton under uncertainty, which will provide an approach to improving the comfort and safety for the wearer. A kernel density function based uncertainty quantification method is provided for precisely quantitatively estimating the time-dependent reliability of joints and the position of the end-effector firstly. After decoupling time sequence and failures correlation due to error propagation, the original reliability problem is then transferred to a series time-dependent reliability model. The time-dependent system reliability analysis is finally realized by calculating conditional probability. A case study is implemented to testify the effectiveness of the proposed method.

Suggested Citation

  • Yu, Shui & Wang, Zhonglai & Zhang, Kewang, 2018. "Sequential time-dependent reliability analysis for the lower extremity exoskeleton under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 45-52.
    • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:45-52
    DOI: 10.1016/j.ress.2017.10.006
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    References listed on IDEAS

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    1. Wu, Xianguo & Liu, Huitao & Zhang, Limao & Skibniewski, Miroslaw J. & Deng, Qianli & Teng, Jiaying, 2015. "A dynamic Bayesian network based approach to safety decision support in tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 157-168.
    2. Lisnianski, Anatoly, 2007. "Extended block diagram method for a multi-state system reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1601-1607.
    3. Ge, Daochuan & Lin, Meng & Yang, Yanhua & Zhang, Ruoxing & Chou, Qiang, 2015. "Quantitative analysis of dynamic fault trees using improved Sequential Binary Decision Diagrams," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 289-299.
    4. Signoret, Jean-Pierre & Dutuit, Yves & Cacheux, Pierre-Joseph & Folleau, Cyrille & Collas, Stéphane & Thomas, Philippe, 2013. "Make your Petri nets understandable: Reliability block diagrams driven Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 61-75.
    5. Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
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    Cited by:

    1. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. Chengning Zhou & Ning-Cong Xiao & Ming J Zuo & Xiaoxu Huang, 2020. "AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis," Journal of Risk and Reliability, , vol. 234(3), pages 536-549, June.
    3. 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).
    4. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    5. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    6. Hu, Yingshi & Lu, Zhenzhou & Jiang, Xia & Wei, Ning & Zhou, Changcong, 2021. "Time-dependent structural system reliability analysis model and its efficiency solution," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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