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A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties

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  • Chang, Qi
  • Zhou, Changcong
  • Wei, Pengfei
  • Zhang, Yishang
  • Yue, Zhufeng

Abstract

This paper proposes a new non-probabilistic time-dependent reliability model for evaluating the kinematic reliability of mechanisms when the input uncertainties are characterized by intervals. Based on the introduction of the non-probabilistic interval process of motion error, the most probable point of an outcrossing is defined to transform the complicated time-dependent problem into a concisely time-independent problem. A non-probabilistic time-dependent reliability index is proposed to evaluate the kinematic reliability of various mechanisms, and two computational strategies are designed to calculate the index. Then, the proposed reliability model is applied to a numerical example, a typical four-bar linkage mechanism and a car rack-and-pinion steering linkage mechanism, to demonstrate the significance of the proposed reliability model. The results show that the proposed model provides an effective tool for making reliability evaluation of time-dependent problems.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021002982
    DOI: 10.1016/j.ress.2021.107771
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    References listed on IDEAS

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    1. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Zhang, Xiurong & Wang, Li, 2017. "Estimation of the failure probability of an integrated energy system based on the first order reliability method," Energy, Elsevier, vol. 134(C), pages 1068-1078.
    2. Nicola Pedroni & Enrico Zio, 2013. "Uncertainty Analysis in Fault Tree Models with Dependent Basic Events," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1146-1173, June.
    3. 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.
    4. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Nannapaneni, Saideep & Mahadevan, Sankaran, 2016. "Reliability analysis under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 9-20.
    6. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    7. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    Cited by:

    1. Zhang, Ruijing & Dai, Hongzhe, 2022. "A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Hong, Linxiong & Li, Huacong & Fu, Jiangfeng & Li, Jia & Peng, Kai, 2022. "Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Xiao, Tianli & Park, Chanseok & Lin, Chenglong & Ouyang, Linhan & Ma, Yizhong, 2023. "Hybrid reliability analysis with incomplete interval data based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. 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).
    5. 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).

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