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Mixed uncertainty analysis for dynamic reliability of mechanical structures considering residual strength

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  • Liu, Xintian
  • Yu, Xueguang
  • Tong, Jiachi
  • Wang, Xu
  • Wang, Xiaolan

Abstract

Aiming at the uncertainty of parameters in structural dynamic reliability analysis model, such as randomness, interval and fuzziness, a dynamic reliability model with mixed uncertain parameters is proposed in this paper. Based on entropy equality information, random and fuzzy variables are transformed. And the different possible uncertainties of parameters are analyzed, four reliability index analysis models are given under different parameters. Probability and interval reliability analysis are adopted to solve the reliability index, and the influence of parameter type on reliability index is discussed. The dynamic reliability index of 45# steel (test specimens) under fatigue load is calculated to verify the feasibility of the proposed method.

Suggested Citation

  • Liu, Xintian & Yu, Xueguang & Tong, Jiachi & Wang, Xu & Wang, Xiaolan, 2021. "Mixed uncertainty analysis for dynamic reliability of mechanical structures considering residual strength," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000405
    DOI: 10.1016/j.ress.2021.107472
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    References listed on IDEAS

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    1. Olatubosun, Samuel Abiodun & Zhang, Zhijian, 2019. "Dependency consideration of passive system reliability by coupled stress-strength interference/functional relations of parameters approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 549-560.
    2. 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.
    3. Liu, Xintian & Mao, Kui & Wang, Xiaolan & Wang, Xu & Wang, Yansong, 2020. "A modified quality loss model of service life prediction for products via wear regularity," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
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    Cited by:

    1. Wang, Haijie & Li, Bo & Lei, Liming & Xuan, Fuzhen, 2024. "Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Ebrahimi, Mehrdad & Nobahar, Elnaz & Mohammadi, Reza Karami & Noroozinejad Farsangi, Ehsan & Noori, Mohammad & Li, Shaofan, 2023. "The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Pei, Liang & Chen, Chen & He, Kun & Lu, Xiang, 2022. "System reliability of a gravity dam-foundation system using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    4. Liu, Fuxiu & Li, Zhaojun & Liang, Minglang & Zhao, Binjian & Ding, Jiang, 2023. "Prediction method of non-stationary random vibration fatigue reliability of turbine runner blade based on transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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