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Dynamic Reliability Assessment of Multi-cracked Structure under Fatigue Loading via Multi-State Physics Model

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  • Jiang, Shan
  • Li, Yan-Fu

Abstract

The structure strength abruptly decreases due to the propagation of multiple cracks. The crack interaction renders the multiple crack reliability problem being a challenging one. Most of the existing methods cannot fit the whole multiple crack growth process and quantify the crack linkup effects on reliability estimation. To address this problem, we extend the multi-state physics modelling and computation framework to evaluate the dynamic reliability of multi-cracked structure via implementing piecewise deterministic Markov processes (PDMP). The proposed multi-state physics model (MSPM) incorporates the crack propagation state transitions and the Forman model that describes the crack growth rate within the states. For the former, the probability of the linkup of adjacent cracks is calculated to identify the multiple crack systems and define the discrete states of each system. For the latter, the Forman model is employed to calculate the crack growth rate within the states. Therefore, each multiple crack system in the structure can be quantified by PDMP and a Monte Carlo algorithm is presented to evaluate the corresponding dynamic reliability values. Ultimately, the dynamic reliability formulation of the multi-cracked structure is developed. The accuracy of the proposed approach is verified by using the sample data from the literature and simulation example.

Suggested Citation

  • Jiang, Shan & Li, Yan-Fu, 2021. "Dynamic Reliability Assessment of Multi-cracked Structure under Fatigue Loading via Multi-State Physics Model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002052
    DOI: 10.1016/j.ress.2021.107664
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Weikai & Chen, Xian, 2023. "Piecewise deterministic Markov process for condition-based imperfect maintenance models," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Gassab, Adel & Sghaier, Rabi Ben & Fathallah, Raouf, 2023. "Fatigue reliability prediction of shape memory alloy parts based on multi-scale high cycle fatigue criterion," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Kim, Wongon & Lee, Guesuk & Son, Hyejeong & Choi, Hyunhee & Youn, Byeng D., 2022. "Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. 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).
    6. Lee, Dooyoul & Kwon, Kybeom, 2023. "Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Sun, Tianqi & Vatn, Jørn, 2024. "A phase-type maintenance model considering condition-based inspections and maintenance delays," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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