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Reliability analysis of bending fatigue life of hydraulic pipeline

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  • Shen, Xingkeng
  • Feng, Kaixuan
  • Xu, Heming
  • Wang, Guangqiang
  • Zhang, Yishang
  • Dai, Ying
  • Yun, Wanying

Abstract

Fatigue failure caused by high pressure of fluid and external vibration seriously affects the performance and reliability of hydraulic pipelines. Probabilistic fatigue life assessment can quantify failure probability and fully utilize the performance of pipeline. The objective of this paper is to develop an effective reliability method to analyze the bending fatigue life of hydraulic pipeline combining with fatigue tests. Firstly, three-point bending fatigue experiments of pipelines are conducted to establish probabilistic-based life models by linear variance regression analysis. Then the established formulas are further verified by the combined results of deterministic finite element analysis and cantilever bending fatigue experiment of hydraulic pipelines. Successive AK-MCS based on ESC is proposed for the fatigue reliability analysis of hydraulic pipelines. The main novelty of successive AK-MCS is that a training information sharing strategy is designed to estimate the failure probabilities under different load cycles. The effectiveness and feasibility of the proposed method is verified by comparing the results of fatigue reliability analysis on cantilever pipeline under an internal pressure of 28Â MPa with traditional AK-MCS. Lastly, the influence of internal pressure and survival probability on the relationship between load cycle and failure probability of the cantilever hydraulic pipeline are further discussed.

Suggested Citation

  • Shen, Xingkeng & Feng, Kaixuan & Xu, Heming & Wang, Guangqiang & Zhang, Yishang & Dai, Ying & Yun, Wanying, 2023. "Reliability analysis of bending fatigue life of hydraulic pipeline," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006342
    DOI: 10.1016/j.ress.2022.109019
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    Cited by:

    1. 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).
    2. Wang, Yifei & Xie, Mingjiang & Su, Chun, 2024. "Multi-objective maintenance strategy for corroded pipelines considering the correlation of different failure modes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. 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).
    4. Zhu, Chun-Yan & Li, Zhen-Ao & Dong, Xiao-Wei & Wang, Ming & Li, Qing-Da, 2024. "Collaborative modeling-based improved moving Kriging approach for low-cycle fatigue life reliability estimation of mechanical structures," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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