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Improved fatigue failure model for reliability analysis of mechanical parts inducing stress spectrum

Author

Listed:
  • Yuhan Wang
  • Xintian Liu
  • Haijie Wang
  • Xu Wang
  • Xiaolan Wang

Abstract

To study the influence of random parameter on the reliability of the vehicle front stabilizer bar, the fatigue strength is analyzed according to the random fatigue strength prediction method, then the fatigue limit of the actual parts are estimated, a new fatigue failure model is improved by using the fatigue limit S e of actual parts. Through this model, the stress spectrum collected by real vehicle is introduced, Monte-Carlo simulation method is adopted to analyze the reliability and sensitivities of stabilizer bar under driving conditions. In addition, the influence of each basic random parameter on the failure probability is obtained. The results show that stabilizer bar diameter has a great influence on the failure probability, which provides certain reference for improving the reliability of the front stabilizer bar.

Suggested Citation

  • Yuhan Wang & Xintian Liu & Haijie Wang & Xu Wang & Xiaolan Wang, 2021. "Improved fatigue failure model for reliability analysis of mechanical parts inducing stress spectrum," Journal of Risk and Reliability, , vol. 235(6), pages 973-981, December.
    • Handle: RePEc:sae:risrel:v:235:y:2021:i:6:p:973-981
    DOI: 10.1177/1748006X20987402
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    Cited by:

    1. 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).
    2. Raimundo Aguayo-Estremera & Gustavo R. Cañadas & Elena Ortega-Campos & Tania Ariza & Emilia Inmaculada De la Fuente-Solana, 2023. "Validity Evidence for the Internal Structure of the Maslach Burnout Inventory-Student Survey: A Comparison between Classical CFA Model and the ESEM and the Bifactor Models," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    3. Weiguang Zheng & Haonan Jiang & Shande Li & Qiuxiang Ma, 2023. "Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network," Mathematics, MDPI, vol. 11(10), pages 1-21, May.

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