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Time-variant reliability analysis of angular contact ball bearing considering the coupled effect of rolling contact fatigue damage and wear

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Listed:
  • Xie, Bin
  • Wang, Yanzhong
  • Zhu, Yunyi
  • Liu, Peng
  • Wu, Yu
  • Lu, Fengxia

Abstract

Rolling contact fatigue (RCF) damage and wear of angular contact ball bearing (ACBB) are time-varying and coupled, which are competing failure mechanisms affecting each other. Therefore, to more reasonably and accurately evaluate the reliability of ACBB under different number of cycles, a physical model for ACBB reliability analysis considering the coupled effect of RCF damage and wear is proposed. First, the load distribution of ACBB is obtained by performing force analysis on the ball and inner ring separately. The octahedral shear stress, which is the key factor affecting RCF damage, is obtained utilizing Hertzian contact theory. Subsequently, based on damage mechanics theory, a damage evolution equation is applied to describe RCF damage degree. The classical Archard wear model is applied to calculate the wear amount of ACBB. To establish the coupled effect between RCF damage and wear, a geometric constraint equation considering the wear depth of ACBB is proposed. Additionally, to avoid expensive computational effort caused by excessive calls to the performance function, the modified instantaneous response surface (t-IRS) method is used to evaluate the time-varying reliability of ACBB. Eventually, a practical example of ACBB is given to verify the validity and accuracy of the proposed model and method.

Suggested Citation

  • Xie, Bin & Wang, Yanzhong & Zhu, Yunyi & Liu, Peng & Wu, Yu & Lu, Fengxia, 2024. "Time-variant reliability analysis of angular contact ball bearing considering the coupled effect of rolling contact fatigue damage and wear," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005811
    DOI: 10.1016/j.ress.2023.109667
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    References listed on IDEAS

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    1. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    2. Li, Guofa & Wei, Jingfeng & He, Jialong & Yang, Haiji & Meng, Fanning, 2023. "Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Wang, Zequn & Wang, Pingfeng, 2013. "A new approach for reliability analysis with time-variant performance characteristics," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 70-81.
    4. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Kaya, Gulsum Kubra & Ozturk, Fatih & Sariguzel, Emine Emel, 2021. "System-based risk analysis in a tram operating system: Integrating Monte Carlo simulation with the functional resonance analysis method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Zhang, Yongchao & Ji, J.C. & Ren, Zhaohui & Ni, Qing & Gu, Fengshou & Feng, Ke & Yu, Kun & Ge, Jian & Lei, Zihao & Liu, Zheng, 2023. "Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Dai, Le & Guo, Junyu & Wan, Jia-Lun & Wang, Jiang & Zan, Xueping, 2022. "A reliability evaluation model of rolling bearings based on WKN-BiGRU and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    10. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Chen, Chuanhai & Li, Bowen & Guo, Jinyan & Liu, Zhifeng & Qi, Baobao & Hua, Chunlei, 2022. "Bearing life prediction method based on the improved FIDES reliability model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
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