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Bearing life prediction method based on the improved FIDES reliability model

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  • Chen, Chuanhai
  • Li, Bowen
  • Guo, Jinyan
  • Liu, Zhifeng
  • Qi, Baobao
  • Hua, Chunlei

Abstract

A bearing life prediction method based on the improved FIDES reliability model was proposed in this paper to solve the problem of difficulty in predicting the service life of products under the action of single acceleration stress level. Firstly, the degradation model of the bearing is established on the basis of the nonlinear Wiener process to fit and predict the degradation data of the bearing. Secondly, transient failure rate function is introduced in the FIDES reliability model by combining the Weibull distribution function. The prediction of bearing life is achieved by considering stress, application and installation factors. Finally, the proposed method is validated with actual cases of bearings. Results showed that the bearing life prediction method based on the improved FIDES reliability model can accurately predict the service life of bearings, and the superiority of the model is demonstrated by comparative analysis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:227:y:2022:i:c:s0951832022003696
    DOI: 10.1016/j.ress.2022.108746
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    as
    1. Moustafa, Kassem & Hu, Zhen & Mourelatos, Zissimos P. & Baseski, Igor & Majcher, Monica, 2021. "System reliability analysis using component-level and system-level accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    2. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2019. "Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 261-270.
    3. Zheng, Bokai & Chen, Cen & Lin, Yigang & Hu, Yifan & Ye, Xuerong & Zhai, Guofu & Zio, Enrico, 2022. "Optimal design of step-stress accelerated degradation test oriented by nonlinear and distributed degradation process," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Hu, Wei & Westerlund, Per & Hilber, Patrik & Chen, Chuanhai & Yang, Zhaojun, 2022. "A general model, estimation, and procedure for modeling recurrent failure process of high-voltage circuit breakers considering multivariate impacts," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Zhang, Cai Wen, 2021. "Weibull parameter estimation and reliability analysis with zero-failure data from high-quality products," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    7. Miao, Mengqi & Yu, Jianbo & Zhao, Zhihong, 2022. "A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Liu, Di & Wang, Shaoping & Cui, Xiaoyu, 2022. "An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    9. Liao, Guobo & Yin, Hongpeng & Chen, Min & Lin, Zheng, 2021. "Remaining useful life prediction for multi-phase deteriorating process based on Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    10. Peng, Weiwen & Li, Yan-Feng & Yang, Yuan-Jian & Huang, Hong-Zhong & Zuo, Ming J., 2014. "Inverse Gaussian process models for degradation analysis: A Bayesian perspective," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 175-189.
    11. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    13. Yu, Wennian & Tu, Wenbing & Kim, Il Yong & Mechefske, Chris, 2021. "A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    14. Liu, Shujie & Fan, Lexian, 2022. "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    15. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    16. Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    17. Wu, Bei & Ding, Dong, 2022. "A gamma process based model for systems subject to multiple dependent competing failure processes under Markovian environments," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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