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A reliability evaluation model of rolling bearings based on WKN-BiGRU and Wiener process

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  • Dai, Le
  • Guo, Junyu
  • Wan, Jia-Lun
  • Wang, Jiang
  • Zan, Xueping

Abstract

Reliability evaluation is highly significant for the safe and reliable service of rolling bearings. It is to accurately reflect degradation states of rolling bearings. However, traditional methods have difficulties in solving the problems resulted from the lack of measured data, while the deep learning techniques are insufficient in dealing with uncertainties. This paper proposes a new reliability evaluation schedule based on the WaveletKernelNet (WKN), bidirectional gated recurrent unit (BiGRU), and Wiener process model. The proposed method consists of two parts: a health index construction model by the WKN-BiGRU and a Wiener process-based reliability evaluation method. The WKN-BiGRU network is to extract deep features and construct the health index of the rolling bearings. The Wiener process is to achieve the reliability evaluation of rolling bearings and to quantify uncertainties. The effectiveness of the proposed methodology is confirmed by a real case study of rolling bearings. Overall, the proposed methodology contributes to effectively deep features extraction and reliability estimation of rolling bearings.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002824
    DOI: 10.1016/j.ress.2022.108646
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    6. Chang, Yuanhong & Li, Fudong & Chen, Jinglong & Liu, Yulang & Li, Zipeng, 2022. "Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
    9. Chen, Zhiwei & Zhao, Yanlin & Yang, Jinling & Wang, Yao & Dui, Hongyan, 2024. "A novel degradation model and reliability evaluation methodology based on two-phase feature extraction: An application to marine lubricating oil pump," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    10. Chen, Wen-Bin & Li, Xiao-Yang & Wu, Ji-Peng & Kang, Rui, 2024. "Uncertain random accelerated degradation modelling and statistical analysis with aleatory and epistemic uncertainties from multiple dimensions," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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