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A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

Author

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  • Baoshan Zhang
  • Jilian Guo
  • Feng Zhou
  • Xuan Wang
  • Shengjun Wei

Abstract

Due to the effects of noise disturbances and system resilience, the current methods for rolling bearing fault feature extraction and degradation trend estimation can hardly achieve more satisfactory results. To address the above issues, we propose a different method for fault feature extraction and degradation trend estimation. Firstly, we preset the Bayesian inference criterion to evaluate the complexity of the denoised vibration signal. When its complexity reaches a minimum, the noise disturbances are exactly removed. Secondly, we define the system resilience obtained by the Bayesian network as the intrinsic index of the system, which is used to correct the equipment degradation trend obtained by the multivariate status estimation technique. Finally, the effectiveness of the proposed method is verified by the completeness of the extracted fault features and the accuracy of the degradation trend estimation over the whole life cycle of the bearing degradation data.

Suggested Citation

  • Baoshan Zhang & Jilian Guo & Feng Zhou & Xuan Wang & Shengjun Wei, 2023. "A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-22, July.
    • Handle: RePEc:plo:pone00:0287544
    DOI: 10.1371/journal.pone.0287544
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