CVRgram for Demodulation Band Determination in Bearing Fault Diagnosis under Strong Gear Interference

Authors

  • Pengda Wang Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124 https://orcid.org/0000-0003-0986-0886
  • Dezun Zhao Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124
  • Dongdong Liu Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124
  • Lingli Cui Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124

DOI:

https://doi.org/10.37965.jdmd.2022.135

Keywords:

bearing fault diagnosis; CVRgram; gear meshing interference; resonance frequency band detection

Abstract

Fault-related resonance frequency band extraction-based demodulation methods are widely used for bearing diagnostics. However, due to the high peaks of strong gear meshing interference, the classical band selection methods have poor performance and cannot work well for bearing fault type detection. As such, the CVRgram-based bearing fault diagnosis method is proposed in this paper. In the proposed method, inspired by the conditional variance (CV) index and root mean square (RMS), a novel index, named the CV/ root mean square (CVR), is first proposed. The CVR index has high robustness for the interference of non-Gaussian or Gaussian noise and has the ability to determine the center frequency of the weak bearing fault-related resonance frequency band under strong interference. Secondly, motived by the Kurtogram, the CVRgram algorithm is developed for adaptively determining the optimal filtering parameters. Finally, the CVRgram-based bearing fault diagnosis method under strong gear meshing interference is proposed. The performance of the CVRgram-based method is verified by both the simulation signal and the experiment signal. The comparison analysis with the Kurtogram, Protrugram, and CVgram-based method shows that the proposed technique has a much better ability for bearing fault detection under strong noise interference.

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Published

2022-12-23

How to Cite

Wang, P., Zhao, D., Liu, D., & Cui, L. . (2022). CVRgram for Demodulation Band Determination in Bearing Fault Diagnosis under Strong Gear Interference. Journal of Dynamics, Monitoring and Diagnostics, 1(4), 237–250. https://doi.org/10.37965.jdmd.2022.135

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Section

Regular Articles