| Citation: | LUAN Xiaochi, XU Shi, SHA Yundong, et al. Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN[J]. Journal of Aerospace Power, 2023, 38（5）：1185-1197 doi: 10.13224/j.cnki.jasp.20210547
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For the problem that the vibration signal of rolling bearing faults is disturbed by background noise and the fault features are not easily extracted, a combination of non-local mean denoising (NLM) based on the optimization of the gray wolf algorithm (GWO) and fully adaptive noise-enabled ensemble empirical modal decomposition (CEEMDAN) was proposed for bearing fault diagnosis. First, CEEMDAN and the Correlation coefficient-energy ratio-kurtosis criterion were used as preprocessing way, and signal reconstruction was performed; then the grey wolf algorithm was used to optimize the parameters of NLM, and the optimal parameters were used to denoise the reconstructed signal, and secondary denoising of the denoised signal was achieved through SG (Savitzky-Golay) filtering to obtain the final denoised signal, and envelope analysis of the final signal was performed to obtain diagnostic results. For the hybrid feature extraction technique of GWO-NLM denoising, CEEMDAN and envelope analysis, the signal-to-noise ratio was improved by 9.31 dB after denoising as shown by the simulated signal, and the fault characteristic frequency and multiplication frequency of the bearing and the series modulation frequency of the fault characteristic frequency and rotation frequency can be clearly extracted by the experimental signal.
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