| Citation: | LUAN Xiaochi, GAO Xiang, XIA Ao, et al. Rolling bearing fault diagnosis method based on grey wolf algorithm optimized feature mode decomposition[J]. Journal of Aerospace Power, 2026, 41（9）：20250334 doi: 10.13224/j.cnki.jasp.20250334
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Aiming at the problem that the vibration signal fault features of rolling bearing are weak when the fault occurs, a rolling bearing fault diagnosis method based on grey wolf algorithm optimized feature mode decomposition was proposed. Firstly, the signal was decomposed by feature mode decomposition to obtain several modal components. Then, the kurtosis-correlation coefficient selection criterion was used to filter and classify the modal components, and the high noise signal and low noise signal were output. Secondly, the high noise signal was decomposed by wavelet packet, and the signal component was reconstructed by weighted fusion index composed of kurtosis, skewness and information entropy to complete signal noise reduction and fault feature enhancement. At the same time, in order to solve the disadvantage that the key parameters of feature mode decomposition and the wavelet packet basis of wavelet packet transform need to be set artificially, the information entropy of the reconstructed signal divided by the kurtosis was selected as the objective function, and the grey wolf algorithm was used to optimize in a certain range and substituted. Finally, the signal was envelope demodulated to extract fault features. The simulation signals, the data set of Western Reserve University, the data of the main bearing test bed of turbofan engine and the fault test data of deep groove ball bearing were used to verify the proposed method. The results showed that the fault features of the processed signal were obvious, and the noise reduction effect of the method was good, the signal-to-noise ratio of the simulated noisy signal increased by 10.76, and the kurtosis value of the aircraft main bearing noisy signal increased by 1.94.
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