| Citation: | LUAN Xiaochi, GAO Xiang, ZHAO Fengtong, et al. Rolling bearing fault feature extraction method based on feature mode decomposition and adaptive window length sliding window noise reduction[J]. Journal of Aerospace Power, 2025, 40（12）：20240695 doi: 10.13224/j.cnki.jasp.20240695
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In view of the problem that the fault signal of aero-engine rolling bearing is affected by background noise, which leads to weak characteristics of the fault signal, a fault feature extraction method of rolling bearing based on grey wolf algorithm optimization feature mode decomposition and adaptive window length sliding window noise reduction was proposed. The vibration signal was decomposed by feature mode decomposition method, and then the signal components were filtered and reconstructed according to the kurtosis correlation coefficient selection criterion. To solve the problem that the input parameters need to be manually filtered with the feature mode decomposition method, the information entropy of the reconstructed signal was selected as the objective function, and the grey wolf algorithm was used to optimize the input parameters to determine the number of modal components and the size of the filter. Then, the noise reduction method of adaptive window length sliding window was used to perform secondary noise reduction and fault feature enhancement on the reconstructed signal, and the noise reduction signal was output. The noise reduction signal was used to extract fault features via envelope demodulation. The effectiveness of the proposed method was verified by using the simulation signal, the data set of Case Western Reserve University, the data of the aeroengine intermediate bearing testbed and the data of the main bearing testbed. The results showed that the combination of feature mode decomposition and sliding window noise reduction method can effectively filter out the interference noise components. The information entropy decreased by approximately 40%, making the fault characteristic frequency and its 2—4 times amplitude in the envelope spectrum more prominent. Compared with other existing bearing fault diagnosis methods, it had better noise reduction effect and stronger fault feature extraction ability.
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