| Citation: | LI Yanzheng, LUAN Xiaochi, YANG Jie, et al. Rolling bearing vibration feature extraction and characterization method based on maximum correlated kurtosis deconvolution optimized by gray wolf optimization algorithm[J]. Journal of Aerospace Power, 2025, 40（3）：20230338 doi: 10.13224/j.cnki.jasp.20230338
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In view of the problem that the weak fault characteristics of rolling bearings in the early stage affected by background environmental noise are difficult to be extracted, a rolling bearing vibration feature extraction and characterization method was proposed based on maximum correlated kurtosis deconvolution optimized by gray wolf optimization (GWO) algorithm. The complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) was used to decompose the weak fault vibration signal disturbed by strong background environmental noise into several signal components, and the signal components were screened and reconstructed according to the kurtosis and correlation coefficient as the screening index in this method. The maximum correlated kurtosis deconvolution (MCKD) optimized by the GWO algorithm filtered out the noise components in the reconstructed signal, enhanced the weak fault feature components and performed envelope demodulation to extract the weak fault features. A comprehensive verification of the effectiveness of the vibration signal fault feature extraction and characterization method was carried out based on the rolling bearing test bench data and the real whole machine data of the aero-engine. The results showed that this method can effectively filter out the strong background environmental noise part in the weak fault vibration signal and enhance the weak fault characteristics, indicating that the peak factor of the denoising signal processed by this method increased by 2.43 compared with the original vibration signal in the turbofan engine experiment, so it effectively enhanced the shock component in the vibration signal. The method proposed can be used as one of the effective methods for fault diagnosis of aero-engine.
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