基于频谱重构的经验傅里叶分解算法及轴承故障诊断应用OA

To address the tendency of spectral segmentation boundaries concentrating on local narrow bands when the empirical Fourier decomposition(EFD)method was applied to bearing fault signals,an order statistics filter(OSF)was used to simplify the frequency spectrum of the acquired bearing vibration signal,and then averaging and sliding processing and pre-segmentation were performed.To address the potential problem of excessive decomposition,a boundary fusion algorithm based on the frequency-domain squared Gini index(FDSGI)was proposed to adaptively determine segmentation boundaries and decomposition modes.The envelope spectrum harmonic significance(ESHS)indicator was used to select the optimal components.Further,bearing fault diagnosis was enabled through envelope spectrum analysis of the optimal components.The comparative test of bearing fault simulation signals and experimental signals demonstrates that empirical Fourier decomposition based on spectrum reconstruction(SREFD)outperforms EFD and empirical wavelet transform(EWT)in terms of spectral segmentation accuracy.The processed signals allow for clearer observation of bearing fault characteristic frequencies and their harmonics,which validates the effectiveness and robustness of the proposed method.