Spectral feature-informed difference multi-modes decomposition for compound bearing fault diagnosis

Difference mode decomposition (DMD) is proposed to accurately decompose the signal into health component, fault component and noise. However, DMD is only applicable to vibration signals containing a single fault and is extremely sensitive to interference from other components across all frequencies. In practical operating conditions, bearing damage typically manifests as complex compound faults with numerous intricate disturbances, which makes it challenging for DMD to accurately separate different fault components. To broaden the application prospects of DMD, this paper proposes a difference multi-modes decomposition (DMMD) method, aiming to achieve accurate diagnosis of complex compound-fault signals. Firstly, the center frequencies (CFs) and boundary frequencies (BFs) that indicate fault information are located by the spectral structure information analyzer (SSIA), and the modes containing fault information are selected via correlation kurtosis (CK). Secondly, The initial DMMD weight is set as the average of the difference between two normalized Fourier spectra to improve the efficiency and accuracy of the operation. Finally, Gaussian mixture model (GMM) is used to distinguish the rearranged optimal difference spectrum into three categories accurately and the optimal threshold can be obtained. Simulated and experimental results indicate the effectiveness and accuracy of the proposed method in practical application.