Bearing fault feature extraction method based on enhanced combination difference multiply morphological filter

To address the limitations of conventional time-frequency domain feature extraction methods when dealing with the non-linear, non-stationary and strongly noisy characteristics of rolling bearing fault signals, a bearing fault feature extraction method based on an enhanced combination difference multiply morphological filter is proposed in this study. Based on the understanding of the positive and negative shock pulse extraction characteristics of the four basic operations of mathematical morphology, a new combination difference multiply operator (CDMO) is constructed. This CDMO has the ability to simultaneously extract positive and negative shock pulses by combining cascade, difference and multiply operations. The gradient multiply operation that is more sensitive to pulse extraction is utilized to achieve comprehensive fault information extraction. The fault characteristic frequency ratio index is introduced to optimize the parameters of the CDMO structural elements. This optimization modifies the geometric characteristics of the signal to be processed, allowing for the extraction of signal characteristic information that matches the structural elements. Following CDMO filtering, third-order cumulant slice spectrum technology is employed to suppress Gaussian noise and highlight the advantages of secondary coupling components. This enhances the ability to accurately extract fault feature frequencies and their multiplications, thus improving bearing fault feature extraction and suppressing noise interference. The proposed method’s effectiveness is verified by relying on actual engineering signals from two different sources and comparing its performance with classic fault feature extraction methods.