Quaternion empirical wavelet transform and its applications in rolling bearing fault diagnosis

In view of the fact that it is difficult to predict the location and size of the failure of rotating machinery, the sensors sometimes cannot collect the fault information. When the fault location differs from the sensor direction by 180°, the fault information in the signal is weak and may be masked by noise. Multi-channel signal acquisition is one of the methods to solve the problem. Although existing algorithms can extract fault information from a single-channel signal, they are inefficient and sometimes fail to extract weak fault information. Therefore, the information in each channel needs to be associated and combined in advance. This paper proposes a multi-channel signal processing method called quaternion empirical wavelet transform (QEWT). QEWT based on the quaternion Fourier transform can process multiple groups of vibration signals at the same time, which realizes synchronous spectral division, fusion and modal decomposition. The modal separation method based on spectral trend avoids the problems of modal aliasing and low efficiency of scale-space representation. The proposed method combines spectral negentropy with the identification of the fault characteristics of the outer ring and the inner ring, and successfully realizes the fault diagnosis of the rolling bearing.