Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images

In the fault diagnosis of mechanical equipment, vibrational signals often contain noise. In order to solve the problem of rolling bearing fault diagnosis under the influence of noise, an image enhancement method based on mean difference images is proposed. Convert one-dimensional time series into two-dimensional image data by using the symmetrized dot pattern method. The data are processed and grouped by variational mode decomposition to obtain a mean image with stable features. A feature enhancement method based on improved mean difference images is used to achieve data mining of fault features. The improved Canberra distance is used as the classification basis to realize the accurate classification of rolling bearing faults. Finally, the classification effect of the method is verified by experiments. The experimental results show that the image enhancement method proposed in this paper can improve fault features in the image and has a good anti-noise ability.