Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

Aiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the collected vibration signal is obtained through local maximum synchrosqueezing transformation. Secondly, a local histogram cropping equalization image enhancement algorithm is proposed, which is used to obtain time-frequency images with clearer textures. Then, in order to extract fault features from the enhanced instantaneous frequency (IF) image, A new neural network is proposed. The network consists of Multi-size convolution kernel module, Dual-channel pooling layer and Cross Stage Partial Network (MDCNet). Finally, the fault signal was collected on the bearing fault test bench for prediction, and the accuracy rate reached 99.7%. And compared with AlexNet, VGG-16, Resnet and other methods. The results show that the method can meet the needs of actual engineering.

Automated summary

This paper presents a time-frequency analysis method based on image enhancement using the local maximum synchrosqueezing transform, which improves the identification of bearing fault characteristics. The proposed method achieves clearer extraction of fault features and achieves a high accuracy rate.