Fault diagnosis method for bearing based on fusing CNN and ViT

Here, aiming at characteristics of low data volume and non-stationary fault signals in bearing fault diagnosis tasks, a bearing fault diagnosis method combining short-term Fourier transform (SFT), convolutional neural network (CNN) and vision transformer (ViT) was proposed. Firstly, the original acoustic signal was transformed into a time-frequency image containing timing information and frequency information using SFT. Secondly, the time-frequency image was taken as input of CNN to implicitly extract deep features of the image, and CNN output was taken as input of ViT. ViT was used to extract signal time series information. In ViT output layer, Softmax function was used to identify bearing fault modes. The experimental results showed that the proposed method has a higher accuracy in diagnosing bearing faults. In order to better explain and optimize the proposed bearing fault diagnosis method, the t-distributed stochastic neighbor embedding algorithm was used to visualize classification features. © 2024 Chinese Vibration Engineering Society. All rights reserved.