Rolling bearing fault diagnosis method by using feature extraction of convolutional time-frequency image

Abstract

The vibration signal of the bearing is mostly used in the fault diagnosis research of rolling bearings, and the vibration signal has the characteristics of strong non-smoothness and is easy to be disturbed by noise. This characteristic of the vibration signal has a strong adverse effect on the fault diagnosis of bearings. To improve the accuracy of bearing fault diagnosis, a rolling bearing fault diagnosis method based on the combination of a short-time Fourier transform and a convolutional neural network is proposed. Firstly, a short-time Fourier transform is performed on the original vibration signal of the bearing to obtain the time-frequency image of the vibration signal. Then, the dual model of 2D convolutional neural network which can obtain abundant fault information is built. Secondly, the obtained time-frequency image is image compressed to fit the proposed model. Finally, the images are input into the network for fault diagnosis, and the fault classification accuracy reaches 100%. The proposed model is tested by two methods: adding Gaussian noise to the time-frequency image and testing the model using bearing data from other working conditions, and the results show that the proposed method has good noise immunity and generalization, which can provide an effective diagnostic scheme for bearing fault diagnosis.