A novel intelligent fault diagnosis method of rolling bearings with small samples

Abstract

It’s a challenging work to diagnose faults from the measured vibration signals automatically and efficiently under small samples. A new intelligent fault diagnosis method of rolling bearing with small samples is proposed based on structural similarity generative adversarial network (SSGAN) and improved MobileNetv3 convolutional neural network (IMCNN). Firstly, the wavelet transform (WT) is performed on the signal to obtain a wavelet 2D image with time–frequency characteristics. Then, SSGAN is constructed to obtain high-quality generated samples for expanding the small training sets. Finally, the improved MobileNetv3 convolutional neural network (IMCNN) is proposed to extract feature information of the extended samples by using the self-focus mechanism instead of the original lightweight focus mechanism, and the classification results are acquired for fault recognition. The experimental results show that the proposed SSGAN-IMCNN method can effectively extend the small samples and automatically detect the rolling bearing faults with high classification accuracy.