A rolling bearing fault diagnosis method based on GADF-CWT-GCNN

Because of poor model generalization ability and low diagnostic accuracy caused by rolling bearing fault diagnosis in a small sample environment, a novel method based on the Gram angle division field (GADF), the continuous wavelet transform (CWT) and the parallel two-dimensional group normalizatio convolutional neural network (P2D-GCNN) for the fault diagnosis of rolling bearings is proposed. Firstly, collected data are preprocessed and one-dimensional vibration signals are converted into two-dimensional images by using the Gram angle division field and the continuous wavelet transform as the input of the model. Then the data enhancement technique is used to expand the sample sub-graph to meet the input requirements of the network. The sample sub-graph is imported into the convolutional neural network with the group normalization algorithm for diagnostic detection. The results show that the generalization ability of the data processing method and the model built in this paper in the small-sample environment is much higher than that of other network models such as the small vector machine and the 1D-CNN. In order to further verify the recognition ability of the model in the small sample environment, the sample sizes of 70%, 40% and 20% of the dataset are used to do experiments many times. Their corresponding training accuracy and test accuracy were 99.38%, 99.02%, 99.47%, 98.29%, 99.05% and 97.08% respectively, indicating that the model is highly accurate for the fault diagnosis of rolling bearings in the small sample environment.