Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique

Abstract It confronts great difficulty to apply the traditional rolling bearing fault diagnosis methods to adaptively extract features conducive to fault diagnosis under complex operating conditions, and obtaining numerous fault data under real operating conditions is difficult and costly. To address this problem, a fault diagnosis method based on two-dimensional time-frequency images and data augmentation is proposed. To begin with, the original one-dimensional time series signal is converted into two-dimensional time-frequency images by continuous wavelet transform to obtain the input data suitable for two-dimensional convolutional neural network (CNN). Secondly, data augmentation technique is employed to expand labeled fault data. Finally, the generated and original fault data are served as training samples to train the fault diagnosis model based on CNNs. Experimental studies are conducted on standard and real-world datasets to validate the proposed method and demonstrate its superiority over the traditional methods in detecting bearing faults.