Unbalanced Data-Based Fault Diagnosis Method of Bearing Utilizing Time-Frequency DCGAN Processing

Aiming at the unbalanced datasets of fault samples of bearing, a fault diagnosis method of bearing based on time-frequency DCGAN processing is proposed in this paper. Firstly, through STFT, the vibration signals are converted into the time-frequency images, and then the time-frequency images are input into DCGAN to expand the fault samples. Secondly, the expanded fault samples are evaluated for image quality through the comprehensive method of PSNR and SSIM. Thirdly, the Canny edge detection algorithm is used to extract features from the time-frequency image, and the obtained binary image is used as the feature. Finally, k-nearest neighbor algorithm is used for classification to testify the superiority of time-frequency DCGAN processing. The experimental results show that the expanded samples can effectively improve the unbalance of the samples and improve the accuracy of fault diagnosis of bearing.