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

Abstract

Aiming at the unbalanced datasets of bearing fault samples, a fault diagnosis method based on time-frequency conversion and processing of bearing vibration signals based on deep convolutional generative adversarial network (DCGAN) is proposed in this paper. Firstly, through short-time Fourier transform(STFT), the vibration signals are converted into time-frequency images, and then 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 peak signal-to-noise ratio(PSNR) and structural similarity(SSIM). Thirdly, using the Canny edge detection algorithm to extract features of the time-frequency image, and using the obtained binary image 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 imbalance of the samples and improve the accuracy of fault diagnosis of bearing.