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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.
Fei et al. (Tue,) studied this question.