A Novel GAN Based Data Augmentation Method Coupled Time–Frequency Domain for Imbalanced Bearing Faul

For imbalanced bearing fault diagnosis, generative adversarial networks (GANs) are a common data augmentation (DA) approach. Nevertheless, current GAN-based methods cannot update the generator from time–frequency domain simultaneously, downgrading the authenticity of signal time–frequency character. In this article, Fourier-like transform GAN (FTGAN), a novel GAN method, is proposed by introducing a Fourier-like transformer (FLT) based on autoencoder (AE) to improve synthetic data quality. FLT approximates the discrete Fourier transform (DFT) by the neural network, learning a universal map from time to frequency domain during training. FTGAN with FLT can decouple input into a time–frequency domain, fitting the distribution of time and frequency of data simultaneously. Multidomain distribution is manipulated in FTGAN without introducing additional signal transformation means. Furthermore, train on real, test on synthetic (TRTS) and train on synthetic, test on real (TSTR) analyses of 1-D data are introduced to evaluate data quality. Real and synthetic data are applied as training or test sets of diagnostic classifiers by turns so that data quality can be analyzed through diagnosis results. Experiment results show that the proposed method can generate bearing fault signals closer to real data in the time and frequency domains, effectively improving the performance under an imbalanced dataset.