LOSGAN: latent optimized stable GAN for intelligent fault diagnosis with limited data in rotating machinery

Abstract Despite the great achievements of the intelligent diagnosis methods of rotating machinery based on being data-driven, it still suffers from the problem of scarce labeled data. Therefore, this paper focuses on developing a data augmentation method of few-shot learning for fault diagnosis under small sample size conditions. Firstly, we developed the latent optimized stable generative adversarial network to adaptively augment the small sample size data without prior knowledge. Furthermore, penalty terms based on the distance metric for differences in distributions are adopted to constrain the optimization objective of the model. And self-attention and spectral normalization are applied in the model to stabilize the training process. Then, supervised classifier training is conducted based on the augmented sample set. Comparative analysis of the frequency spectrum verified the authenticity and reliability of the generated samples. Finally, the performance of the proposed method is validated with a comparative study on three cases of rolling bearing fault diagnosis experiments. The average accuracy can achieve 99.71%, 99.7%, and 96.27% in 10-shot sample fault diagnosis.