Fault diagnosis strategy for few shot industrial process based on data augmentation and depth information extraction

Abstract Intelligent fault diagnosis method is an important tool for ensuring the stability of industrial processes. However, in the actual industrial process, forming a fault diagnosis model with good performance is difficult because of the complexity of feature extraction and the lack of labelled fault data. Data enhancement on the basis of the original data is important. To address this problem, this study proposes a method called self‐attention embedded generative adversarial network combined with a residual network (SAGAN‐ResNet). First, to address the lack of fault data, the data augmentation method consisting of the self‐attention embedded generator and discriminator is adopted. Then, to extract the features for better diagnosis performance, the residual network (ResNet) is introduced based on the augmented training dataset. A comparison of the proposed method with others shows that it has advantages in the case of complex process fault diagnosis with few‐shot industrial data.