Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

The dataset in the application scenario of existing fault diagnosis methods is often balanced, while the data collected under actual working conditions are often imbalanced. Directly applying existing fault diagnosis methods to this scenario will lead to poor diagnosis effect. In view of the above problems, we proposed a method called dual generative adversarial networks (DGANs) combining conditional assistance and feature enhancement. The method uses data augmentation as a basic strategy to supplement imbalanced datasets by generating high-quality data. Firstly, a new generator is designed to build the basic framework by sharing the dual-branch deconvolutional neural networks, and combining the label auxiliary information and the coral distance loss function to ensure the diversity of generated samples. Secondly, a new discriminator was designed, which is based on deep convolutional neural networks and embedded with auxiliary classifiers, further expanding the function of the discriminator. Thirdly, the self-attention module is introduced into both the generator and the discriminator to enhance deep feature learning and improve the quality of generated samples; finally, the proposed method is experimentally validated on datasets of two different testbeds. The experimental results show that the proposed method can generate fake samples with rich diversity and high quality, using these samples to supplement the imbalanced dataset, the effect of imbalanced fault diagnosis has been substantially improved. This method can be used to solve the problem of fault diagnosis in the case of sample imbalance, which often exists in actual working conditions.