A Safe-Domain Generative Adversarial Network with Swin Transformer for Noisy Imbalanced Fault Diagnosis

Currently, data-driven fault diagnosis methods have achieved remarkable progress. However, in industrial scenarios, acquiring a sufficient amount of fault data poses a challenge, thereby leading to the issue of imbalanced data in intelligent fault diagnosis. Furthermore, manual recording and instrument measurement errors will introduce label noise, which significantly impacts diagnosis performance. To address these problems, this paper proposes a safe-domain generative adversarial network with Swin Transformer (SDGAN-ST). A safe domain selection method is utilized to eliminate noisy samples and construct a pure dataset that poses no risk to the GAN training process. Consequently, GAN can generate high-quality minority samples to rebalance the original dataset. Additionally, the Swin Transformer is employed as a classifier to capture global information for each fault sample, thereby achieving high diagnostic accuracy. Experiments on the CWRU dataset and a real-world oxygen compressor bearing dataset demonstrate the effectiveness of the proposed method. On the CWRU dataset, SDGAN-ST achieves accuracies of 98.88%, 97.63%, and 97.50% under imbalance ratios of 1:10, 1:20, and 1:30, respectively. On the real-world dataset, SDGAN-ST achieves 100% accuracy under all three imbalance ratios. Additional experiments under noise ratios of 20%, 30%, and 40% show that SDGAN-ST maintains stable diagnostic performance and is more robust to label noise than ordinary WGAN-GP-based methods.