Attention-Enhanced Conditional Wasserstein GAN with Wavelet–ResNet for Fault Diagnosis Under Imbalanced Data

Rolling bearings are critical components in mechanical systems, and their health directly affects operational reliability and safety. However, their exposure to harsh conditions makes accurate fault diagnosis essential. Conventional methods relying on expert knowledge and handcrafted features are inefficient, while deep learning still suffers from data imbalance, which limits generalization. To address this challenge, an Attention-Enhanced Conditional Wasserstein GAN (ACWGAN) is proposed, in which the attention mechanism is incorporated into both the generator and discriminator to capture global dependencies and enhance feature diversity. By combining attention guidance with the Wasserstein distance, the framework achieves more stable training, alleviates mode collapse, and generates high-fidelity fault samples to balance imbalanced datasets. Compared with existing GAN-based methods, this method, combined with wavelet-based ResNet, significantly improves the accuracy of diagnosis, achieving 100% accuracy in the generated dataset.