Abstract: The rolling mill is a key piece of equipment in steel production, and the health of its rotor directly affects equipment safety and production efficiency. However, in practice, fault data is scarce and class imbalance exists, limiting the performance of traditional diagnostic models. To address this, this paper proposes a data augmentation method based on SN-WGAN-MMD to improve the effectiveness of rotor fault diagnosis. The method combines Wasserstein loss with Maximum Mean Discrepancy(MMD) loss to generate realistic samples from both global distribution and high-dimensional feature perspectives, while spectral normalization(SN) is used to enhance training stability. The generated samples are added to the original dataset to alleviate class imbalance. Experiments conducted on two datasets show that the proposed method outperforms GAN, WGAN, and WGAN-GP in terms of sample similarity, class balance, and diagnostic accuracy. The highest accuracy reaches 97.3%, representing a 15% improvement over the original dataset, demonstrating its advantages in global alignment, local feature representation. This provides an efficient and novel data augmentation solution for rotor fault detection.