PerDCGAN: A Perceptual Generative Framework for High-Fidelity Bearing Fault Diagnosis

Data imbalance significantly hinders the performance of deep learning models in rolling bearing fault diagnosis. While Generative Adversarial Networks (GANs) are widely used for data augmentation, traditional architectures employing pixel-level loss functions often fail to capture complex time-frequency textures, resulting in blurred spectrograms and the loss of transient fault characteristics. To address this, we propose a data augmentation framework based on a Perceptually Optimized Deep Convolutional GAN (PerDCGAN). By integrating a perceptual loss function derived from a pre-trained VGG-16 network, the generator is constrained at the feature level rather than the pixel level, explicitly enforcing the preservation of structural details and high-frequency impact patterns. Extensive experiments on the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate that the proposed method effectively mitigates spectral blurring. Ablation studies confirm the synergistic effect of the joint loss function. Furthermore, under extreme 0 dB noise conditions, the classifier augmented by PerDCGAN maintains a robust diagnostic accuracy of 89.65% on the PU dataset, significantly outperforming standard DCGAN and demonstrating strong potential for complex industrial applications.