A Physics-Constrained Spectral Generative Adversarial Network for Rolling Bearing Fault Diagnosis
TL;DR
The PCS-GAN model employs a time-frequency dual-discriminator structure to enforce statistical and spectral realism via physics-informed loss terms via physics-informed loss terms to provide a computationally efficient and reliable solution for fault diagnosis in data-scarce industrial settings.
Abstract
Accurate bearing fault diagnosis is often hindered by sample scarcity and class imbalance. This paper proposes the Physics-Constrained Spectral Generative Adversarial Network (PCS-GAN), which embeds prior mechanical knowledge into adversarial training to enhance physical interpretability. The PCS-GAN model employs a time-frequency dual-discriminator structure to enforce statistical and spectral realism via physics-informed loss terms. To ensure stability, it integrates WGAN-GP with a curriculum-based loss scheduling strategy. Evaluations on CWRU and MFPT datasets confirm that the PCS-GAN model achieves high spectral integrity while reducing peak GPU memory consumption by 28% compared to Transformer-based architectures. Furthermore, the model exhibits robust noise resilience and elevates macro-F1 scores to over 0.94 in extreme imbalance scenarios. These results demonstrate that the PCS-GAN model provides a computationally efficient and reliable solution for fault diagnosis in data-scarce industrial settings.