Physics-Informed Generative Self-Attention Framework for Sliding Bearing Fault Diagnosis

Timely detection of sliding bearing oil-film faults is critical for preventing costly failures and ensuring equipment reliability. Yet, diagnosis accuracy is often hindered by data scarcity and severe class imbalance in industrial environments. To overcome these challenges, this study proposes a physics-informed generative framework that couples sliding bearing mechanics with data-driven modeling. The Reynolds equation is embedded into a physics-informed neural network to generate physically consistent samples for data augmentation, while a self-attention–enhanced convolutional neural network is employed to extract features from three-dimensional oil-film pressure distributions. To the best of our knowledge, this is the first work to integrate physics-informed generative modeling with self-attention mechanisms for sliding bearing fault diagnosis. Comparative experiments against conventional finite difference and other imbalanced approaches demonstrate that the proposed method not only solves the oil-film pressure field with higher efficiency but also improves fault classification accuracy on imbalanced datasets by up to 37.5%, thereby offering a new paradigm for robust diagnosis under limited data conditions.