As critical components in rotating machinery, the operational status of rolling bearings directly affects the safety and reliability of equipment. However, in real-world industrial environments, factors such as strong background noise, multi-source coupled vibrations, and complex transmission paths often mask the early failure characteristics of bearings, making it difficult for traditional methods to achieve accurate diagnosis. While existing signal-processing-based diagnostic methods possess clear physical significance, they exhibit poor adaptability under non-stationary, high-noise conditions; conversely, purely data-driven deep learning methods, despite their powerful feature extraction capabilities, face issues such as poor interpretability, heavy reliance on labeled data, and limited generalization ability. To address this, this paper proposes a fault diagnosis network that integrates physical feature enhancement and attention mechanisms—PFA-Net. This network consists of a cascaded Feature Decoupling Enhancement Module (FDEM) and a Feature Aggregation Module (FAM). The FDEM comprises a Quadratic Convolution Feature Enhancement Submodule (QCFEM) and a Physical Feature Decoupling Submodule (PFDM). the former enhances higher-order nonlinear features in time-frequency images through learnable quadratic convolution operations, while the latter introduces physical operators such as the Fourier transform and the Hilbert transform to decouple fault pulses and envelope information from the enhanced features into interpretable physical metrics, thereby effectively highlighting fault modes in noisy environments. FAM, on the other hand, integrates an improved residual block with a Swin Transformer. It enhances local key features through a convolutional attention module (CBAM) and captures global dependencies using a shift-window self-attention mechanism, achieving efficient synergy between local and global features. Through optimization via a dynamic joint loss function, PFA-Net significantly improves fault diagnosis accuracy in high-noise environments while ensuring physical interpretability. Experimental results demonstrate that this method exhibits excellent robustness and generalization capabilities under complex operating conditions, providing a structurally clear and high-performance solution for intelligent fault diagnosis.
Wang et al. (Tue,) studied this question.