An interpretable frequency-domain matrix rank transformation and time-domain spatial graph attention enhancement network for rolling bearing fault diagnosis under strong noise environment

Extracting weak fault features obscured by environmental noise is a critical challenge. However, existing fault feature enhancement methods ignore the low-rank distribution of feature correlation structures, making it difficult to effectively extract critical fault information. To address this problem, a frequency-domain matrix rank transformation and time-domain spatial graph attention enhancement network (FMRT-TSGAEN) is proposed. First, an interpretable frequency-domain feature enhancement mechanism is designed, which establishes a transformation theory from low-rank to high-rank matrices by controlling the spectral condition number of the frequency-domain similarity matrix. Then, a dynamic graph structure is designed to capture the spatial dependencies of features across time scales. Meanwhile, a global context gating mechanism is constructed to calibrate global spatial information, thereby achieving global spatial fusion of cross-scale features. Finally, the time-frequency feature learning module is constructed through the residual structure, enabling the model to extract more comprehensive time-frequency information. The experimental results show that the accuracy of FMRT-TSGAEN on two simulated fault datasets reaches 95.63 and 93.83%, respectively. On the real industrial spinning machine bearing dataset, the model achieved excellent results under conditions of no noise, pink noise, Gaussian white noise, and impulse noise, with an average accuracy of 90.39%. Moreover, we prove and visualize changes in the rank of the frequency-domain correlation matrix, which not only reveal the physical principles behind frequency-domain feature enhancement but also effectively improve the model’s interpretability.