FDCA-GRU: A dual-branch fusion network with frequency dynamic convolution attention for bearing fault diagnosis

Rolling bearing fault diagnosis is confronted with significant technical challenges, including the difficult extraction of weak fault features and the insufficient fusion of one-dimensional (1D) temporal dynamics with two-dimensional time-frequency patterns. To address this issue, this article proposes a novel dual-branch fusion network that synergistically integrates a ResNet optimized with frequency dynamic convolutional attention with a gated recurrent unit for accurate bearing fault diagnosis, termed FDCA-GRU. Firstly, a parallel dual-branch architecture is constructed. The image branch encodes the 1D signal into a Markov transition field image, which is then fed into an optimized FDCA-ResNet backbone. Innovatively, frequency dynamic convolution (FDConv) is introduced in this branch to replace standard convolution, enabling it to adaptively learn filter characteristics in the frequency domain. This significantly enhances the perception of specific frequency fault patterns. Meanwhile, the convolutional block attention module (CBAM) is integrated into the residual blocks. Through its channel and spatial attention mechanisms, features are adaptively weighted to enhance the model’s response to discriminative feature regions. On the other hand, the temporal branch utilizes a GRU network to directly model the 1D stacked sequences, thereby efficiently capturing long-term dynamic temporal dependencies and evolutionary laws. The two branches are complementary in that the image branch emphasizes state-transition textures and frequency-related spatial patterns, whereas the temporal branch preserves amplitude evolution, transient impact order, and long-range sequential dependencies. Finally, the deep features extracted by the two branches are fused via cross-modal concatenation to achieve synergistic optimization of time-frequency spatial patterns and dynamic temporal information. Test results on the Beijing Jiaotong University Rail Autonomous Operation bogie datasets (BJTU-RAO) and HUST public datasets show that the proposed method achieves average diagnostic accuracies of 99.52 and 98.61%, respectively. Furthermore, ablation experiments systematically validate the synergistic effects and respective contributions of the dual-branch fusion architecture, FDConv, and CBAM, confirming the effectiveness of the proposed framework.