A Multi-Modal Three-Channel Bearing Fault Diagnosis Method Based on CNN Fusion Attention Mechanism Under Strong Noise Conditions

As a core component of mechanical equipment, the operational status of bearings directly determines equipment safety, making early fault diagnosis critically important. However, bearing vibration signals are susceptible to substantial noise interference and exhibit both nonlinear and non-stationary characteristics, rendering traditional single-mode diagnostic methods ineffective at extracting fault features. Therefore, this paper proposes a three-channel multimodal fault diagnosis network (M-CNNBiAM) integrated with a convolutional autoencoder (CAE). Based on a convolutional neural network (CNN) architecture, this network employs CAE for signal denoising, utilizes continuous wavelet transform (CWT) to construct time-frequency features, and incorporates dual enhancement modules: convolutional attention (CBAM) and window attention (S-W-MSA).On one hand, it extracts complementary features from the raw vibration signal and the wavelet transform frequency domain signal, fusing them at the channel dimension. On the other hand, it embeds Shifted Window Attention (SW-MSA) and Window Self-Attention (W-MSA) between convolutional layers to capture global-local features. Combined with CBAM to enhance fault location attention, it mitigates the vanishing gradient problem through residual connections, enabling the extraction of frequency domain features. To address the characteristics of one-dimensional time-series signals, a bidirectional gated recurrent unit (BiGRU) is introduced to collaborate with CNN for extracting temporal features. Experiments demonstrate that on the West China University public dataset and self-test dataset, M-CNNBiAM achieves an average diagnostic accuracy of 95.84% under -10dB high-noise conditions, outperforming comparative methods and validating its superior performance in complex noise environments.