# Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism
## 3 Data  
The research data used in this paper derived from the public dataset Vibration, Acoustic, Temperature, and Motor Current Dataset of Rotating Machine under Varying Operating Conditions for Fault Diagnosis, published by [PERSON], [PERSON], [PERSON], et al. [20]. The dataset is divided into two parts, and this paper uses the second part, which is a fault diagnosis dataset containing vibration signals and current signals. Furthermore, the simultaneous acquisition of both vibration and current signals allows for the extraction of more effective features, which enhances the model's ability to capture diverse fault characteristics. The large size of the dataset also improves the generalization and reliability of the model. The data information is organized in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name Detailed** & **Output Tensor Shape** & **Layer Description** \\ \hline Conv1 & [2, 64, 120, 100] & Convolutional layer: input channels: 1; output channels: 64; kernel size: 3 \(\times\) 3; padding = 1. Extracts low-level features. \\ ReLU1 & [2, 64, 120, 100] & Activation function: applies ReLU activation to Conv1 output \\ Conv2 & [2, 128, 120, 100] & Convolutional layer: input channels: 64; output channels: 128; kernel size: 3 \(\times\) 3; padding = 1. Extracts higher-level features. \\ ReLU2 & [2, 128, 120, 100] & Activation function: applies ReLU activation to Conv2 output. \\ Padding & [2, 128, 120, 100] & Padding operation: ensures image dimensions are divisible by patch\_size = 8. \\ Unfold (Patch Division) & [2, 15, 12, 128, 8.8] & Divides image into patches of size 8 \(\times\) 8 and flattens each patch, resulting in (batch\_size, num\_patches\_h, num\_patches\_w, channels, patch\_size, patch\_size). \\ Patch Embedding & [2, 180, 256] & Linear projection layer, flattens each patch and maps to embed\_dim = 256 dimension. Results in [batch\_size, num\_patches, embed\_dim]. \\ Position Embedding & [1, 180, 256] & Dynamically generates position encoding and adds to patch\_embeddings. \\ Transformer Encoder & [2, 180, 256] & Transformer encoder section with 4 TransformerEncoder Layers, with each layer containing num\_heads = 4. Performs encoding on input patches. \\ CBMA Attention & [2, 180, 256] & Multi-head self-attention mechanism (CBMA): input is patch embeddings after Transformer encoding; dimensions remain unchanged \\ MLP & [2, 180, 256] & Multi-layer perceptron, including GELU activation. \\ Mean Pooling & [2, 256] & Pooling operation: computes mean along num\_patches dimension, resulting in [batch\_size, embed\_dim]. \\ Fully Connected & [2, 4] & Classification layer: outputs num\_classes = 4 categories, representing the classification results. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FAN-BD model layer information. This paper extracted vibration signals for various fault types. Figures 4 and 5 show the time-domain waveforms of vibration signals and current signals, respectively. It can be observed that, under normal conditions, the vibration signal waveform is relatively stable, while under rolling element fault and inner-ring fault conditions, obvious periodic impact features appear in the signals. The three-phase current waveforms are basically symmetrical under normal conditions, but obvious current imbalance phenomena appear under outer-ring fault conditions, reflecting the impact of faults on motor performance.