## 3 Dynamics Simulation and Experimental Verification
### Method Comparison  
To further evaluate the validity of the model, three methods were adopted to compare with the method proposed in this section. The comparison results are shown in Figure 11, where Base represents the method proposed in this paper. Although the verification accuracy rates of all four methods can reach over 80%, it can be seen from the comparison result graph that the method proposed in this paper is superior to the other methods. To verify the effectiveness of dynamic weight allocation, an ablation study was designed. The baselines include Base (a basic CNN), CNN-SF (CNN with static fusion), CNN-SA (CNN with self-attention mechanism) and CNN-CA (our proposed method). Both experimental and simulation data validate the method. Figure 12 shows that cross-attention with dynamic weights achieves higher accuracy. Other methods, without dynamic weights, have relatively lower accuracy. This ablation study further confirms that CNN-CA outperforms other CNN-fused methods in accuracy. In order to see the comparison results more intuitively, we drew the confusion matrices of ResNet, BiLSTM and CNN and the t-SNE classification visualization diagrams. As shown  
Figure 11: Comparison chart of the accuracy rates of four different models, blue represents Base, purple represents CNN, red represents BiLSTM, and yellow represents ResNet. Figure 12: Accuracy comparison. in Figure 13, although the classification result of the ResNet method reaches 90%, compared to the approach proposed in this study, it is marginally worse. Although the residual structure of ResNet can alleviate the vanishing gradient problem and extract deep features, its fixed skip connection mechanism lacks the ability of dynamic feature interaction. It is difficult to adaptively capture the complex correlations among multi-sensor signals or features of different frequency bands like the cross-attention mechanism. ResNet has a relatively weak ability to model the global context of multi-scale fault features in the feature fusion stage. However, cross-attention can more effectively integrate local details and global dependencies through the interaction between feature channels/spaces, especially having a stronger recognition ability for weak fault signals and time-varying features under non-stationary working conditions. As shown in Figure 14, although the classification accuracy of BiLSTM has reached more than 80%, it can be seen from t-SNE that the classification results are relatively poor and the classification points are relatively scattered. Although BiLSTM can effectively model the long-term bidirectional dependency relationship of temporal signals, its recursive structure lacks the inherent local spatial feature extraction ability of CNN when dealing with high-dimensional and multi-channel sensor signals, making it difficult to efficiently capture the key local patterns in fault signals. The serial computing characteristics of BiLSTM lead to its low efficiency in parallelization processing of long sequence data. Figure 14: (**a**) BiLSTM confusion matrix; (**b**) visualization diagram. Figure 13: (**a**) ResNet confusion matrix; (**b**) t-SNE visualization diagram. As shown in Figure 15, the classification of the CNN not only has a lot of confusion but also has a classification accuracy rate lower than 80%, and the classification points are particularly scattered. Although the CNN can effectively extract local spatial features through convolutional layers, its fixed receptive field and static weight allocation mechanism cause difficulty in adaptively focusing on the key fault areas. The cross-attention mechanism can enhance the representation ability of weak fault features through dynamic weight adjustment. The hierarchical stacking structure of the CNN is prone to losing fine-grained fault information in deep networks, while cross-attention, through bidirectional information fusion between feature maps, can integrate local details and global dependencies more robustly.