## 3 Results
### Analysis Results  
The training and testing process as well as the loss value of the algorithm proposed in this paper are illustrated in Figure 7. The blue line depicts the accuracy rate's trajectory throughout the training cycles, initially starting from a relatively low value but rapidly ascending in the initial few cycles before stabilizing at approximately 100%. On the other hand, the green line represents the loss value during these training cycles. Around the 10 th cycle, it reaches an extremely low point and remains close to zero for the remainder of the training period. The swift increase in accuracy and simultaneous decrease in loss values during the initial phase underscored this model's efficient learning capability. Furthermore, maintaining stability across subsequent cycles further accentuates robustness within our training process. To demonstrate the rationality of the algorithm proposed in this paper, the following fusion experiment analysis was conducted on the algorithm. The following evaluation metrics were selected: F1-Score, Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy, and precision. Each module was evaluated using these metrics, and the formulas for each metric are defined as follows:  
\[F1=2\times\frac{Precision\times Sensitivity}{Precision+Sensitivity} \tag{9}\]  
\[MCC=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{10}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{11}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{12}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{13}\]  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
where \(TP\) stands for True Positive, \(TN\) stands for True Negative, \(FP\) stands for False Positive, and \(FN\) stands for False Negative. From Table 4, it can be seen that ResNet1D's sensitivity and precision are both above 0.85, while its specificity and MCC are relatively lower, indicating that this model has some shortcomings in identifying negative samples, and its overall classification performance is still lacking. ResNet2D shows significant improvement, but its overall metric scores are still not ideal. After combining both one-dimensional and two-dimensional features, the performance of the combined model improves compared to ResNet1D and ResNet2D. Figure 7: Loss and accuracy of the CRAMNet training process. Accuracy and specificity increase to 0.92 and 0.91, respectively, demonstrating an overall enhancement in classification performance. By introducing a 4-head multi-head attention mechanism, the model's performance metrics are further improved. The F1-Score reaches 0.95, MCC reaches 0.92, and specificity reaches 0.94, indicating a significant improvement in the recognition accuracy of negative samples. When the number of attention heads is increased to eight, all the model's performance metrics approach a perfect state, with sensitivity and specificity both reaching 0.98 and 0.97, respectively. Using a 12-head multi-head attention mechanism, the model achieves an average score of 1.00 across all metrics, meaning that the model can correctly identify all class samples without any misclassification. This demonstrates that the method has reached optimal performance with the current dataset, proving its effectiveness and reliability in fault diagnosis tasks. Figure 8 shows the t-SNE dimensionality reduction results of features extracted by different modules. Each point represents a sample, with different colors indicating different classes. In Figure 8a,b, the ResNet1D module demonstrates a certain level of discriminative ability in feature extraction, while the ResNet2D module exhibits a higher capacity in capturing spatial features. The distribution of features extracted by the multi-head attention mechanism after t-SNE dimensionality reduction is shown in Figure 8c, indicating that the attention mechanism performs excellently in feature extraction and classification, effectively capturing the relevant information in the data.