## 3 Results
### Comparison Results with Other Methods
This shows that the data enhancement strategy has a significant effect on the improvement of model performance. CRAMNet has the best performance, with an average test accuracy of nearly 100% and a small error range. Compared with other models, CRAMNet not only leads in average test accuracy, but also shows better robustness and stability, so it is more suitable for application in practical engineering. To achieve quantitative analysis, this paper uses accuracy, recall, precision, and F1-Score as evaluation metrics for bearing fault data. Each method was trained 10 times, with each epoch set to 100, and evaluated on the same test set. The average results of the tests are shown in Table 6.  
As can be seen from Table 5, the CRAMNet outperforms other comparative methods in all metrics, particularly in precision and F1-Score. This demonstrates that by combining the features of one-dimensional signals and GAF images, and utilizing the multi-head self-attention mechanism, the proposed method can more effectively capture the key features in the bearing fault data, thereby improving classification performance. In the experiments, the classic CNN and LSTM models performed well when processing one-dimensional signal data, while DenseNet achieved relatively good results on GAF image data due to its advantages in handling image data. However, the combined model proposed in this paper significantly improved classification performance by integrating one-dimensional \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**MCC**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Time (Seconds per 100 iterations on GPU)**} \\  & & & & & & & & \\ \hline CNN & 0.724 & 0.724 & 0.729 & 0.724 & 0.681 & 0.726 & 0.715 & 25 s \\ LSTM & 0.576 & 0.576 & 0.576 & 0.576 & 0.559 & 0.576 & 0.561 & 45 s \\ DenseNet & 0.679 & 0.679 & 0.679 & 0.679 & 0.661 & 0.681 & 0.673 & 40 s \\ CNN-Transformers & 0.889 & 0.889 & 0.889 & 0.889 & 0.870 & 0.887 & 0.872 & 30 s \\ CRAMNet & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 35 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Numerical comparison results of different methods. Figure 11: The results of 10 experiments with different models compared. signal and two-dimensional image features and incorporating the multi-head self-attention mechanism. In terms of computation time, the CNN has a relatively simple structure, and thus, its running time is faster. LSTM does not parallelize as effectively on GPUs as CNN, and its computation time is relatively long, reaching up to 45 s. Although DenseNet can efficiently utilize features, its network is deeper, leading to higher computational complexity and longer processing time. CNN-Transformers have a slightly higher computational complexity compared to traditional CNNs, but the parallel-friendliness of Transformers places them between CNNs and LSTM in terms of efficiency. The CRAMNet includes a multi-head attention mechanism, which improves the classification performance of the model but also slightly increases its computational complexity, meaning the processing time may be slightly longer than CNNs. In future research, network architecture should be further optimized. In summary, CRAMNets have important application potential in fault diagnosis tasks.