## 3 Results
### Comparison of Results with Other Methods  
The fault diagnosis performance of the algorithm proposed in this paper was further validated through a comparative and analytical study with ResNet [34], DenseNet [35], and Le-Net-5 [36]. The detailed configuration parameters of the other three methods are presented in Table 4. Each algorithm utilizes an identical classifier, ensuring fairness in comparison by adopting the same configuration parameters. Specifically, the ResNet network incorporates one residual module (Res), while the DenseNet network consists of two dense connection block modules (Den). Both networks employ a convolution kernel size of 3 with a step size of 1 and utilize a maximum step size of 2 for their pooling operations. All methods input 16 channels from an acoustic array comprising 36 training batches, with an iteration length set to 100. The results from five cross-validations are presented in Table 5, showcasing that the proposed algorithm achieves a fault classification accuracy of 98.56% and a minimal computation time of 22.97 s. Notably, the average accuracy of DenseNet surpasses that of DenseNet by 3.02%. This discrepancy can be attributed to the complex network structure of DenseNet, which demands significant computational resources with an execution time of 92.07 s and substantial memory usage. Meanwhile, due to the absence of morphological \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Parameters** \\ \hline LeNet-5 & 2 \(\times\) (Conv-Pool)-2 \(\times\) \(t\)-Classifier \\ ResNet & 2 \(\times\) [Conv-Pool]-1 \(\times\) Res-Pool-Classifier \\ DenseNet & 2 \(\times\) [Conv-Pool]-2 \(\times\) Den-Pool-Classifier \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structure and parameter setting of comparative methods. Figure 11: Output results of different fault middle layers. Figure 12: Output results at the last fault layer. filter layers and channel attention mechanisms for extracting rich features, the classification accuracy of ResNet is limited to 93.64%. Similarly, the traditional LeNet-5 network exhibits a relatively lower classification accuracy of 84.60%. In conclusion, this paper's proposed method demonstrates exceptional diagnostic precision. In order to further evaluate the feature extraction capability of the four methods, we utilized the t-SNE algorithm to visualize the extracted features on a two-dimensional plane, as illustrated in Figure 13. It is evident from Figure 13 that our proposed method demonstrates superior clustering effectiveness with distinct separation among different fault types. Although Figure 13c showcases satisfactory classification results achieved by the DenseNet, there are instances of overlapping features. Conversely, the LeNet-5 exhibits poor performance in terms of classification as it fails to effectively distinguish and separate fault features. Furthermore, ResNet displays inadequate feature clustering performance. Consequently, it can be inferred that our proposed algorithm possesses commendable noise reduction and feature extraction capabilities. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **Average** & **Total Time** \\ \hline ResNet & 92.7 & 93.1 & 93.5 & 94.0 & 94.9 & 93.64 & 28.87 s \\ DenseNet & 97.2 & 94.5 & 92.1 & 96.5 & 97.4 & 95.54 & 92.07 s \\ LeNet-5 & 88.0 & 80.7 & 87.4 & 88.8 & 78.1 & 84.60 & 28.67 s \\ Proposed method & 98.7 & 98.0 & 98.1 & 98.8 & 99.2 & 98.56 & 22.97 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of four models based on five-fold cross-validation. Figure 13: Visualization of features from (**a**) LeNet-5, (**b**) ResNet, (**c**) DenseNet, (**d**) The proposed method. In order to provide a quantitative explanation of the classification accuracy and error ratio for rolling bearings using four different methods, Figure 14 displays the output results in the form of confusion matrices. These matrices depict the accuracy and recall rates for each type of bearing failure.