## 4 Experimental Verification and Analysis
### Comparative Analysis  
5.1 Comparison between Single-Channel Fault Diagnosis Methods and Multi-Channel Fault Diagnosis Methods  
In this section, the data of each view was input into the single-channel CNN model in the same way, and the comparison experiments were carried out with the proposed method. Each method was repeated 10 times, the diagnostic results of the model on test sets A, B and C are shown in Figure 6. As can be seen from the figure, the diagnostic accuracy of the CAM_MCFCNN model basically reached 100%. The diagnostic accuracy of the single-channel CNN model was affected by the view positon. The signal quality was related to the view position to a certain extent, which determines the number of fault features contained in the signal. Therefore, the fusion of multi-view information could provide more comprehensive and accurate information for the fault diagnosis of the gearbox. The average test accuracy, standard deviation and average training time of the model in ten trials were shown in Table 4. As can be seen from the table, the average diagnostic accuracy of the CAM_MCFCNN model on different datasets was between 99.87% and 99.95%, while the average diagnostic accuracy of the single-channel CNN model was between 81.10% and 99.58%. The average test accuracy of the single-channel CNN based on view 1 and view 9 was between 96.12% and 99.58%. Although they had reached acceptable test accuracy, the standard deviation of the single-channel CNN model was much larger than that of the CAM_MCFCNN model. In addition, compared with the single-channel \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Layer** & **Parameters** & **Output Size** \\ \hline Input & Input Layer & Channel = 9 & \([9\times 1\times 1024]\) \\ \hline \multirow{4}{*}{View-level weight} & Global average Pooling & None & \([9\times 1]\) \\  & Global max Pooling & None & \([9\times 1]\) \\  & Convolutional Layer 1 & C = 18, KS = 1 & \([18\times 1]\) \\  & Convolutional Layer 2 & R = 1/2, C = 9, KS = 1 & \([9\times 1]\) \\ \hline \multirow{4}{*}{Feature extraction} & Convolutional Layer & C = 20, KS = 9, S = 3 & \(20\times 1\times 339\) \\  & Max pooling Layer & Pool size = 2 & \(20\times 1\times 169\) \\ \cline{1-1}  & Convolutional Layer & C = 40, KS = 9, S = 3 & \(40\times 1\times 54\) \\ \cline{1-1}  & Max pooling Layer & Pool size = 2 & \(40\times 1\times 27\) \\ \cline{1-1}  & Fully connection Layer & Hidden Nodes = 100 & \(100\times 1\) \\ \hline \multirow{2}{*}{Output} & \multirow{2}{*}{Fully connection Layer} & Hidden Nodes = 5, & \([5\times 1]\) \\ \cline{1-1}  & & activation = Softmax & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Module parameters setup of of CAM_MCFCNN model. Figure 6: Diagnostic accuracy of ten experiments with single-channel convolutional neural networks (CNN) based on single-view data and CAM_MCFCNN based on multi-view data on test sets A (a), B (b), and C (c), respectively. CNN model, The multi-channel structure of the CAM_MCFCNN model had little effect on the training time while improving the accuracy of diagnosis. In order to more clearly explain the recognition effect of the model on each fault category of the test set, the best diagnostic results of the seventh view data with the best signal quality were selected to compare with the worst diagnostic results after multi-view fusion, as shown in Figure 7. The best diagnosis results of the single-channel CNN model on the three test sets are shown in Figure 7(a1-a3). Except for test set C, the accuracy of the single-channel CNN model on test sets A and B was lower than that of the CAM_MCFCNN model. On test set A, the diagnosis accuracy was 99.7%; two samples of three teeth wear states were misclassified as single tooth wear. On test set B, one sample of three teeth wear was misdiagnosed as two teeth wear, and three samples of normal state were misdiagnosed as single tooth wear. The worst diagnosis results of the CAM_MCFCNN model in the three test sets are shown in Figure 7(b1-b3).