# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
### VGG Fault Diagnosis Model  
In the case of \(\theta=120^{\circ}\), two sets of data were selected for comparison with the optimal data in order to verify that the selected parameters were optimal. The first group of experimental comparisons was used to verify the differences obtained between the thickness and shape characteristics of the petals of the SDP images. The second group of experimental comparisons was used to verify the influence of the degree of concentration of SDP images on the results; the experimental comparisons are shown in Table 4.  
In the VGG16 network structure, the parameter setting matters a great deal. On the basis of several parameter setting experiments, it was concluded that the learning rate set to 0.003, the number of samples per input of 8, and the number of iterations of 1000 were the best settings. When iterating 100 times, the first set of simulation data tends to be stable. Hence, only the first 150 datasets are shown in the figure. The value of sensor accuracy is the sensor name with the time lag factor and the angular gain factor. The training accuracy and loss curve of the VGG16 network structure are shown in Figure 4.  
From the experimental results in Figure 4a, the training accuracy of the VGG16 network based on SDP images was close to 100% after 100 iterations of training, with 99.29% accuracy and a 2.86% loss rate. Judging from Figure 4b, the training times were only close to convergence after 140 iterations, with an accuracy of 97.23% and a loss rate of 11.24%; in contrast, in Figure 4c, it can be seen that the accuracy rate keeps fluctuating, with an accuracy rate of 73.02% and a loss rate of 39.27%. Overall, it can be concluded that when \(\theta=120^{\circ}\), the time lag coefficient and the angular gain factor of \(l=10\), \(\zeta=40^{\circ}\) were optimal.  
On the basis of Figure 4, it was concluded that the diagnosis results based on the SDP method tended to be the best when \(l=10\), \(\zeta=40^{\circ}\). Since the deflection angle also affects the diagnosis results, the present study compares the effect of different deflection angles on the fault diagnosis results when \(l=10\), \(\zeta=40^{\circ}\), as shown in Table 5.  
The training accuracy and loss curves of the VGG16 network structure under different values of deflection angle are shown in Figure 5.  
From Figure 5a, it can be seen that the accuracy is 90.60% and the loss rate is 25.91%. From Figure 5b, it can be seen that the accuracy is 94.37% and the loss rate is 19.99%. From Figure 5c, it can be seen that the accuracy is 96.75% and the loss rate is 13.12%. However, from Figure 4a it can be concluded that the accuracy is 99.29% and the loss rate is 2.86%. Therefore, the fault diagnosis accuracy reached its maximum value at \(\theta=120^{\circ}\), \(l=10\), \(\zeta=40^{\circ}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **Time Lag Coefficient \(I\)** & **Angular Gain Factor \(\zeta\)** \\ \hline
1 & 1 & 10\({}^{\circ}\) \\
2 & 10 & 20\({}^{\circ}\) \\
3 & 10 & 40\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Group** & **Deflection Angle \(\theta\)** \\ \hline
1 & 120\({}^{\circ}\) \\
2 & 90\({}^{\circ}\) \\
3 & 72\({}^{\circ}\) \\
4 & 60\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of deflection angle parameter settings.  
Figure 4: Accuracy of VGG16 experimental results with different parameters at \(\theta=120^{\circ}\). (a) \(l=10\), \(\zeta=40^{\circ}\). (b) \(l=10\), \(\zeta=20^{\circ}\). (c) \(l=1\), \(\zeta=10^{\circ}\).