# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 5 Fault Diagnosis and Result Analysis of SOM Neural Network
### Fault Diagnosis and Result Analysis
Figures 17-19 show that when the training times are 100 times, 8 test samples of SOM neural network fault diagnosis model classifier correspond with the training samples of standard SOM neural network, and its fault diagnosis accuracy is up to 100%. It effectively proves the effectiveness of SOM neural network in the classification and identification of gear fault diagnosis. In order to obtain the identification effect advantage of the SOM neural network when the training times are 100 times and the training times are 10, 50, 500, and 1000 times, the gear failure accuracy is compared, respectively, in Figures 20-23. Figure 20 shows the recognition result of the training number 10:  
As can be seen from the identification results in Figure 20, the four fault types of gear, normal gear, gear with tooth wear, gear with tooth crack, and gear with tooth break were classified into three fault states and onegear fault state was not identified. From the identification results in Tables 5 and 6, it can be concluded that normal gear and gear with tooth wear are identified into the same category. This is the classification result of SOM neural network with 10 times of training, and Table 5 is the classification result of SOM neural network with 10 times of training as the training sample matrix with sample numbers 1, 2, 5, 6, 9, 10, 13, and 14. Table 6 shows the classification results of the sample numbered 3, 4, 7, 8, 11, 12, 15, and 16, which were input into the SOM neural network as the test sample matrices after 10 training times. By comparing the classification results in Table 5, it can be seen that the classification results of the test samples from the fault training samples are not good, in which there is an identification error in the fault state of gear with tooth wear and gear with tooth break respectively, resulting in a fault diagnosis identification rate of 75%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Gear Fault Type** & \begin{tabular}{c} **Training** \\ **Steps** \\ \end{tabular} & **Normal Gear** & \begin{tabular}{c} **Gear with** \\ **Tooth Wear** \\ \end{tabular} & \begin{tabular}{c} **Gear with** \\ **Tooth Crack** \\ \end{tabular} & \
\begin{tabular}{c} **Gear with** \\ **Tooth Break** \\ \end{tabular} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Sample number \\ \end{tabular} } & \multirow{2}{*}{100} & 3 & 4 & 7 & 8 & 11 & 12 & 15 & 16 \\ \cline{2-2} \cline{6-10} Classification results & & 1 & 1 & 6 & 6 & 20 & 20 & 32 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification results of the test samples at 100 training times. Figure 20: SOM neural network classification results of gear fault when the number of training is 10. Figure 21 shows the recognition result of the training number 50:  
As can be seen from the identification results in Figure 21, the four fault types of gear, normal gear, gear with tooth wear, gear with tooth crack, and gear with tooth break, were classified into three fault states, and also onegear fault state was not identified. From the identification results in Tables 7 and 8, it can be concluded that gear with tooth wear and gear with tooth crack are identified into the same category. This is the classification result of SOM neural network with 50 times of training, and Table 7 is the classification result of SOM neural network with 50 times of training as the training sample matrix with sample numbers 1, 2, 5, 6, 9, 10, 13, and 14. Table 8 shows the classification results of the sample numbered 3, 4, 7, 8, 11, 12, 15, and 16, which were input into the SOM neural network as the test sample matrices after 50 training times. By comparing the classification results in Table 7, it can be seen that the classification results of the test samples from the fault training samples are not good, in which there is an identification error in the fault state of gear with tooth wear and gear with tooth break, respectively, resulting in a fault diagnosis identification rate of 75%.