# 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
Figure 23: SOM neural network classification results of gear fault when the number of training is 1000. As shown in Figure 23, the four fault types of gear fault, normal gear, gear with tooth wear, gear with tooth crack, and gear with tooth break were classified into eight fault states and four unknown fault states emerged. When the number of training sessions of SOM neural network is 1000, Table 11 shows the classification results of the training samples numbered 1, 2, 5, 6, 9, 10, 13, and 14 as training samples matrix input to SOM neural network when the number of training sessions is 1000. Table 12 shows the classification results of samples numbered 3, 4, 7, 8, 11, 12, 15, and 16, which were input into the SOM neural network as the test sample matrices after 1000 times of training. By comparing the classification results in Table 11, it can be seen that the classification results of test samples from fault training samples are not good, in which there is one identification error in the fault state of gear with tooth crack and gear with tooth break respectively, and two identification errors in the fault state of gear with tooth wear, resulting in a fault diagnosis identification rate of 50%. Figures 19-23 shows 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. However, when the training times were 10 times and 50 times, the 8 test samples of SOM neural network model classifier showed onegear fault state was not identified. And when the training times were 500 times and 1000 times, the 8 test samples of SOM neural network model classifier showed unrecognized types. This is not an identification error, which means that as the number of training sessions increases it is the same for each sample, but it is not divided into categories, and there is an unrecognizable type. At this time, if you increase the number of training, there is no practical significance. From the comparison of the accuracy of gear fault recognition of the SOM neural network in different training times in Table 13, it can be obtained that the gear fault recognition accuracy of the SOM neural network is the highest when the training times are 100 times. So the training times of the SOM neural network are 100 times. \begin{table}
\begin{tabular}{c c} \hline \hline
**Training Steps** & **Fault Diagnosis Identification Rate** \\ \hline
10 & 75\% \\
50 & 75\% \\
100 & 100\% \\
500 & 75\% \\
1000 & 50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The accuracy of SOM neural network for gear fault identification under different training times. \begin{table}
\begin{tabular}{c 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 \\ results \\ \end{tabular} } & \multirow{2}{*}{1000} & 1 & 2 & 5 & 6 & 9 & 10 & 13 & 14 \\ \cline{2-2} \cline{6-10} Classification results & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification results for 1000 training sessions. From the above results, it can be concluded that the effect of SOM neural network is the best when the training times are 100 times. The EMD algorithm mentioned in the article will have adverse effects such as mode mixing in the gear fault signal decomposition. Here, the EMD algorithm is combined with the SOM neural network to identify the gear fault states, and then compared with the VMD algorithm and the SOM neural network recognition results.