## 5 Experimental Results
### Result Comparison  
The CNN network model recognition results of this study are presented in Table 3. This study's proposed method was compared with the Lorenz e1e2 chaos dynamic error scatter diagram with four other algorithms: visual geometry group (VGG) 16, SVM [26], back propagation neural network (BPNN) [27], and edited nearest neighbor (ENN) [28]. The test data were mixed with 20% and 30% noises to conduct recognition tests. From the feature extraction method perspective, in the case of the same recognition method, the SDP had a higher recognition rate than chaos dynamic error scatter diagrams. Regarding training time, the methods using SDP other than SVM had a shorter training time than chaos dynamic error scatter diagrams. The results indicate that the SDP could extract fault features effectively and increase recognition accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{\
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Testing** \\ **Time (s)** \\ \end{tabular} } & \multirow{2}{*}{**Epoch**} & \multicolumn{4}{c}{**Accuracy Rate (\%)**} & \multirow{2}{*}{**Ranking**} \\  & & & & **Noise** & & **Noise** & \\ \hline SDP + CNN & 46.52 & 0.0004 & 50 & 98.8 & 98.6 & 98.6 & 1 \\ \hline SDP + VGG16 & 353 & 0.0075 & 50 & 97.6 & 96.2 & 94 & 2 \\ \hline SDP + HOG + SVM & 9.04 & 0.972 & N/A & 97.2 & 96.6 & 95.8 & 3 \\ \hline SDP + HOG + BPNN & 174.11 & 0.359 & 10,000 & 96.8 & 93.6 & 89.6 & 4 \\ \hline SDP + HOG + ENN & 2952 & 1.23 & 50 & 89 & 85.4 & 82.6 & 5 \\ \hline Chaos dynamic error scatter & \multirow{2}{*}{49.2} & \multirow{2}{*}{0.0043} & \multirow{2}{*}{50} & \multirow{2}{*}{84} & \multirow{2}{*}{78.4} & \multirow{2}{*}{76.8} & \multirow{2}{*}{8} \\  & + CNN & & & & & & \\ \hline Chaos dynamic error scatter & \multirow{2}{*}{376} & \multirow{2}{*}{0.0057} & \multirow{2}{*}{50} & \multirow{2}{*}{84.4} & \multirow{2}{*}{83} & \multirow{2}{*}{78.2} & \multirow{2}{*}{7} \\  & + VGG16 & & & & & & \\ \hline Chaos dynamic error scatter & \multirow{2}{*}{2.9787} & \multirow{2}{*}{0.0311} & \multirow{2}{*}{N/A} & \multirow{2}{*}{78.6} & \multirow{2}{*}{74.6} & \multirow{2}{*}{72.8} & \multirow{2}{*}{9} \\  & + HOG + SVM & & & & & & \\ \hline Chaos dynamic error scatter & \multirow{2}{*}{184.32} & \multirow{2}{*}{0.549} & \multirow{2}{*}{10,000} & \multirow{2}{*}{74.2} & \multirow{2}{*}{68.4} & \multirow{2}{*}{52.8} & \multirow{2}{*}{10} \\  & + HOG + BPNN & & & & & & \\ \hline Chaos dynamic error scatter & \multirow{2}{*}{3673} & \multirow{2}{*}{2.34} & \multirow{2}{*}{50} & \multirow{2}{*}{84.8} & \multirow{2}{*}{80.4} & \multirow{2}{*}{78.8} & \multirow{2}{*}{6} \\  & + HOG + ENN & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results of recognition methods. Figure 21: Performance test results of CNN models with different pooling methods. From the perspective of recognition methods, in the case of the same feature extraction method, CNN and VGG 16 had higher recognition rates than the other methods. The SDP with CNN had the highest recognition rate. Typically, when the SDP or chaos dynamic error scatter diagram was used, the SVM needed the least training time. However, it had lower accuracy than CNN and VGG 16. The chaos dynamic error scatter diagram, combined with VGG 16, had a higher recognition rate than CNN. However, the recognition rate was lower than that of SDP with CNN. When the SDP or chaos dynamic error scatter diagram was used, the CNN had a shorter training time than VGG 16. Therefore, the method proposed in this study could extract fault features effectively, reduce the training cost and increase the recognition accuracy. In this study, the training data of each model were increased to 300 and 400 for comparison. The test results are shown in Figure 22. The findings indicate that even if the total number of training data was increased to 2000, the recognition accuracy did not increase significantly.