## 3 Case Study
### Experimental Results Analysis  
The obtained results of all the compared methods are given in Table 5. The training losses, training accuracy, and confusion matrix of all the compared methods are given in Figure 17.  
In this study, 11 classical outstanding networks are compared. Figure 17 and Table 5 show that (1) for the EV electric-system failure data, most of the models reach the convergence state with a good trend; among these, the proposed PSDSEF converges faster than the other networks and has less training loss; (2) the accuracy, Macro-Precision, Macro-Recall, and Macro-F1 score of the proposed PSDSEF reach 97.22%, 97.59%, 97.38%, and 97.38%, respectively; the four indicators of the PSDSEF are higher than all the comparison networks; this is compared to the comparison networks in which the effect belongs to ResNet101, which has the most significant effect among the comparison networks; the indicators of the PSDSEF are improved by 5.03%, 3.63%, 4.84%, and 5.23%, respectively; by contrast, the training time of the model is only 10,680 s, which is faster than the average training time of the comparison networks by 5014 s; (3) offline learning is utilized for training. When the deep network framework is trained, it takes only a little time to categorize the faults on the test set. It can be seen that most of the networks take less than 5 s to test, while the proposed PSDSEF takes only 1.62 s to test, which is only 0.58 s more than the network Places365 GoogLeNet, which takes the least amount of time; (4) from the confusion matrix, it can be seen that the overlap between the classification results and the actual values of the proposed method in this study is the highest among all the networks and the classification accuracy is the highest.  
The experiments show that the classification effect of the proposed PSDSEF with higher accuracy and less training time is better than that of the comparison networks.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **Macro-P (\%)** & **Macro-R (\%)** & **Macro-F1 (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline PSDSEF & 97.22 & 97.59 & 97.38 & 97.38 & 10,680 & 1.62 \\ DarkNet53 & 91.43 & 93.26 & 91.87 & 91.61 & 14,234 & 2.45 \\ ResNet50 & 91.69 & 93.25 & 92.07 & 91.76 & 12,316 & 1.61 \\ ResNet101 & 92.19 & 93.96 & 92.54 & 92.15 & 21,048 & 2.39 \\ DenseNet201 & 89.21 & 91.85 & 89.72 & 88.41 & 18,951 & 6.44 \\ NasNetLarge & 88.80 & 90.40 & 89.32 & 89.11 & 58,613 & 14.17 \\ Xception & 70.96 & 77.71 & 72.40 & 70.59 & 14,984 & 2.70 \\ EfficientNetB0 & 80.55 & 84.51 & 81.47 & 80.34 & 5978 & 3.63 \\ Places365 GoogLeNet & 90.17 & 93.09 & 90.53 & 90.36 & 3568 & 1.04 \\ ShuffleNet & 86.49 & 90.19 & 87.09 & 86.15 & 4010 & 1.61 \\ InceptionV3 & 65.75 & 74.44 & 67.55 & 65.77 & 13,216 & 3.33 \\ MobileNetV2 & 84.97 & 88.12 & 85.47 & 84.03 & 5922 & 1.94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results and evaluation indicators.  
Figure 17: _Cont._  
Figure 17: Training loss, training accuracy, and confusion matrix of each network: (**a**) training loss of ShuffleNet, ResNet50, ResNet101, DenseNet201; (**b**) training accuracy of ShuffleNet, ResNet50, ResNet101, DenseNet201; (**c**) training loss of MobileNetV2, Xception, EfficientNetB0, Place365 GoogLeNet; (**d**) training  
accuracy of MobileNetV2, Xception, EfficientNetB0, Place365 GoogLeNet; (**e**) training loss of DarkNet53, InceptionV3, NasNetLarge, PSDSEF; (**f**) training accuracy of DarkNet53, InceptionV3, NasNetLarge, PSDSEF; (**g**) confusion matrix of PSDSEF; (**h**) confusion matrix of DarkNet53; (**i**) confusion matrix of ResNet50; (**j**) confusion matrix of ResNet101; (**k**) confusion matrix of DenseNet201; (**l**) confusion matrix of NasNetLarge; (**m**) confusion matrix of Xception; (**n**) confusion matrix of EfficientNetB0; (**o**) confusion matrix of Places365 GoogLeNet; (**p**) confusion matrix of ShuffleNet; (**q**) confusion matrix of InceptionV3; (**r**) confusion matrix of MobileNetV2.