## 5 Test Comparison and Analysis
### Construction of Two-Dimensional Feature Map Data Samples
The distribution of the recognition accuracy of the test set samples at the 100 th epoch is shown in Table 5 below. \begin{table}
\begin{tabular}{c c} \hline
**Different Load Conditions** & **The 100 th Round of Recognition Accuracy (\%)** \\ \hline
90\% \(I_{N}\) & 99.25 \\
100\% \(I_{N}\) & 100 \\
110\% \(I_{N}\) & 100 \\
90\% \(I_{N}+100\%\)\(I_{N}+110\%\)\(I_{N}\) & 99.54 \\ \hline \end{tabular}
\end{table} 
Table 4: Recognition accuracy under different load conditions. Figure 14: The training process of transformer winding looseness identification under different load currents: (**a**) Test set recognition accuracy. (**b**) Test set loss value change. Analysis of the table reveals that when the number of spectrum samples drops to 80%, the 100 th round of recognition accuracy of Measurement Point 1 decreases by nearly 3 percentage points. When the sample quantity is halved, the 100 th round of recognition accuracy falls to 85.33%. Thus, the number of samples is one of the key factors influencing the recognition accuracy of the ConvNeXt model; the more sufficient the samples, the richer the fault feature information extracted by the model. Comparative Analysis of Transformer Winding Loose Fault Identification Under Different Measuring Points  
In this section, the two-dimensional spectra of different winding loose fault states, corresponding to six measuring points, were trained and identified. Each measuring point contained vibration data in four states of 100% \(F_{N}\), 75% \(F_{N}\), 50% \(F_{N}\), and 25% \(F_{N}\), with a total of 71,784 images. The samples included in the measuring point were partitioned into a training set and test set at a ratio of 5 to 1. The data samples were fed into the ConvNeXt model for training, and the number of training rounds was set to 100. The change curves of the recognition accuracy rate and the loss value of the test set after training for each measuring point were obtained, as shown in Figure 16 below:  
Comprehensive analysis of the test set recognition accuracy and loss value transformation curve in the figure shows that the convergence of Measuring Points 1, 2, and 5 is the best in the recognition process. When the testing reaches the 10 th round, the recognition accuracy of Measuring Points 1, 2, and 5 tends to be stable, reaching 97.9%, 100%, and 97.0%, respectively. When the testing reaches 50 rounds, the loss function of Measuring Points 3 and 4 effectively reaches the convergence state, and the recognition accuracy is 94.7% and 96.9%, respectively. The convergence of the loss function at six measuring points is poor, and the loss value is 0.11. After 100 rounds of recognition on the test set, the accuracy rate reaches 93.7%, which has a relatively good classification effect. Figure 15: Classification and recognition T-SNE effect diagram. \begin{table}
\begin{tabular}{c c} \hline \hline
**The Proportion of Image Samples (\%)** & **The 100 th Round of Recognition Accuracy (\%)** \\ \hline
100 & 99.54 \\
80 & 96.77 \\
60 & 89.31 \\
50 & 85.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The distribution of model recognition accuracy under different sample numbers. Figure 17 shows the confusion matrix under the 100 th round of identification of the test set of six measuring points. Based on the confusion matrix, the classification precision, recall rate, and F1-scores for each measurement point were calculated, yielding the evaluation indicators shown in Table 6. A comprehensive analysis revealed that Measurement Points 1, 2, 4, and 5 exhibited remarkably high average recognition accuracy, demonstrating the exceptional classification capability of the ConvNeXt model. Notably, the test set images at Measurement Point 2 achieved 100% \(F_{N}\) correct classification, representing a perfect performance. For Measurement Point 3, the 75% \(F_{N}\) fault condition showed significant recognition advantages, with  
Figure 16: The training process of transformer winding looseness identification under different measuring points: (**a**) Test set recognition accuracy.