## 5 Test Comparison and Analysis  
### Construction of Two-Dimensional Feature Map Data Samples  
Before the image is transmitted to the ConvNeXt network for training, it is necessary to construct a sample library for the generated time-frequency-domain feature map. The vibrations of 100% \(F_{N}\), 75% \(F_{N}\), 50% \(F_{N}\), and 25% \(F_{N}\) loosening faults of the three-phase winding of the transformer are collected in the test; the signals of 90% \(I_{N}\), 100% \(I_{N}\), and 110% \(I_{N}\) load conditions are collected for each loosening fault; and the time-frequency two-dimensional characteristic spectrum is constructed. In order to ensure that each image contains complete periodic feature information, the original vibration signal time series is processed by continuous slicing; each slice contains 4000 sampling points. The image sample set generated in this way contains three load current conditions under each loose fault state. The image samples generated by the winding loose fault signal corresponding to each measuring point are 11,964, and the size of each image sample is 224 \(\times\) 224 \(\times\) 3. Figure 13 shows some image sample sets under different loose fault states of transformer windings. Comparative Analysis of Transformer Winding Loose Fault Identification Under Different Load Current Conditions  
For the purpose of researching the precision of recognizing transformer winding looseness faults in different load situations, the time-frequency-domain feature maps generated under three load conditions of 90% \(I_{N}\), 100% \(I_{N}\), and 110% \(I_{N}\) of the transformer were input into ConvNeXt for training. A total of 3988 images were generated under each load current condition. The samples under the load condition were partitioned into a training set and test set at a ratio of 5 to 1, and there were 100 training rounds. Taking Measuring Point 1 as an example, the change curves of recognition accuracy and loss value  
Figure 13: Partial image datasets of transformer windings in different loose states. of the test set were obtained as shown in Figure 14 below, the recognition accuracy under different load conditions is shown in Table 4.  
From the training correlation curves of transformer winding looseness identification under different load current conditions, it is evident that regarding the identification accuracy of the test set, the three load conditions of 90% \(I_{N}\), 100% \(I_{N}\), and 110% \(I_{N}\) all reached more than 99% after the 50 th round of testing, and the accuracy of test set identification under the mixture of the three load conditions also reached 99.54%. In terms of the convergence performance of the loss function, the convergence performance of the test set under the three load current conditions had a significant advantage, and the loss value tended toward 0, while the other three load current conditions had different degrees of fluctuation in the loss value when they were trained separately. In summary, the two-dimensional images under three load conditions of 90% \(I_{N}\), 100% \(I_{N}\), and 110% \(I_{N}\) not only enriched the dataset in the identification of transformer winding looseness faults but also achieved very high recognition accuracy and good convergence. The T-distributed Stochastic Neighbor Embedding (T-SNE) dimension reduction processing [40] was performed on the fault classification and recognition feature quantity at Measuring Point 1, and the visual 3D effect diagram shown below was obtained. As shown in Figure 15, it can be seen that each loose fault classification shows good independence, of which the 75% \(F_{N}\) loose fault classification effect is better. To verify the sensitivity of the recognition accuracy of the ConvNeXt model to the number of image samples, 80%, 60%, and 50% of the spectrum samples from Measurement Point 1 were extracted and input into the model for training and recognition, with the number of training epochs set to 100.