## 6 Conclusions  
In this study, the time-frequency-domain feature map library of winding looseness faults under different transformer load current conditions was constructed by using the  
Figure 21: Comparison of recognition accuracy of different models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Recognition Model** & **Identification Accuracy (\%)** & **Standard Deviation** \\ \hline ConvNeXt & 97.9 & 0.0020 \\ ResNet50 & 96.7 & 0.0110 \\ GoogLeNet & 95.3 & 0.0042 \\ AlexNet & 96.1 & 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Different models’ recognition accuracy and volatility. relative position matrix and Gram angle and field. The fault diagnosis and identification model of transformer winding looseness relying on feature maps and ConvNeXt was built, and the experimental verification was carried out. The following conclusions were reached:  
(1) The time-frequency two-dimensional feature map library under three load currents of 90% \(I_{N}\), 100% \(I_{N}\), and 110% \(I_{N}\) was constructed, and the fault recognition accuracy and convergence of the test set reached the performance under the single load current condition. The recognition rate of the test set of Measuring Point 1 was as high as 99.54%; it provided sample data for the diagnosis and identification of transformer looseness faults across various load conditions, and it improved the adaptability of the model to the diagnosis of transformer winding looseness faults with various load conditions. (2) The recognition accuracy of the ConvNeXt looseness fault model corresponding to the six measuring points on the measuring surface of the transformer box was generally high, and the average recognition accuracy of all measuring points was 97.9%. (3) The ConvNeXt model improved the accuracy of transformer winding looseness fault identification by 1.2% compared with the traditional ResNet50, solving the overfitting problem of traditional model training to a certain extent, and showed significant advantages in fault classification effect. In summary, the transformer winding looseness fault identification model proposed in this paper, integrating feature spectrograms and ConvNeXt, demonstrates remarkable advantages in both recognition efficiency and robustness. This model not only provides technical support for mechanical fault diagnosis of transformer windings in operational substations but also lays a theoretical foundation for the research on fault diagnosis devices. This study still has certain limitations. First, only a single type of power transformer was used in the experiment, which cannot fully reflect the general law of transformer winding looseness. Second, only a single sensor arrangement scheme was used in the experiment, which cannot completely capture the characteristic information of transformer winding vibration. Third, image recognition for transformer winding looseness faults generally only identifies the occurrence of such faults, struggling to precisely locate the internal fault positions--such as the specific turn layer of a phase coil. Therefore, this paper proposes the following solutions:  
1. Measure the vibration data of different types of transformer windings under faults to enrich the fault spectrum database of transformer winding looseness. 2. Increase the number of sensors, and uniformly arrange sensors on the top and sides of the transformer tank to fully collect the vibration signals of the transformer. 3. Position fiber-optic sensors between the transformer winding pancakes to monitor stress variations, generate spectrograms from tank surface vibration signals to detect faults, and use fiber-optic sensor data to localize faults. Establish a correlation model between the two sensor datasets to enable the precise localization of winding looseness faults in subsequent experiments using only vibration sensor data. Conceptualization, J.F., X.D., Y.X. and Z.Z. ; Data Curation, J.F. and C.W. ; Formal Analysis, J.F., X.D., Y.X., C.W., Y.L., X.L. and K.C. ; Investigation, J.F., Y.X., C.W., Y.L. and X.L.