## 5 Test Comparison and Analysis
### Model Robustness Verification  
To mitigate overfitting caused by fixed training-test splits and ensure the robustness of the ConvNeXt model, this section incorporated a 6-fold cross-validation pipeline during training. The dataset was repeatedly partitioned and trained across six iterations, with each iteration validated on a distinct subset. Two-dimensional spectra corresponding to different winding looseness fault states at six measurement points were trained individually. For instance, taking Measurement Point 1 as a case study, the boxplot of recognition accuracy for each fold after the 50 th training epoch was generated. Analysis of Figure 18 indicates that the six-fold test sets exhibit generally excellent overall recognition accuracy, with all boxplot boxes remaining narrow--evidence of the model's robust recognition stability. Subsequently, the average recognition accuracy for each fold across the six measurement points was calculated, alongside the standard deviation of the six-fold training outcomes. The standard deviation formula is as follows:  
\[\sigma=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({x_{i}-\mu})}^{2}}}}{n}} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measuring** & **Looseness** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\
**Point** & **Grade** & & & \\ \hline \multirow{4}{*}{\(1\)} & 100\% \(F_{N}\) & 100 & 100 & 100 \\  & 75\% \(F_{N}\) & 100 & 99.5 & 99.7 \\  & 50\% \(F_{N}\) & 100 & 100 & 100 \\  & 25\% \(F_{N}\  
where \(\sigma\) denotes the volatility, \(\mu\) signifies the average value of the data, and \(n\) indicates the quantity of data. Analysis of Table 7 shows that, after six-fold cross-training, the average recognition accuracy of each measurement point dataset exhibits no significant change compared to the training results in Section 5.3, and the standard deviation is also minimal. This verifies that the ConvNeXt model has a certain degree of robustness in the division of the test set and validation set. To validate the ConvNeXt model's robustness against noise, the following section classifies noisy vibration signals. Gaussian white noise with specified power was added to the original signals to generate samples at varying signal-to-noise ratios (SNRs). These noisy signals were then converted into time-frequency-domain 2D feature maps using the spectrum construction method. The Figure 19 below compares the 2D spectrum of the original signal with that of a signal processed at 40 dB SNR. The spectral dataset with an SNR of 40 dB was fed into the ConvNeXt model for training, yielding a boxplot of recognition accuracies for the six measurement points' test sets after the 50 th training epoch. Analysis of Figure 20 reveals that Measurement Points 2, 4, and 5 exhibit higher box positions, higher medians, and lower dispersion--indicative of consistently excellent and stable test accuracy. In contrast, Points 3 and 6 show lower box positions and greater dispersion, leading to lower overall accuracy with pronounced fluctuations. Notably, after introducing Gaussian noise, the recognition accuracy of each measurement point dataset showed no significant change compared to the noise-free datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measuring Point** & **Average Recognition Accuracy** & **Standard Deviation** \\ \hline
1 & 99.427 & 0.00207 \\
2 & 99.999 & 0.00021 \\
3 & 95.439 & 0.00558 \\
4 & 98.291 & 0.00177 \\
5 & 99.523 & 0.00059 \\
6 & 92.529 & 0.00731 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The training recognition accuracy and standard deviation distribution of each measuring point. Figure 18: Test set recognition accuracy boxplot. The SNR values were sequentially varied to 40 dB, 30 dB, 20 dB, and 10 dB. Vibration spectra corresponding to different SNRs were fed into the ConvNeXt model for training and recognition, after which the average recognition accuracy across the six measurement points was calculated.