## 3 Experimental Setup
### Test Result  
Figure 13 visualizes the confusion matrices for the six encoding methods (RGB, DWT, STFT for 3 Phase; linear, DWT, STFT for Id) applied to the test set (270 samples each for oral, ITSC, and 4 Turn), allowing a comparative analysis of models' prediction accuracy. Figure 12: Validation result comparison between 3 Phase-DWT and Id-STFT. Figure 13: Confusion matrix results for the six encoding methods: (**a**) 3 Phase-RGB; (**b**) 3 Phase-DWT; (**c**) 3 Phase-STFT; (**d**) Id-Linear; (**e**) Id-DWT; (**f**) Id-STFT. Table 3 presents the test accuracy derived from the confusion matrices in Figure 13. Accuracy was calculated using the formula (TP + TN)/(total number of samples). Among the three-phase image encoding methods, 3 Phase-RGB achieved the highest classification accuracy of 99.01%, correctly predicting 270 Normal, 266 ITSC, and 266 4 Turn samples. Similarly, among the single-channel (Id) methods, Id-STFT achieved the same accuracy of 99.01%, demonstrating accurate predictions across all fault types and indicating an effective learning of class boundaries. In contrast, 3 Phase-STFT exhibited significant misclassifications: 101 ITSC samples were misclassified as 4 Turn, and 89 4 Turn samples were misclassified as ITSC. Among the Id-based methods, Id-DWT showed the poorest performance, with 80 ITSC samples misclassified as 4 Turn, and 53 4 Turn samples misclassified as ITSC. Additionally, 26 Normal samples were misclassified as ITSC, 20 Normal as 4 Turn, 27 ITSC as Normal, and 20 4 Turn as Normal--highlighting an overall decline in classification accuracy. Table 4 presents a quantitative comparison of classification performance in terms of precision, recall, and F1-score for the three fault classes (Normal, ITSC, 4 Turn), comparing both 3 Phase and Id-based encoding methods. Among the three-phase methods, RGB and DWT demonstrated a strong and consistent diagnostic performance, achieving F1-scores above 97% across all classes. Conversely, 3 Phase-STFT showed a significantly lower performance, with F1-scores of 64.02% and 65.58% for ITSC and 4 Turn classes, respectively. Among the Id-based methods, Id-STFT outperformed all the others, achieving precision, recall, and F1-scores above 98.52% for all classes. In contrast, Id-DWT yielded relatively low scores, ranging between 66% and 83%, across all metrics and classes. In summary, Id-STFT effectively balances computational efficiency--through grayscale input--and a high diagnostic performance, even outperforming RGB-based models in some cases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Data** & **Encoding** & **Normal** & **ITSC** & **4 Turn** \\ \hline \multirow{4}{*}{Precision} & \multirow{3}{*}{3 Phase} & DWT & 100.00\% & 97.06\% & 97.76\% \\  & & RGB & 100.00\% & 98.52\% & 98.52\% \\  & & STFT & 100.00\% & 65.50\% & 64.18\% \\ \cline{2-6}  & \multirow{3}{*}{Id} & DWT & 82.66\% & 67.36\% & 66.33\% \\  & & Linear & 99.26\% & 93.80\% & 90.71\% \\  & & STFT & 100.00\% & 98.52\% & 98.52\% \\ \hline \multirow{4}{*}{Recall} & \multirow{3}{*}{3 Phase} & DWT & 100.00\% & 97.78\% & 97.04\% \\  & & RGB & 100.00\% & 98.52\% & 98.52\% \\  & & STFT & 100.00\% & 62.59\% & 67.04\% \\ \cline{2-6}  & \multirow{3}{*}{Id} & DWT & 82.96\% & 60.37\% & 72.96\% \\  & & Linear & 100.00\% & 89.63\% & 94.07\% \\  & & STFT & 100.00\% & 98.52\% & 98.52\% \\ \hline \multirow{4}{*}{F1-Score} & \multirow{3}{*}{3 Phase} & DWT & 100.00\% & 97.42\% & 97.40\% \\  & & RGB & 100.00\% & 98.52\% & 98.52\% \\ \cline{1-1}  & & STFT & 100.00\% & 64.02\% & 65.58\% \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{Id} & DWT & 82.81\% & 63.67\% & 69.49\% \\ \cline{1-1}  & & Linear & 99.63\% & 91.67\% & 92.36\% \\ \cline{1-1}  & & STFT & 100.00\% & 98.52\% & 98.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance comparison based on precision, recall, and F1-score.