## 6 Exploration of Findings  
Figure 6 presents a comprehensive summary of the harmonic profiles for voltage and current phases. The analysis of the harmonic spectra reveals that, across all three phases, the first harmonic magnitudes of voltage and current remain relatively stable in most cases, except for the current spectrum under the Single-Phase Fault condition. As clearly observed, Single-Phase Fault on phase 2 drives phase 2 current to zero. The spectrum thus collapses (bars near \(10^{-4}\) A). Meanwhile, the fundamental harmonics in the other phases show a relative increase, and the non-fundamental harmonics exhibit noticeable spikes in magnitude. On the other hand, in all phases, the third and fifth harmonics consistently dominate the non-fundamental components with relatively small differences under different scenarios. Notably, THD for voltage (VTHD) remains below 2.5 percent in healthy and minor defect scenarios but exceeds 2 percent under a front defect of 5 mm and reaches up to 2.3 percent in phase 2 only. Current THD (ITHD) remains under 3 percent for all defects but spikes to 27 percent in phase 2 under \"Single-Phase Fault,\" clearly signaling severe unbalance [35]. Comparing HVF to VTHD, the HVF values (\(10^{-3}\)-\(10^{-2}\) percent) are much smaller but still reliably track relative distortions, as can be clearly seen in Figure 6. Overall, the harmonic profiles, along with the THD and HVF metrics, capture subtle variations across  
Figure 6: Harmonic spectra of voltage and current for all three phases under various operating conditions. different cases; these variations form the basis upon which the adopted classifier accurately predicts the actual states of the IM. On another note, to ensure a comprehensive evaluation, the three previously mentioned estimators (RFC, ANN, and SVC) are trained and tested under different scenarios. The effectiveness of the presented method of feature selection against the well-stablished method (feature importance trait of random forest classifier) has been evaluated. As discussed earlier, the hyperparameter of the adopted classifiers were optimized using SVA. Consequently, the SVC employed an RBF kernel with a penalty parameter \(C=1000\) and kernel coefficient \(\gamma=0.001\). The MLP-based ANN was configured with one hidden layer of 150 neurons, an L2 regularization term of \(\alpha=0.0001\), and a maximum of 1000 iterations. For the RFC, the model used 10 decision trees, with a minimum of five samples per leaf and a minimum of two samples required to split an internal node. These settings were selected to balance model complexity and generalization performance across varying fault diagnosis scenarios. In a different context, to recognize the importance of feature selection, Table 2 demonstrates the performance of the three selected classifiers using all 63 features extracted from voltage and current signals. Various performance measures, including accuracy, precision, recall, and the F1 score, were used to evaluate the effectiveness of each estimator, as reported in Table 2. The performance of the SVC was the worst, with an accuracy of approximately 15.43%. This unsatisfactory performance can be directly attributed to the classifier's sensitivity to irrelevant and noisy features, which adversely affected its ability to generalize from the data. Additionally, the SVC required approximately 66 s for training. On the contrary, the ANN demonstrated significantly better performance compared to the SVC, achieving an accuracy of 77.34%, albeit still being disrupted by the presence of noisy or irrelevant features. However, the training time for the ANN was notably longer, at approximately 215 s. The longer fitting time of the ANN reflects the complexity and computational demands of the model. The RFC outperformed both the SVC and the ANN. The classifier achieved an accuracy of 99.192%. Given the inherent structure of the RFC, its superior performance is expected and logically acceptable.