## 7 Conclusions  
In this paper, the agglomerative hierarchical clustering (AHC)-based feature selection algorithm for induction motor (IM) fault diagnosis is investigated. The performance of AHC is compared with a random forest (RF)-based feature importance approach. Across all experiments, AHC consistently yields a compact, highly discriminative feature subset that mitigates multicollinearity and improves model generalizability. A total of 63 extracted features are derived from the voltage and current signals captured from the motor terminals.  
By employing all 63 extracted features, the RF classifier (RFC) achieved an accuracy of 99.192% with minimal training time (0.82 s). In contrast, the Support Vector Classifier (SVC) and the multilayer perceptron (ANN) attained only 15.426% and 77.338% accuracy, respectively, due to their sensitivity to redundant features. Incorporating RF-based feature selection (a subset of 23 features) noticeably improved classification: SVC increased to 96.75% (5.30 s), ANN to 98.68% (8.32 s), and RFC to 99.24% (0.72 s). These performance enhancements underscore the importance of removing irrelevant or noisy inputs, but do not guarantee that the selected subset contains the most discriminative features. On the  
Figure 14: Performance analysis of the SVC with the AHC-based feature selection method.  
Figure 15: Performance analysis of the SVC with the RF-based feature selection method.  
other hand, AHC-based selection further enhanced performance by capturing cluster-representative harmonic features. Using 23 AHC-selected features, SVC achieved an accuracy of 98.724% in 4.32 s, and ANN reached 99.257% in 14.33 s, each outperforming their RF-selected counterparts. Although RFC\({}^{\prime}\)s accuracy slightly declined to 98.869% with AHC, its training time remained low (0.74 s), which highlights the inherent robustness of RFC. Moreover, sensitivity analyses confirmed that AHC attained optimal performance with fewer features, while RF required additional features to achieve similar accuracy levels.  
These findings demonstrate that AHC effectively mitigates multicollinearity by grouping correlated harmonics and selecting representative features. Consequently, AHC enhances classification accuracy, particularly for computationally demanding models such as SVC and ANN, while maintaining computational efficiency. Future work should explore scalability to larger motor systems and the integration of time-frequency features, such as wavelet coefficients, to further improve diagnostic robustness under non-stationary conditions.  
Conceptualization, N.M.A.I.; Methodology, B.A.H.; Validation, A.A.E.-F. and N.M.A.I.; Formal analysis, B.A.H.; Data curation, S.A. and N.M.A.I.; Writing--original draft, B.A.H. and N.M.A.I.; Writing--review & editing, S.A. and A.A.E.-F.; Visualization, B.A.H.; Supervision, A.A.E.-F. and S.A.; Funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2503).  
Not applicable.  
Data supporting the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.