## 1 Introduction
### Current Signature-Based Fault Diagnosis
In a few words, regardless of the source from which the features are extracted, the difficulty in identifying the most influential features remains the major issue. If not adequately addressed, high feature correlation and redundancy can lead to overlapping or redundant information, exacerbating the risk of multicollinearity, a condition where highly correlated features compromise the performance of the IM diagnostic model. While several studies have explored feature selection techniques, they often fail to reduce intercorrelations among features chosen explicitly. In fact, many existing methodologies deliberately retain correlated features to maximize accuracy in training performance, yet this approach risks hindering model generalization and limiting applicability in real-world industrial environments. To overcome this issue, this study proposes a nonintrusive, industry-oriented fault diagnosis strategy that leverages agglomerative hierarchical clustering (AHC) in conjunction with the Spearman rank-order correlation coefficient for feature selection. While hierarchical clustering and clustering-based feature selection have been explored across various domains, including biomedical signal processing (e.g., references [30; 31; 32; 33]) and vibration-based fault diagnosis (references [4; 9]), these existing methods do not fully address the specific challenge of multicollinearity in harmonic-based fault features within induction motor (IM) diagnostics. By systematically quantifying feature dependencies and eliminating redundancy, the proposed method directly addresses the challenges posed by multicollinearity. Moreover, while numerous fault types exist, both mechanical and electrical, a technical report issued by the Electric Power Research Institute (EPRI) indicates that approximately 70% of IM failures result from either bearing defects (~40%) or stator faults (~30%), including single-phase loss [8; 26]. Therefore, this study focuses on detecting mechanical faults using electrical signal analysis as a nonintrusive alternative to conventional vibration and thermal approaches. Additionally, experimental validation using the NI CRIO-9056 platform confirms the effectiveness of the developed technique, while comparative analysis against established techniques, including random forest-based feature selection, demonstrates its advantages in improving classification robustness and minimizing overfitting risks. Novel Contributions in the Context of Existing Approaches:  
1. Unlike many prior studies that rely on vibration or acoustic signals (as in [1; 2; 4; 9]), the proposed work in this study leverages voltage and current phasor measurements to diagnose faults in IMs. This nonintrusive method eliminates the need for expensive or difficult-to-install sensors while ensuring minimal disruption to ongoing industrial processes. 2. Most conventional fault diagnosis methods rely on statistical [28; 30; 34] or deep feature extraction techniques [7; 9; 27] applied to vibration or acoustic data for bearing fault detection. In contrast, the proposed approach leverages harmonic-based features extracted from voltage and current phasors. This methodology captures subtle variations that indicate bearing-related faults. 3. While hierarchical or clustering-based methods have been used in other domains (e.g., [31; 32] use k-means clustering for feature selection, and [33] applies hierarchical clustering in cancer diagnosis), this work is distinct because it directly targets the multicollinearity challenge in IM fault diagnosis, a crucial factor that many existing methods do not explicitly address. 4. The practical viability of the developed method is demonstrated via validation on the NI CRIO-9056 platform, confirming its applicability in real-world scenarios. Furthermore, this study provides a rigorous comparative analysis against established feature selection techniques (such as random forest feature selection) and evaluates performance using three high-performance estimators (RFC, ANN, and SVC).