Mitigating Multicollinearity in Induction Motors Fault Diagnosis Through Hierarchical Clustering-Based Feature Selection

Abstract

This paper addresses the challenge of multicollinearity among input features in induction motor (IM) fault diagnosis, which often degrades the performance and reliability of machine learning classifiers. A novel feature selection approach based on agglomerative hierarchical clustering (AHC) is proposed to mitigate feature redundancy and enhance model generalization. The method is applied using only voltage and current signals, excluding vibration or temperature data, to improve noise immunity and facilitate practical deployment. Experimental validation demonstrates the effectiveness of the AHC framework across multiple classifiers, particularly Support Vector Classifiers (SVCs) and Artificial Neural Networks (ANNs). Compared to random forest-based feature selection, AHC yields a 2% increase in accuracy for SVCs and a 0.6% improvement for ANNs. Moreover, both classifiers exhibit enhanced balance across fault categories, with macro-average recall and F1-score improvements of approximately 1.5%. These findings highlight the ability of AHC to handle complex fault scenarios, which offer a more efficient and generalized fault diagnosis model compared to ensemble methods-based feature selection.