Unsupervised Feature Space Analysis for Robust Motor Fault Diagnosis Under Varying Operating Conditions

Abstract

Reliable fault diagnosis of induction motors from current signals is critical for preventing failures in industrial systems. However, deep learning models often exhibit performance degradation when the torque load and other operating conditions change. Although a lot of research has been completed on supervised fault classification using current signals, the investigation of the behaviour of these datasets for unsupervised learning has not been done. This study quantifies and analyses the “shadowing effect” of operational variability, demonstrating that a baseline 1D-CNN achieving 100% accuracy under static 0 Nm loads drops to 53.19% accuracy when subjected to 4 Nm load in the KAIST dataset using a stator current. Similar trends were validated using the Paderborn University (PU) bearing dataset. Using 1D-CNN feature extraction followed by Principal Component Analysis (PCA), t-SNE, and hierarchical clustering, we show that standard linear mitigation strategies, such as removing high-variance principal components, are ineffective because fault and load features are deeply entangled. Hierarchical clustering analysis confirms that the feature space is organised by load dominance, with the primary tree split consistently occurring by torque load rather than fault type. Crucially, we identify that internal geometric metrics, such as “spread” and “diameter”, correlate with external purity metrics like the proposed “Dominance Score”. The findings establish a quantitative basis for developing unsupervised, load-invariant diagnostic models that utilise geometric stopping criteria to isolate fault clusters without using ground-truth labels.