Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques

This paper presents an experimental investigation into the detection and classification of broken rotor bar (BRB) faults in a 1.1 kW squirrel cage induction motor (IM) across various load conditions and fault severities: 1.5 BRBs, 2 BRBs, 2.5 BRBs, and 3 BRBs. Motor current signature analysis (MCSA), fast Fourier transform (FFT), and the extended Park's vector approach (EPVA) were used to explore the frequency spectra and identify characteristic fault frequencies (CFFs) associated with BRB faults. Following these exploration, the extended Park's vector (EPV) current was used to calculate 15 statistical time-domain features, which underwent exploratory data analysis using principal component analysis (PCA), curvilinear component analysis (CCA), and independent component analysis (ICA), deducing the intrinsic dimensionality to 3. Thereafter, classification was carried out using both neural and non-neural approaches to assess healthy signature as well as BRB fault severities. The PCA-SDNN model achieved the highest accuracy, showcasing its suitability for accurate, real-time fault detection in industrial IMs. This study demonstrates the effectiveness of integrating MCSA, EPVA, dimensionality reduction, and machine learning for robust IM fault diagnosis.