Interpretable Machine Learning-Based Diagnosis of Mechanical Faults in Induction Motors Using Current Features

The study presents an approach to diagnosing mechanical faults in an induction motor drive based on phase current analysis. Experimental data were obtained on a dedicated test bench for the induction motor drive under two operating conditions-no-load and 50% of nominal load-and two technical conditions: healthy, faulty with a “load misalignment” defect, and faulty with a “worn bearing” defect. The preprocessing of current signals included filtering, envelope extraction, and normalization. Statistical features such as root mean square value, kurtosis and skewness coefficients were extracted from the original current waveforms to reflect changes in the waveform shape and distribution under different fault conditions. Additionally, spectral characteristics and integral indicators were calculated from the current envelopes to describe the relative contribution of harmonic components and the fundamental harmonic, allowing for the analysis of signal modulation features associated with various mechanical faults. A classification model,based on the extracted features andusing the Random Forest (RF) algorithm with an average accuracy of 85% under cross-validation,was trained. Feature importance analysis was performed using the SHAP(Shapley Additive exPlanations) method, followed by physical interpretation to enable establishing the relationships between specific features and fault types and confirm the validity of the selected diagnostic indicators. The obtained results confirmed the effectiveness of the classical Random Forest algorithm for diagnosing mechanical faults in induction motor drives, identified the most sensitive features to changes in motor technical condition, and demonstrated the potential for application in condition monitoring and equipment health assessment systems.