Induction Motor Failure Detection Using Explainable Features from Motor Currents

Induction motors are critical to the reliable operation of various mechanical systems. Motor Current Signature Analysis (MCSA) provides a non-invasive way to monitor motor health and prevent costly diagnostics and repairs resulting from mechanical and electrical faults. The conventional MCSA techniques use the Fast Fourier Transform (FFT) to analyze three-phase voltage and current signals. To improve failure detection accuracy and provide actionable insights, this work proposes a framework for explainable feature extraction and modeling. The framework is designed to extract explainable features from current signal data and train an XGBoost classifier using failure modes as the model response. SHAP (SHapley Additive exPlanations) analysis is then used to visualize the impact of the features on each failure mode. We collected training data using a Machinery Fault Simulator (MFS-Magnum) with a three-phase, 0.5 HP induction motor. Various defects were intentionally introduced into the experimental setup, including inter-turn short circuits, rotor unbalance, and broken rotor bars. By analyzing the relationship between extracted features and each failure mode, this approach facilitates the detection and identification of various impending motor failures.