A Review of Predictive Maintenance Methods for Electrical Machines Using Machine Learning Algorithms and Sensor Data

This framework evaluates predictive‑maintenance (PdM) strategies for induction motors by linking traditional diagnostic methods with modern machine‑learning algorithms. Induction motors are essential parts of industrial systems; unexpected failures cause large downtime and high costs. To reduce these risks, the study examines classic signal‑processing techniques—Motor Current Signature Analysis (MCSA) and Park’s Vector Analysis (PVA)—alongside supervised machine‑learning models such as Support Vector Machines (SVM). The approach targets key motor problems, including broken rotor bars, bearing defects, and stator winding faults, under various loading conditions. It looks at changes in electrical and mechanical signals—current, voltage, slip, and vibration—to create a systematic method for early fault detection. The research also builds simulation models to

test the classification accuracy of different machine‑learning classifiers. This comparison helps engineers pick the most effective diagnostic tools, improving the reliability and efficiency of electrical machines in automated industrial settings.

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