MATHEMATICAL MODELING APPROACHES FOR PREDICTING ELECTRIC MOTOR FAILURES THROUGH ELECTROMAGNETIC SIGNALS

Abstract

This article proposes mathematical modeling approaches for early detection and prediction of failures in industrial electric motors. The research focuses on analyzing electromagnetic signals to identify potential issues and develop proactive maintenance strategies. The model, based on wavelet analysis and artificial neural networks, demonstrated over 92% accuracy in detecting faults in motor bearings, rotor bars, and stator windings. This study offers innovative solutions to reduce maintenance costs and enhance the reliability of industrial systems.