Interacting Multiple Model Framework for Incipient Diagnosis of Interturn Faults in Induction Motors

Abstract

This work introduces a novel online signal processing and machine learning (ML) framework designed for the incipient diagnosis of stator interturn faults (SITF) in three-phase squirrel cage induction motors. Addressing the critical need for incipient fault detection to prevent severe motor damage, the framework focuses on motor speed estimation, incipient fault detection, fault severity estimation, and faulty phase identification using only stator currents. A distinctive contribution lies in the proposed interacting multiple model (IMM) framework that leverages carefully selected motor current signatures as features, offering a comprehensive strategy for stator fault diagnosis not explored previously. The article pioneers the use of the selected harmonics with ML models to estimate a fault severity indicator, which is developed based on insights from the motor's physics of failure. Experimental validation showcases the fault indicator's effectiveness under diverse operating conditions, demonstrating its utility in fault severity assessment. Suitable standalone ML model is selected, or an ensemble is constructed from a pool of ML models at each stage of the IMM framework. Further, a feature relevance analysis is also performed to garner insights into the contributions of each handpicked feature in predicting the fault indicator.