Induction Motor Inter-turn Short Circuit Fault Detection Using Efficient Feature Extraction for Machine Learning Based Fault Detectors

Abstract

Inter-turn short circuit of the stator is one of the most common faults of an induction motor that degrades its performance and ultimately causes it to break down. To avoid unexpected breakdown, causing an industrial process to halt, it is desirable to continuously monitor the motor’s operation using an automated system that can differentiate normal from faulty operation. However, such automated systems usually require large datasets containing enough examples of normal and faulty characteristics of the motor to be able to detect abnormal behavior. The aim of this paper is to present some ways to extract such information or features from the available sensor signals data like motor currents, voltages and vibration, to enable a machine learning based fault detection system to discern normal operation from faulty operation with minimal training data.