Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Abstract

This paper proposes a high-resolution motor-current-signature-analysis method and a supervised learning algorithm for the detection and classification of broken rotor bars and broken end-ring connectors in 3-phase induction motors. Signature analysis relies on the matrix pencil method (MPM), a well-known model-based parameter estimation technique, to extract representative features or signatures from stator current signals. Extracted feature vectors are subsequently used to train off-line a support vector machine classifier. Once trained, the classifier is tested on a benchmark dataset of simulated stator current signals representing healthy and faulty rotors with the aim of classifying the underlying motor condition. The obtained results validate the matrix pencil method as a feature extraction method and show that the trained classifier achieves a 100% success rate in identifying the number of broken bars and connectors. Moreover, the advantages of the matrix pencil method over fast Fourier transform are demonstrated using experimental data.