Open AccessRotor fault diagnosis in induction motors by the matrix pencil method and support vector machine
Author(s) -
Chahine Khaled
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2612
Subject(s) - stator , classifier (uml) , matrix pencil , pattern recognition (psychology) , artificial intelligence , computer science , support vector machine , induction motor , feature extraction , feature vector , engineering , voltage , physics , eigenvalues and eigenvectors , quantum mechanics , mechanical engineering , electrical engineering
Summary 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.