An Application of Support Vector Machines for Induction Motor Fault Diagnosis with Using Genetic Algorithm

Authors:
Ngoc-Tu Nguyen;Hong-Hee Lee
Affiliations:
School of Electrical Engineering, University of Ulsan, Ulsan, South Korea;School of Electrical Engineering, University of Ulsan, Ulsan, South Korea
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

Citing 2
Cited 0

The use of features selection and nearest neighbors rule for faults diagnostic in induction motors

Engineering Applications of Artificial Intelligence
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a technique for diagnosing mechanical faults of induction motors by using support vector machine (SVM) and genetic algorithm (GA). Features are extracted from the vibration time signals and selected by using GA with a distance evaluation fitness function. All SVM parameters are also obtained simultaneously by the same GA. The SVM is studied with two types of kernel functions, the radial basis function and the polynomial function. Four motor conditions are investigated with the chosen SVM classifiers. The classification results have high accuracy for the chosen feature set and SVM parameters.