Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
1
Ledmased Laboratory, University of Laghouat, 03000, Algeria
2
Department of Electrical Engineering, Kasdi Merbah University, Ouargla, Algeria
3
(LACoSERE) University of Laghouat, 03000, Algeria
4
IREENA, Saint Nazaire, Polytech’Nantes, France
Submission date: 2020-07-06
Final revision date: 2020-11-04
Acceptance date: 2020-11-07
Online publication date: 2020-11-09
Publication date: 2020-11-09
Diagnostyka 2020;21(4):35–41
KEYWORDS
fault diagnosisinter-turn short circuitleast square support vector machine (LS-SVM))(Induction Motor
TOPICS
Diagnostic systemsDiagnostics of buildingsTechnical diagnosticsDiagnostics of electronic systemsSignal analysis
ABSTRACT
Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in MI applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.
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