Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
 
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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
 
 
Corresponding author
Birame M'hamed   

LEDMASED LABORATORY, UNIVERSITY OF LAGHOUAT, 03000, ALGERIA
 
 
Diagnostyka 2020;21(4):35-41
 
KEYWORDS
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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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