Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
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Ledmased Laboratory, University of Laghouat, 03000, Algeria
Department of Electrical Engineering, Kasdi Merbah University, Ouargla, Algeria
(LACoSERE) University of Laghouat, 03000, Algeria
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   

Diagnostyka 2020;21(4):35–41
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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