Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

被引：0

作者：

BIRAME M. ^{[1
,3
]}

TAIBI D. ^{[2
,3
]}

BESSEDIK S.A. ^{[3
]}

BENKHORIS M.F. ^{[3
,4
]}

机构：

[1] LEDMASED Laboratory, University of Laghouat

[2] Department of Electrical Engineering, Kasdi Merbah University, Ouargla

[3] LACoSERE) University of Laghouat

[4] IREENA, Polytech'Nantes, Saint Nazaire

来源：

Diagnostyka | 2020年 / 21卷 / 04期

关键词：

(Induction Motor; Fault diagnosis; Inter-turn short circuit; Least square support vector machine (LS-SVM));

D O I：

10.29354/diag/130283

中图分类号：

学科分类号：

摘要：

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 IM 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. © 2020 Polish Society of Technical Diagnostics. All rights reserved.

引用

页码：35 / 41

页数：6

共 20 条

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