Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach
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Ledmased Laboratory, University of Laghouat, 03000, Algeria
(LACoSERE) University of Laghouat, 03000, Algeria
IREENA, Saint Nazaire, Polytech’Nantes, France
Submission date: 2020-06-12
Final revision date: 2021-01-07
Acceptance date: 2021-02-03
Online publication date: 2021-02-04
Publication date: 2021-02-04
Corresponding author
Birame M'hamed   

University of Laghouat
Diagnostyka 2021;22(1):57-63
This work attempts to clarify the potentials of Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to partial rupture rotor bar of the cage induction motor. Afterward the LS-SVM approach is established as monitoring system to detect the degree of broken rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.
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