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

• Autorzy: Birame M’hamed , Taibi Djamel , Bessedik Sid Ahmed , Benkhoris Mohamed Fouad

Identyfikatory

DOI

10.29354/diag/130283

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

induction motor; inter-turn short circuit; fault diagnosis; least square support vector machine; LS-SVM

PL

silnik indukcyjny; zwarcie międzyzwojowe; diagnostyka uszkodzeń

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2020
• Tom: Vol. 21, No. 4
• Strony: 35--41
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Birame M’hamed

• LEDMASED Laboratory, University of Laghouat, 03000, Algeria

autor

Taibi Djamel

• Department of Electrical Engineering, Kasdi Merbah University, Ouargla, Algeria

autor

Bessedik Sid Ahmed

• (LACoSERE) University of Laghouat, 03000, Algeria

autor

Benkhoris Mohamed Fouad

• IREENA, Saint Nazaire, Polytech’Nantes, France

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).