Stator winding fault diagnosis of induction motor operating under the field-oriented control with convolutional neural networks

• Autorzy: Skowron M. , Wolkiewicz M. , Tarchała G.

Identyfikatory

DOI

10.24425/bpasts.2020.134660

• Języki publikacji: EN

Abstrakty

EN

In this paper deep neural networks are proposed to diagnose inter-turn short-circuits of induction motor stator windings operating under the Direct Field Oriented Control method. A convolutional neural network (CNN), trained with a Stochastic Gradient Descent with Momentum method is used. This kind of deep-trained neural network allows to significantly accelerate the diagnostic process compared to the traditional methods based on the Fast Fourier Transform as well as it does not require stationary operating conditions. To assess the effectiveness of the applied CNN-based detectors, the tests were carried out for variable load conditions and different values of the supply voltage frequency. Experimental results of the proposed induction motor fault detection system are presented and discussed.

Słowa kluczowe

EN

diagnostics; stator faults; field-oriented control; convolutional neural networks

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 5
• Strony: 1039--1048
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Skowron M.

• Wrocław University of Science and Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Wolkiewicz M.

• Wrocław University of Science and Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Tarchała G.

• Wrocław University of Science and Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

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