Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

• Autorzy: Pietrzak Przemysław , Wolkiewicz Marcin

Identyfikatory

DOI

10.24425/bpasts.2024.150202

• Języki publikacji: EN

Abstrakty

EN

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, research has been increasingly focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were conducted to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

Słowa kluczowe

EN

fault diagnosis; interturn short circuits; continuous wavelet transform; convolutional neural networks; permanent magnet synchronous motor

PL

diagnostyka usterek; zwarcia międzyzwojowe; ciągła transformacja falkowa; splotowe sieci neuronowe; silnik synchroniczny z magnesami trwałymi

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 5
• Strony: art. no. e150202
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Pietrzak Przemysław

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

• https://orcid.org/0000-0002-4429-0009

autor

Wolkiewicz Marcin

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

• https://orcid.org/0000-0003-1197-8517

Bibliografia

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