An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This article presents a novel approach based on the electromechanical torque signal for the inter-turn short-circuit fault (ISCF) detection and the ISCF severity estimation in permanent magnet synchronous motors (PMSMs). The electromechanical torque data have been obtained experimentally in the healthy condition and in three various states of the ISCF at various load rates and at various operating speeds. To extract the features to be used in the ISCF diagnosis, the fast Fourier transform (FFT) implemented to the torque signal. The torque's second and fourth harmonics were found to be new turn fault features that could be used for ISCF diagnosis. These features were used to train and test the classification algorithms. Four classification algorithms were used to detect ISCF and determine the severity of ISCF: decision trees (DT), artificial neural networks (ANN), K-nearest neighbor (KNN) and support vector machines (SVM). Classification accuracies of 100%, 99.30%, 97.91% and 95.48% were achieved by the ANN, SVM, KNN and DT classifiers, respectively. High accuracy ISCF detection and high accuracy ISCF severity estimation were performed using the developed diagnostic method based on the torque signal.
Açıklama
Anahtar Kelimeler
Turn Fault Detection, Machine Learning, Permanent Magnet Synchronous Motor, Torque Analysis
Kaynak
Electrical Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Lale, T. ve Gümüş, B. (2023). An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor. Electrical Engineering, 1-12.
