An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor

被引:0
|
作者
Lale, Timur [1 ]
Gumus, Bilal [1 ]
机构
[1] Dicle Univ, Dept Elect Elect Engn, Diyarbakir, Turkiye
关键词
Turn fault detection; Machine learning; Permanent magnet synchronous motor; Torque analysis; INTERTURN FAULTS; DIAGNOSIS METHOD; STATOR; PMSM;
D O I
10.1007/s00202-023-02113-w
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:2865 / 2876
页数:12
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