Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

Jie Ma, Xiaodong Liu, Jisheng Hu, Jiyou Fei (), Geng Zhao and Zhonghuan Zhu
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Jie Ma: College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
Xiaodong Liu: College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
Jisheng Hu: College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
Jiyou Fei: College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
Geng Zhao: College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
Zhonghuan Zhu: Shenyang EMU Depot, China Railway Shenyang Group Co., Ltd., Shenyang 110179, China

Energies, 2023, vol. 16, issue 15, 1-16

Abstract: EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples.

Keywords: ITSC fault; traction motor; fault diagnosis; apFFT; SVM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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