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

Author:

Ma Jie12ORCID,Liu Xiaodong1,Hu Jisheng1,Fei Jiyou1,Zhao Geng1,Zhu Zhonghuan3

Affiliation:

1. College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China

2. Liaoning Railway Vocational and Technical College, Jinzhou 121000, China

3. Shenyang EMU Depot, China Railway Shenyang Group Co., Ltd., Shenyang 110179, China

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.

Funder

Liaoning Provincial Department of Education

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Reference52 articles.

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3. Analysis and Comparison of Locomotive Traction Motor Intelligent Fault Diagnosis Methods;Chen;Appl. Mech. Mater.,2011

4. Al-Ameri, S.M., Alawady, A.A., Yousof, M.F.M., Kamarudin, M.S., Salem, A.A., Abu-Siada, A., and Mosaad, M.I. (2022). Application of Frequency Response Analysis Method to Detect Short-Circuit Faults in Three-Phase Induction Motors. Appl. Sci., 12.

5. Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains;Chao;IEEE Trans. Power Electron.,2020

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