On-line fault diagnosis model for locomotive traction inverter based on wavelet transform and support vector machine

被引：29

作者：

Mei Fei ^{[1
]}

Liu Ning ^{[1
]}

Miao Huiyu ^{[2
]}

Pan Yi ^{[2
]}

Sha Haoyuan ^{[2
]}

Zheng Jianyong ^{[2
]}

机构：

[1] Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Jiangsu, Peoples R China

[2] Southeast Univ, Sch Elect Engn, Nanjing 210096, Jiangsu, Peoples R China

来源：

MICROELECTRONICS RELIABILITY | 2018年 / 88-90卷

基金：

中国博士后科学基金;

关键词：

Locomotive traction inverter; Fault diagnosis; Wavelet transform; Support vector machine;

D O I：

10.1016/j.microrel.2018.06.069

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：

A traction inverter is the power source for rail transit vehicles. An insulated-gate bipolar transistor (IGBT) is the primary component of a traction inverter. IGBT faults can cause serious problems in locomotive power supply systems. The disadvantage of traditional fault detection methods for IGBT modules is a lack of real-time processing and high efficiency. An on-line fault diagnosis method based on a wavelet transform and multi-classification support vector machine (multi-SVM) is proposed for IGBT faults. Wavelet decomposition is used to process fault current signals, and energy vectors are constructed. Multi-SVM is used to establish a fault recognition model. The validity of this method is verified by simulations with MATLAB/Simulink.

引用

页码：1274 / 1280

页数：7

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