The diagnosis method of stator winding faults in PMSMs based on SOM neural networks

被引：18

作者：

Cao Chuang ^{[1
]}

Zhang Wei ^{[2
]}

Wang Zhifu ^{[1
]}

Li Zhi ^{[1
]}

机构：

[1] Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China

[2] China Automot Technol & Res Ctr, Tianjin 300300, Peoples R China

来源：

8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016) | 2017年 / 105卷

基金：

对外科技合作项目（国际科技项目）;

关键词：

fault diagnosis; wavelet transform; Self-organizing feature map; artificial intelligence;

D O I：

10.1016/j.egypro.2017.03.663

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, the diagnosis method based on wavelet and neural network is proposed. This method needs not to collect a large number of data, but simplifies the diagnostic process while ensuring the accuracy of diagnostic result. The three-phase stator current data were decomposed by db6 wavelet function. It does not require the introduction of additional detection equipment, but also avoid the intrusion detection that may destruct the motor. This study has significance in engineering application to the development of on-line diagnosis system. The research on fault diagnosis system will promote the development of electric vehicle industry. As the improvement of safety control, it will accelerate the popularization of electric vehicles. (C) 2017 The Authors Published by Elsevier Ltd.

引用

页码：2295 / 2301

页数：7

共 12 条

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