J4 ›› 2012, Vol. 30 ›› Issue (6): 555-561.

• 论文 •    下一篇

基于SOM神经网络的永磁同步电机故障诊断

张袅娜, 王永庆, 李景帅   

  1. 长春工业大学 电气与电子工程学院, 长春 130012
  • 收稿日期:2011-11-25 出版日期:2012-11-23 发布日期:2013-06-05
  • 作者简介:张袅娜(1972—), 女, 长春人,长春工业大学教授,博士,硕士生导师,主要从事非线性系统控制、故障诊断研究,(Tel)86-13384308881(E-mail)zhangniaona@163.com。
  • 基金资助:

     吉林省重大科技攻关基金资助项目(10ZDGG002)

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Som Neural Networks

ZHANG Niao-na, |WANG Yong-qing, |LI Jing-shuai   

  1. College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
  • Received:2011-11-25 Online:2012-11-23 Published:2013-06-05

摘要:

 为实现永磁同步电机的故障类别的诊断, 采用小波函数根据不同频段进行故障特征提取, 进行归一化数据样本处理, 以剔除奇异样本, 利用小波函数构成SOM(Self Organizing Map)的领域函数, 形成次兴奋神经元进行权值更新, 以避免SOM的局部最优。采用实验提取的故障数据作为SOM神经网络的输入样本进行网络训练, 从而得出产生特定故障时所激发的相应神经元索引。实验结果验证了该方法的可行性和实用性。

关键词: 神经网络, 故障诊断, 神经元索引, 永磁同步电机

Abstract:

In order to achieve the diagnosis of fault categories of permanent magnet synchronous motor, fault feature is extracted by using wavelet function according to the different frequency band, then normalized date sample as the SOM(Self Organizing Map) network input, the SOM field function is constructed with the wavelet function and the second excited neurons are formed to update weights, so the local optimization of SOM is avoided. The fault data extracted is regarded as the input samples of SOM neural networks in order to train the network, and appropriate neuron index stimulated when a specific fault occurs is obtained. the feasibility has been demonstrated by simulation results.

Key words: neural network, fault diagnosis, neuron index, permanent magnet synchronous motor (PMSM)

中图分类号: 

  • TP183