北京航空航天大学学报 ›› 2006, Vol. 32 ›› Issue (05): 544-547.

• 论文 • 上一篇    下一篇

基于RBF神经网络的控制电器元件故障诊断

袁海文, 吕弘, 袁海斌   

  1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100083
  • 收稿日期:2005-04-19 出版日期:2006-05-31 发布日期:2010-09-20
  • 作者简介:袁海文(1968-),男,陕西扶风人,教授, yhw@buaa.edu.cn.
  • 基金资助:

    北京市自然科学基金资助项目(3042011)

Fault diagnosis of control electric component based on RBF neural network

Yuan Haiwen, Lü Hong, Yuan Haibin   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2005-04-19 Online:2006-05-31 Published:2010-09-20

摘要: 针对控制电器元件故障征兆与故障类型之间的非线性映射关系,提出了基于径向基函数神经网络RBFNN(Radial Basis Function Neural Network)的控制电器元件故障诊断方法.在分析控制电器元件故障机理和失效形式的基础上,提取出描述故障类型的典型故障特征矢量.给出在获得足够多故障信息的情况下,运用RBFNN进行故障诊断的模型及整个故障诊断算法的实现过程.为了验证故障诊断模型的有效性和合理性,利用训练好的RBFNN对故障特征矢量进行识别.仿真结果表明,RBFNN能克服诊断过程中容易陷入局部极小的缺点,并能满足故障诊断的快速性和准确性要求.

Abstract: Based on nonlinear mapping relationship between fault symptom and fault type in control electric component, RBFNN(radial basis function neural network) approach was presented for fault diagnosis. Fault mechanism and failure behavior of control electric component was analyzed, then featured fault types were extracted from control electric component failures and the extracted features were regarded as fault symptom eigenvector. The process of fault diagnosis principal, fault diagnosis model and fault diagnosis algorithm was given using RBFNN with enough fault feature information. Trained RBFNN was used for fault vector recognition and diagnosis to verify the proposed fault diagnosis model effectiveness and rationality. Simulated result shows that RBFNN can overcome the limitation of local infinitesimal during fault diagnosis process, and the requirement for fast diagnosis rate and high diagnosis precision can be met.

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