电子科技 ›› 2019, Vol. 32 ›› Issue (6): 26-30.doi: 10.16180/j.cnki.issn1007-7820.2019.06.006

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基于Elman神经网络的模拟电路故障诊断研究

孙李阔1,王代强2   

  1. 1. 贵州大学 大数据与信息工程学院,贵州 贵阳550025
    2. 贵州民族大学 机械电子工程学院 贵州 贵阳550025
  • 收稿日期:2018-06-25 出版日期:2019-06-15 发布日期:2019-07-01
  • 作者简介:孙李阔(1992-),男,硕士研究生。研究方向:电路与系统。|王代强(1965-),男,博士,教授。研究方向:电路与系统等。
  • 基金资助:
    国家自然科学基金(11564005);贵州省教育厅创新群体重大研究项目(黔教合KY字[2017]035);贵州省功率元器件重点实验室项目基金(KFJJ201501)

Research on Fault Diagnosis of Analog Circuit Based on Elman Neural Network

SUN Likuo1,WANG Daiqiang2   

  1. 1. School of Big Data & Information Engineering,Guizhou University,Guiyang 550025,China
    2. School of Mechanical and Electronic Enginnering,Guizhou Minzu University,Guiyang 550025,China
  • Received:2018-06-25 Online:2019-06-15 Published:2019-07-01
  • Supported by:
    National Natural Science Foundation of China(11564005);The Major Research Project of the Innovation Group of the Guizhou Provincial Education Department(QJH KY [2017]035);Guizhou Power Component Key Laboratory Project Fund(KFJJ201501)

摘要:

针对模拟电路的软故障,文中提出了一种基于改进Elman神经网络与提高特征向量有效性相结合的诊断方法。该方法对不同情况下的输出信号进行3次小波分析,形成8维的特征向量,再与改进的Elman神经网络结合进行分类与诊断。将改进Elman神经网络应用于非线性模拟电路故障诊断中可提高其诊断率与分类率。文中对其诊断方法进行了实验对比测试,结果表明,该方法提高了诊断性能,其诊断率与分类率分别为92.5%和83%。

关键词: 软故障, Elman神经网络, 特征向量, 小波分析, 故障诊断, 分类率

Abstract:

Aiming at the soft fault of analog circuits, a diagnosis method based on improved Elman neural network combined with the improvement of feature vector effectiveness was proposed. This method performed three wavelet analysis on the output signals under different cases to form an eight-dimensional feature vector, which was combined with the improved Elman neural network for classification and diagnosis. Applying the improved Elman neural network to the fault diagnosis of nonlinear analog circuits could improve its diagnostic rate and classification rate.This paper had carried out experimental comparison tests on the diagnostic methods.The results showed that the method improved the diagnostic performance, of which the diagnostic rate and classification rate were 92.5% and 83%,respectively.

Key words: soft fault, Elman neural network, feature vector, wavelet analysis, fault diagnosis, classification rate

中图分类号: 

  • TP183