一种新的模拟电路故障特征提取与诊断方法  

A New Fault Feature Extraction and Diagnosis Method of Analog Circuits

作　　者：祝文姬[1] 何怡刚[1] 

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2011年第4期41-46,共6页Journal of Hunan University:Natural Sciences

基　　金：国家杰出青年科学基金资助项目(50925727);国家自然科学基金资助项目(60876022);国家863计划资助项目(2006AA04A104);湖南省科技计划资助项目(2008Gk2022);广东省教育部产学研资助项目(2009B090300196)

摘　　要：提出了一种基于小波变换、傅立叶变换与神经网络相结合的模拟电路故障诊断新方法.该方法使用小波变换和傅立叶变换对模拟电路在各种故障状态下进行特征的提取,即首先对电路在各种故障状态下的节点电压信号进行小波变换以将干扰信号(如噪声)除去,防止不相关的能量混在有效信号中,然后采用傅立叶变换进行分析,得到有效信号频谱,提取其能量值再经主元分析与归一化处理后作为故障特征,采用概率型神经网络实现故障的定位.分析与仿真结果表明,本文方法获得了较好的故障分辨率与诊断正确率.Based on wavelet transform（WT）,Fourier transform（FT） and neural network,a new fault diagnosis method of analog circuits was proposed.The proposed method uses wavelet transform（WT） and Fourier transform（FT） for fault feature extraction when the analog circuits are under different faulty situations.That is,we use WT to filter the disturbance influences（for example,noises） on the original signals to prevent the unrelated energies from being mixed with the effective signals.These signals are then analyzed by FT to obtain the frequency spectrum of the effective signals.And then,the energies of these signals are extracted and preprocessed by principal component analysis（PCA） and normalization as fault features.Meanwhile,considering that the probability neural networks（PNNs） have characteristics of simple structures,high-speed of training process and easy append training samples,we use this kind of neural networks for fault location.The diagnosis principles and procedures were outlined,And the satisfied diagnosis resolution and accuracies have been achieved by using the proposed method.

关 键 词：小波变换 傅立叶变换 径向基网络 概率型网络 模拟电路 故障诊断 

分 类 号：TN39[电子电信—物理电子学]