模拟电路故障诊断新方法  

New method of analog circuits fault diagnosis

作　　者：谢涛[1,2] 何怡刚[3] 李珩[4] 

机构地区：[1]湖南科技大学计算机科学与工程学院,湖南湘潭411201 [2]湖南大学电气与信息工程学院,长沙410082 [3]合肥工业大学电气与自动化工程学院,合肥230009 [4]中南林业科技大学外国语学院,长沙410004

出　　处：《计算机工程与应用》2015年第21期52-57,共6页Computer Engineering and Applications

基　　金：国家杰出青年科学基金(No.50925727);国家自然科学基金(No.60876022;No.61202462;No.61308001);国家高技术研究发展计划(863)(No.2006AA04A104);湖南科技大学校级课题(No.E51366);湖南省科技计划项目(No.2008Gk2022);广东省教育部产学研项目(No.2009B090300196)

摘　　要：提出了一种新颖的基于多小波神经网络的模拟电路故障诊断方法。介绍了多小波的原理,分析了多小波神经网络的结构、逼近性质及多小波神经网络的算法,提出了用多小波来处理故障信号,提取故障特征向量输入给神经网络,从而进行模拟电路故障诊断。由于多小波函数具有连续、对称性及支撑集短等一系列优点,所以用多小波神经网络来进行模拟电路故障诊断比一般的小波神经网络具有诊断精度高、诊断速度快的优点。给出了仿真诊断实例,验证了该方法的有效性。This paper proposes a novel method of analog circuits fault diagnosis based on multi-wavelet neural network. It introduces the principle of multi-wavelet, analyzes the structure of multi-wavelet neural network and the approximation properties and the algorithm of multi-wavelet neural network. It uses multi-wavelet to deal with fault signal and extracts fault feature vector input to the neural network, thus diagnoses analog circuits fault. Since the multi-wavelet function is provided with a series of advantages such as continuous and symmetry as well as support set that is short and so on, using multi-wavelet neural network to analog circuits fault diagnosis is provided with advantages such as high accuracy and fast speed of diagnosis than the general wavelet neural network. It gives a simulation diagnosis example, and the effectiveness of this method is verified.

关 键 词：模拟电路 故障诊断 故障特征向量 多小波变换 神经网络 

分 类 号：TN911[电子电信—通信与信息系统]