基于优选小波基和模糊SOM网络的模拟电路故障诊断

为了解决模拟电路故障诊断中的特征提取困难并实现对模拟电路故障模式准确的分类,提出一种优选小波基、模糊理论和自组织特征映射网络(SOM,self-organizing feature map)相结合的模拟电路故障诊断方法.该方法首先对模拟电路故障响应信号进行小波分解、提取能量值、均值和方差组成输入特征向量,同时采用余弦分离度评价小波变换在不同小波基函数下获取故障特征的有效性,据此选择余弦分离度最小的小波基分解的特征向量输入到自组织特征映射网络进行故障分类.仿真实验表明,利用余弦分离度选择的最优小波基能有效提高模拟电路故障特征提取,模糊神经网络能对故障模式进行精确分类.

Analog Circuit Fault Diagnosis Method Based on Preferred Wavelet Basis and Fuzzy SOM

In order to solve the difficulty of feature extraction in analog circuits fault diagnosis and to accurately classify fault pattern,a new analog circuit diagnosis method based on the combination of optimal wavelet basis,fuzzy theory and self-organizing feature map is proposed.The response signals of the analog circuit are preprocessed by wavelet transform to extract feature consisting of energy,mean and variance,and the optimal wavelet coefficients with better classification capacity are obtained by using the minimum cosine separating degree.Then the feature set that is composed of optimal wavelet energy,mean and variance is inputted into a self-organizing feature map(SOM) to identify different fault cases.Simulation and experiment results show that the optimal wavelet transform based on cosine separating degree can extract analog circuit feature effectively,and the fuzzy SOM neural network can classify fault cases accurately.