一种基于核密度估计的模拟电路故障诊断方法

为解决模拟电路中含有噪声等异常信息给支持向量机的最优分类面建立带来的困难,提出了一种基于核密度估计方法的模拟电路故障诊断新方法。首先提取电路的时域信号统计参数作为故障特征,然后运用核密度估计方法构造模糊隶属度函数,将该隶属度函数应用到模糊支持向量机上进行故障诊断。通过训练模糊支持向量机获得故障诊断模型,实现对电路单故障和多故障的诊断分类,能有效消除特征中噪声和野点的影响。将该方法应用于CSTV滤波电路进行仿真实验,结果表明该方法能突出不同故障的特性并正确有效地诊断出多故障类型,综合诊断正确率达到95%,为模拟电路故障诊断提供了新的技术途径。

An Analog Circuit Fault Diagnosis Method Based on Kernel Density Estimation Principle

Because analog circuits such as abnormal noise contained in the information,to the support vector machine to build up the optimal classification brings difficulties,this paper proposes a new method for analog circuit fault diagnosis.First of all,time-domain signal extraction circuit statistical parameters,a set of fault characteristics and then use kernel density estimation method,proposed a form of fuzzy membership function construction,to eliminate the impact of noise characteristics.The establishment of such a membership functions with fuzzy support vector machines on the circuit fault diagnosis.Through the training of support vector machine fault diagnosis model was to achieve single-fault and multi-circuit fault diagnostic classification.The method is applied on CSTV filter circuit,the simulation experiment results show that the method can highlight the different characteristics of fault can be diagnosed correctly and effectively multi-fault types,comprehensive diagnostic accuracy of 95%,and the method for analog circuit fault diagnosis a new way.This technology has good prospects for engineering applications.