基于输出响应矩阵特性分析的模拟电路故障诊断

Fault diagnosis of analog circuits based on output response matrix characteristic analysis

针对现有模拟电路故障诊断方法的人工神经网络、支持向量机(SVM)等人工智能算法需要大量的训练样本和时间的问题,该文提出一种利用矩阵特征分析进行模拟电路故障诊断方法。该方法建立一个输出响应方阵,当电路发生故障时,方阵中的元素会发生变化,根据矩阵理论,利用矩阵谱半径和矩阵模的扰动矩阵最大奇异值来描述这种差异。Sallen_Key电路和CTSV电路的实验结果表明,该方法能够很好地判断模拟电路是否发生故障以及故障定位,该文方法有效性在Sallen_Key、CTSV电路上得到验证,并且在这两个电路中,故障诊断率高达100%。

In view of the existing artificial neural network,support vector machine(SVM)and other artificial intelligence algorithms of analog circuit fault diagnosis methods need a large number of training samples and a lot of time,this paper proposed a method of analog circuit fault diagnosis using matrix feature analysis.In this method,an output response square matrix was established.When the circuit fails,the elements in the square matrix will change.According to the matrix theory,the spectral radius of the matrix and the maximum singular value of the perturbation matrix of the matrix module had been used to describe the difference.The experimental results of Sallen_Key circuit and CTSV circuit showed that this method could well judge whether the analog circuit was faulty and located the fault.The effectiveness of the method proposed in this paper had been verified on Sallen_Key and CTSV circuits,and the fault diagnosis rate was as high as 100%in these two circuits.