基于DAGSVC的模拟电路故障字典法

Analog circuit diagnosis with fault dictionary method based on DAGSVC

针对模拟电路的故障诊断和支持向量机分类器的设计问题,讨论了一种基于有向无环图支持向量机分类器(DAGSVC)的故障字典新方法,并比较了几种支持向量机故障分类器的平均测试复杂度指标。通过对2个实际模拟滤波器的实际测试和验证表明:该方法性能要优于"1-v-r"SVC,"1-v-1"SVC等常规的故障分类器,并和聚类二叉树SVC的诊断性能接近,适合模拟电路的故障分类和诊断。

Focusing on the design of problem of fault diagnosis of analog circuit and classifier with support vector machines（SVMs）,a new method of fault dictionary based on directed acyclic graph SVMs classifier（DAGSVC） is presented,and a specification for estimating the average test complexity of the support vector machine classifier（SVC） is also compared.Two actual analog filter are tested to validate the proposed method,whose performance is proven to be superior to the traditional methods,such as ＂1-v-r＂SVC and ＂1-v-1＂SVC.The proposed method,being proper to perform analog circuit diagnosis and faults isolation,could also achieve almost the same diagnosis rate as the clustering binary tree SVC,whose test structure is not unique.