基于PCA和SVM的模拟电路故障诊断

针对电路故障诊断中存在的样本要求高、推广能力弱、特征提取难等问题,提出了一种基于主成分分析(PCA)与支持向量机(SVM)相结合的模拟电路故障诊断方法.通过对电路输出响应的采样信号进行PCA处理,提取故障特征的主要成分,然后利用多类SVM对各种状态下的故障模式进行分类决策,实现被测电路的故障诊断.实验结果表明:该方法能够实现电路故障的快速检测与故障元件的准确定位,具有速度快、精度高、鲁棒性好的特点.

Fault Diagnosis in Analog Circuit Based on PCA and SVM

A method of fault diagnosis in analog circuits based on principle component analysis(PCA) and multiclass support vector machine(SVM) was proposed in order to solve the problems of requiring many fault samples,weak generalization ability and difficult feature extraction.The circuit output is sampled in frequency domain to collect training data,which is preprocessed by PCA to extract main components of the fault features.Fault patterns under various states are classified using multiclass SVM,and fault diagnos...