基于混合核函数PSO-SVM的模拟电路故障诊断

摘要

针对模拟电路故障诊断中应用传统支持向量机算法存在的问题,提出由粒子群算法优化混合核函数支持向量机模型对模拟电路进行故障诊断的新方法.首先,对待诊断电路进行瞬态分析,记录输出点的电压值,采用小波包技术对输出值进行特征提取;其次,由粒子群算法优化混合核函数支持向量机的核函数权重和结构参数,用训练好的模型进行故障诊断,该方法不仅降低参数选择时的随机性,而且故障诊断的精确度提升了5％左右.在对某高通滤波器模拟电路进行的故障诊断中,验证了该方法的有效性.%For the question caused by traditional support vector machine algorithm in analog circuit fault diagnosis,the way using support vector machine algorithm of hybrid kernel function (HSVM) and particle swarm optimization (PSO) is proposed.First,after analyzing the transient circuit under test,and writing down the output voltage,wavelet package is used to extract the output voltage feature;second,we use PSO to optimize the kernel weight and structure parameters of HSVM;last,the trained model is used to diagnose the fault.This method not only reduces the randomness of parameters selection,but also the accuracy of simulation result is improved 5％.The effectiveness is proved during the process of fault diagnosis in high-pass filter analog circuit.