一种模拟电路故障诊断方法研究

将LSSVM算法应用于模拟电路故障诊断模型,使用PSO算法对LSSVM算法的参数进行寻优。以带通滤波器电路和双二次高通滤波器电路的故障诊断实例对该文研究的模拟电路故障诊断方法进行验证。使用三层小波包分解输出电压信号,得到8个频带能量特征向量,通过Monte Carlo仿真得到数据样本,用于故障诊断模型的训练和测试。结果表明,该文使用的改进LSSVM算法构建的故障诊断模型针对8种故障的诊断准确率均高于95%,具有较好的故障诊断性能。     

基于CBA-SVM的模拟电路故障诊断方法研究

本文在SVM故障诊断中,引入全局寻优性较好的蝙蝠算法,能够实现寻优性能的提升,可以混沌优化关键参数β。本文以为试验的方式进行验证,选取某型雷达导引头测试系统自检模块比例积分电路作为研究对象开展实验,最终结果表明,在相同条件下,实施这一方法,相比引力搜索算法、粒子群算法优化之后的SVM,在诊断精准上提升了2.0%、7.0%,诊断耗时缩短了4s、1s,可以高质量、高效率的完成故障诊断任务。     

一种基于多频灵敏度分析的模拟电路K故障诊断方法

在模拟电路灵敏度分析的基础上，提出了多频灵敏度K故障诊断方法，详细说明了多频灵敏度K故障诊断方法的原理和步骤。针对模拟电路中最常见的双故障进行了电路仿真，仿真结果说明了该方法的有效性。     

基于改进的VMD和SVM的模拟电路故障诊断方法的研究

随着模拟电路的集成度和复杂度越来越高,提取其响应的特征信息也变得愈加困难.为解决提取故障信息的难题,提出将变分模态分解(variational modal decomposition,VMD)和复合多尺度排列熵(compound multi-scale permutation entropy,CMPE)相结合的算法构建故障特征向量,并且依靠麻雀搜索算法优化支持向量机(sparrow search algorithm-support vector machine,SSA-SVM)完成故障的分类。首先,通过PSPICE软件采集故障时的原始信号,并被VMD处理成多组含有原始信号特征的本征模态函数(intrinsic mode function,IMF)分量. 其次,计算出前3个IMF分量的CMPE值,归一化处理后作为故障特征向量.最后,在分类器中训练和测试.仿真测试显示本方案最终诊断正确率可达99.67%,对比其它方案能够有效提高故障诊断效率,是一种可行的模拟电路故障诊断思路.       

模拟电路故障诊断方法设计及虚拟维修过程

复杂的电路装置对于装备性能及实用效果起着决定性作用,在整个系统中发挥出重要作用。本文章主要对设备电路系统进行故障诊断的方法进行设计以及对于故障进行虚拟维修的过程。采用支持向量机法进行故障诊断,应用基于Petri网的虚拟维修拆卸过程方法设计。从而最终达到电路故障诊断及虚拟维修的目的。     

基于支持向量机的个人信用评估方法初探

针对传统个人信用评估方法的不足，鉴于支持向量机具有全局收敛性和良好的推广能力，本文将这种新方法应用到信用评估中，并进行了实例应用。与K最近邻等其它信用评估方法比较。支持向量机分类方法简单、精确度高，取得了比较好的结果。     

基于IDDT和SVM的混合电路故障诊断探究

混合电路待测数据受限,存在故障诊断速度较慢、效率有限等问题,提出了一种基于动态电流测试结合支持向量机的混合电路故障诊断方法,其基本思想是运用小波分解提取混合电路动态电流的有效信息,再融合SVM进行故障诊断。采用标准样本Iris数据集研究、确定了多类支持向量机的算法,采用高斯径向基核函数,运用改进的网络搜索方法进行了粗搜索和细搜索,以确定出SVM的最佳参数对。PSPICE及MATLAB软件对混合电路实例的仿真表明,该方法模式识别能力较强,可改善BP神经网络的收敛速度慢和容易陷入局部极小值等不足,适用于混合电路故障的快速准确诊断。     

基于IDDT和SVM的混合电路故障诊断探究

混合电路待测数据受限,存在故障诊断速度较慢、效率有限等问题,提出了一种基于动态电流测试结合支持向量机的混合电路故障诊断方法,其基本思想是运用小波分解提取混合电路动态电流的有效信息,再融合SVM进行故障诊断。采用标准样本Iris数据集研究、确定了多类支持向量机的算法,采用高斯径向基核函数,运用改进的网络搜索方法进行了粗搜索和细搜索,以确定出SVM的最佳参数对。PSPICE及MATLAB软件对混合电路实例的仿真表明,该方法模式识别能力较强,可改善BP神经网络的收敛速度慢和容易陷入局部极小值等不足,适用于混合电路故障的快速准确诊断。     

基于GA-LMBP算法的模拟电路故障诊断方法

主要分析遗传算法和BP神经网络的特点和存在的一些缺陷,研究遗传算法和改进型的BP算法相结合的相关技术,设计并实现一个基于遗传算法和LMBP算法相结合的GA—LMBP算法。通过诊断实例．比较三种算法的模拟电路故障诊断,结果证明在相同精确度的要求下,基于GA—LMBP的算法可以大大提高模拟电路故障诊断准确率。     

基于小波支持向量机的模拟电路故障诊断

在模拟电路故障诊断中,提出了利用小波分析与支持向量机结合的系统方法,利用小波变换对信号进行特征提取得到特征向量并作为支持向量机的训练向量,得到故障分类器。针对激励信号必须能够充分地激励电路的需求,提出一种通用激励信号——连续多抽样函数,利用抽样函数在带通区间内频谱分布均匀且能量相同这一特点作为模拟电路的通用激励信号。仿真结果表明,该激励条件下,利用小波-支持向量机能够较好地对模拟电路进行故障诊断。     

基于RS-PSO-SVM集成的模拟电路软故障诊断

针对模拟电路软故障诊断准确度不高的问题,提出一种基于粗糙集(RS)-粒子群算法(PSO)-支持向量机(SVM)集成的模拟电路软故障诊断方法。首先利用粗糙集理论对采集的模拟电路软故障特征信息进行维数约简,然后利用粒子群算法对支持向量机的参数进行优化,以提高支持向量机分类器的诊断性能,最后进行故障诊断。对四运放双二次高通滤波器进行仿真,实验结果表明,基于RS-PSO-SVM集成的模拟电路软故障诊断方法是有效的。与其他常用方法相比,该诊断方法具有更好的故障诊断性能。     

基于迁移学习LSSVM的模拟电路故障诊断

采用数据驱动方法进行模拟电路故障诊断时,在目标故障数据较少的条件下,诊断效果显著下降。针对该问题,提出一种基于TL-LSSVM的模拟电路故障诊断方法。该方法将相关的源域数据迁移至目标故障训练集,首先提取输出信号的小波系数作为特征数据,然后在LSSVM分类器的目标函数中增加源域辅助数据的误差惩罚项,构建出新的诊断模型。以滤波电路为诊断实例,实验结果表明,该方法使单、双故障诊断正确率分别达到97.2%和95.7%,显著提高了诊断正确率。     

层次支持向量机在数字电路故障诊断中的应用

针对在数字电路故障诊断过程中存在的样本不平衡度严重的问题,采用层次式支持向量机实现对其故障诊断,通过考虑各类样本的数据量来构造以支持向量为叶节点的树,该方法可有效地解决样本不平衡所带来的问题,同时能够减少计算SVM分类器的个数,提高了训练和诊断速度及准确率.针对故障样本集不可能覆盖所有故障状态而出现的未知故障状态的问题...     

An Analog Circuit Fault Characterization Methodology

A methodology for diagnosing and characterizing multiple faults in analog circuits, and results from applying this methodology to a real circuit is presented. Our method is a novel combination of a Simulation Before Test (SBT) and Interpolation After Test (IAT) methodology. Our method uses the classical SBT concept of a fault dictionary database constructed before test. It also uses a method of IAT that consists in using the measurements to guide an interpolation algorithm to effectively increase the local resolution of the fault dictionary database and thereby yield the most likely test parameter value. Our methods underlying principle is to characterize the fault-free and faulty circuit cases by their impulse responses obtained by simulation and subsequently stored in a fault dictionary database. The method uses the technique of Lagrange interpolation to resolve the faults between the fault dictionary database entries and the actual measurements. Our experimental results reveal that the method is effective for characterizing faults when the simulations match the measurements sufficiently. Consequently, the methods effectiveness depends highly on the quality of the models used to build the dictionary as well as on the accuracy of the measurements.Yvan Maidon was born in Bordeaux, France. He received the M.Sc degree in (electronics) applied physics from the University of Bordeaux, in 1980. He is currently Head of the Department for Applied Sciences in Electrical and Electronic Engineering at the University of Bordeaux 1. His special research interests include failure analysis and relaibility of analog circuits. He has also developed original BICS for mixed circuits and SoC testing.Thomas Zimmer is currently Professor at the University of Bordeaux 1. He received the M.Sc. degree in physics from the University of Würzburg, Germany, in 1989 and the Ph.D. degree in electronics from the University of Bordeaux 1, France, in 1992. His research interests include characterization and modeling of high frequency bipolar devices. He has authored and co-authored about 70 scientific and technical publications including several book chapters. He is also co-founder of the start-up company XMOD.André Ivanov is Professor in the Department of Electrical and Computer Engineering, at the University of British Columbia. Prior to joining UBC in 1989, he received his B.Eng. (Hon.), M. Eng., and Ph.D. degrees in Electrical Engineering from McGill University. In 1995–96, he spent a sabbatical leave at PMC-Sierra, Vancouver, BC. He has held invited Professor positions at the University of Montpellier II, the University of Bordeaux I, and Edith Cowan University, in Perth, Australia. His primary research interests lie in the area of integrated circuit testing, design for testability and built-in self-test, for digital, analog and mixed-signal circuits, and systems on a chip (SoCs). He has published widely in these areas and holds several patents in IC design and test. Besides testing, Ivanov has interests in the design and design methodologies of large and complex integrated circuits and SoCs. Ivanov has served and continues to serve on numerous national and international steering, program, and/or organization committees in various capacities. Recently, he was the Program Chair of the 2002 VLSI Test Symposium (VTS 02) and the General Chair for VTS 03 and VTS 04. In 2001, Ivanov co-founded Vector 12, a semiconductor IP company. He has published over 100 papers in conference and journals and holds 4 US patents. Ivanov serves on the Editorial Board of the IEEE Design and Test Magazine, and Kluwers Journal of Electronic Testing: Theory and Applications. Ivanov is currently the Chair of the IEEE Computer Societys Test Technology Technical Council (TTTC). He is a Golden Core Member of the IEEE Computer Society, a Senior Member of the IEEE, a Fellow of the British Columbia Advanced Systems Institute and a Professional Engineer of British Columbia.     

基于监督的距离度量学习方法研究

很多机器学习算法（比如K近邻算法）,学习的效果非常依赖于输入数据的距离度量,距离度量学习的主要目标是通过训练样本学习出一个能够更有效反映样本空间的距离函数,在此距离函数下,同类样本具有较近的距离,异类样本具有较远的距离。对近年来基于监督的距离度量学习方法的基本思想和算法进行了研究,并对当前距离度量学习的热点进行了介绍。     

基于红外热图序列的板级芯片故障诊断研究

提出一种基于红外热图序列的板级芯片开/短路缺陷检测方法。首先记录芯片关键区域在上电程序响应过程的温度均值序列,运用Savitzky Golay卷积平滑法对其平滑滤波后提取时域特征参量,利用主成分分析法优选关键特征;然后构建支持向量机分类模型,利用粒子群算法优化支持向量机模型参数,使其能有效区分不同的电路板故障类型。为验证提出的方法在芯片开/短路缺陷检测中的有效性,在开发板上的主控芯片上进行了多种焊球开/短路模拟实验。结果表明,优化后的分类模型在测试集的交叉验证分类准确率为96.90%,证明了该方法诊断芯片开/短路缺陷的有效性。