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。     

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。     

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。     

现代模拟电路故障诊断方法探讨

随着电子技术的快速发展,电子系统和仪器仪表的结构随之也变得复杂了,同时这样在设备运行时也会频繁发生故障．故障诊断则成了维持整个电路的安全运行保障。文章主要介绍了现代模拟电路故障诊断中遇到的困难及诊断方法。     

一种基于神经网络的模拟电路故障诊断方法

模拟电路故障诊断一直是一项富有挑战性的研究课题。文章在简要介绍BP神经网络基本原理的基础上,以差分放大电路为例,设计并实现了基于BP算法的模拟电路故障诊断方法,建立了模拟电路故障诊断BP神经网络模型。实验表明,该模型的辨识精度高,能实现对模拟电路故障的正确诊断。     

一种基于小波-神经网络的模拟电路故障诊断方法

小波分析具有数据压缩和特征提取的特性,神经网络具有非线性映射和学习推理的优点。结合两者的特点,提出了一种基于小波与神经网络的模拟电路故障诊断方法,该方法用小波变换对电路响应信号进行特征提取,从而简化神经网络的结构,降低计算的复杂度,加快了训练速度。对实例仿真表明,该法能有效地对模拟电路进行故障诊断。     

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.     

模拟电路故障诊断的结点电压灵敏度比值法

在证明线性电路中结点电压变化量比值等于结点电压灵敏度比值的基础上,提出了结点电压灵敏度比值法,通过结点电压变化量比值和结点电压灵敏度比值的比对确定电路的故障元件。理论分析和实验结果表明,该方法算法简单、诊断速度快,在可测点受限条件下具有较高的诊断精度,特别适合大规模线性模拟电路的故障诊断和测试。     

A Method on Analog Circuit Fault Diagnosis with Tolerance

In this paper, it is proved that the direction of the node-voltage difference vector, which is the difference between the node-voltage vector at faulty state and the one at the nominal state, is determined only by the location of the faulty clement in linear analog circuits. Considering that the direction of the node-voltage sensitivity vector is the same as the one of the node-voltage difference vector and also considering that the module of the node-voltage sensitivity vector presents the weight of the parameter of faulty element deviation relative to the voltage difference, fault dictionary is set up based on node-voltage sensitivity vectors. A decision algorithm is proposed concerned with both the location and the parameter difference of the faulty element. Single fault and multi-fault can be diagnosed while the circuit parameters deviate within the tolerance range of 10 %.     

模拟电路故障诊断的双重扰动支持向量机集成方法

为进一步提高模拟电路故障诊断准确率,提出一种特征和模型参数双重扰动的集成支持向量机新算法.首先在集合覆盖思想下设计基于混沌蚁群算法的属性约简算法将特征样本空间划分成若干子空间,然后针对每个子空间,在"低偏差区域"内进行模型参数扰动,经过两次多数投票法得到最终集成结果.故障诊断实例表明,该方法比多分类支持向量机、Attribute Bagging(AB)算法、Bagging方法等具有更好的故障诊断率.     

现代模拟电路智能故障诊断方法研究与发展

对系统可靠性和经济性要求的提高使得模拟电路故障诊断的重要性日益凸显。首先在介绍了模拟电路故障原因及分类的基础上,详细分析了模拟电路故障诊断的特点。针对传统诊断方法的不足之处,介绍了基于人工智能和现代信息信号处理的现代故障诊断方法,包括专家系统诊断方法、神经网络诊断方法、模糊诊断方法和基于核的诊断方法,同时系统地分析了每种方法的基本原理、优缺点、研究进展和典型应用。最后探讨了目前模拟电路故障诊断研究存在的问题和未来的发展方向。     

模拟电路故障实时诊断的滤波器方法

本文讨论了一种模拟电路故障实时诊断的滤波器方法,该方法首先用状态空间模型来描述模拟电路,然后利用强跟踪滤波器对模型进行状态与参数联合估计,再利用修正的Bayes分类算法对故障进行检测与分离,从而实现故障的实时诊断.实验结果证明了本方法是有效的.