作者：谈恩民, 阮济民, 黄顺梅

作者单位：桂林电子科技大学电子工程与自动化学院，广西 桂林 541004

关键词：模拟电路;故障诊断;谱半径;奇异值;故障定位

摘要：

针对现有模拟电路故障诊断方法的人工神经网络、支持向量机(SVM)等人工智能算法需要大量的训练样本和时间的问题，该文提出一种利用矩阵特征分析进行模拟电路故障诊断方法。该方法建立一个输出响应方阵，当电路发生故障时，方阵中的元素会发生变化，根据矩阵理论，利用矩阵谱半径和矩阵模的扰动矩阵最大奇异值来描述这种差异。Sallen_Key 电路和CTSV电路的实验结果表明，该方法能够很好地判断模拟电路是否发生故障以及故障定位，该文方法有效性在Sallen_Key 、CTSV电路上得到验证，并且在这两个电路中，故障诊断率高达100%。

Fault diagnosis of analog circuits based on output response matrix characteristic analysis
TAN Enmin, RUAN Jimin, HUANG Shunmei
School of Electronic Engineering and Automation, Guilin University of Electronic Science and Technology, Guilin 541004, China
Abstract: In view of the existing artificial neural network, support vector machine (SVM) and other artificial intelligence algorithms of analog circuit fault diagnosis methods need a large number of training samples and a lot of time, this paper proposed a method of analog circuit fault diagnosis using matrix feature analysis. In this method, an output response square matrix was established. When the circuit fails, the elements in the square matrix will change. According to the matrix theory, the spectral radius of the matrix and the maximum singular value of the perturbation matrix of the matrix module had been used to describe the difference.The experimental results of Sallen_Key circuit and CTSV circuit showed that this method could well judge whether the analog circuit was faulty and located the fault. The effectiveness of the method proposed in this paper had been verified on Sallen_Key and CTSV circuits, and the fault diagnosis rate was as high as 100% in these two circuits.
Keywords: analog circuit;fault diagnosis;spectral radius;singular value;fault location
2022, 48(1):92-100  收稿日期: 2020-12-30;收到修改稿日期: 2021-02-19
基金项目: 国家自然科学基金（61741403）
作者简介: 谈恩民（1966-），男，河南光山县人，教授，博士，研究方向为模拟电路故障诊断
参考文献
[1] KUMAR A, SINGH A P. Fuzzy classifier for fault diagnosis in analog electronic circuits[J]. ISA Transactions, 2013, 52(6): 816-824
[2] CHEN P, YUAN L, HE Y, et al. An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis[J]. Neurocomputing, 2016, 211(26): 202-211
[3] 但长林, 李三雁, 张彬. 基于样本熵和SVM的滚动轴承故障诊断方法研究[J]. 中国测试, 2020, 46(11): 37-42
[4] HE X, WANG H, LU J, et al. Analog circuit fault diagnosis method based on preferred wavelet packet and ELM[J]. Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2013, 34(11): 2614-2619
[5] 颜学龙, 汪斌斌. 自适应狼群算法优化ELM的模拟电路故障诊断[J]. 计算机工程与科学, 2019, 41(2): 246-252
[6] 董海迪, 刘刚, 何兵, 等. 基于系统搜索的容差模拟电路多参数故障诊断[J]. 中国测试, 2019, 45(7): 134-139
[7] 崔江, 王友仁. 一种新颖的基于混合故障字典方法的模拟故障诊断策略[J]. 电工技术学报, 2013(4): 278-284
[8] 刘东平, 董俊, 蒲秀英. 基于小波分析和支持向量机的模拟电路故障诊断[J]. 计算技术与自动化, 2012, 31(1): 60-63
[9] 单剑锋, 万国发. 一种LMD和近似熵算法的模拟电路特征提取方法[J]. 机械科学与技术, 2018, 37(9): 1431-1436
[10] 张淑清, 孙国秀, 李亮, 等. 基于LMD近似熵和FCM聚类的机械故障诊断研究[J]. 仪器仪表学报, 2013, 34(3): 714-720
[11] 邓勇, 于晨松. 因子分析和ELM在模拟电路故障诊断的应用[J]. 电子测量与仪器学报, 2016, 30(10): 1512-1519
[12] 李永忠, 张杰. 一种基于云模型和半监督聚类的入侵检测算法[J]. 电子测量与仪器学报, 2014, 28(12): 1376-1381
[13] 钟振华. 具有Perron-Frobenius性质矩阵Perron根的估计[D]. 昆明: 云南大学, 2012.
[14] ZHANG C Y, LUO S, FENGMIN X U, et al. The eigenvalue distribution on Schur complement of nonstrictly diagonally dominant matrices and general H-matrices[J]. The electronic journal of linear algebra ELA, 2009, 18(1): 801-820
[15] 郭少艳. 两类矩阵优化问题的扰动分析[D]. 大连: 大连理工大学, 2016.
[16] TANG X, XU A. Multi-class classification using kerneldensity estimation on k-nearest neighbours[J]. Electronics Letters, 2016, 52(8): 600-602
[17] HAN B R, LI J B. Analog circuit fault diagnosis based on particle swarm neural network hybrid algorithm[J]. Applied Mechanics & Materials, 2013, 373-375: 1049-1052
[18] MA Q, HE Y, ZHOU F. A new decision tree approachof support vector machine for analog circuit fault diagnosis[J]. Analog Integrated Circuits & Signal Processing, 2016, 88(3): 455-463
[19] YUAN Z, HE Y, YUAN L, et al. A diagnosticsmethod for analog circuits based on improved kernel entropycomponent analysis[J]. Journal of Electronic Testing, 2017, 33(6): 697-707