模拟电路测试诊断理论与关键技术研究
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摘要
模拟电路是航空、国防和现代工业中电子设备的重要组成部分，随着电路集成度和复杂度的提高，现代电子设备对模拟电路的可靠性和测试性的要求越来越高。模拟电路故障测试诊断研究能够极大丰富现代电路理论体系，降低工程应用中因电路故障而带来的损失和维护成本，推动电子系统的故障预测和健康管理技术的发展。本文吸取传统方法优点的同时以可测性分析理论、分数阶时频分析理论、支持向量数据描述（Support Vector Data Description, SVDD）理论为基础，对模拟电路测试诊断中的测试激励选择、测试节点选择、故障特征提取和智能故障诊断方法进行研究。本文的主要贡献在于：
（1）提出一种基于信号波形分解的测试激励生成方法。由于可选的测试激励信号及参数有无穷组合，理论上选择最优激励信号是一个NP难问题。为了降低测试激励选择的复杂度，提高待测电路故障特征的可诊性，研究了基于信号波形分解的最优测试激励生成技术。根据信号的傅立叶级数和小波基分解原理提出了两种任意波形激励信号的实现方法，将样本的最大类内类间距离作为故障可诊性的判断依据，由遗传算法进化得到最优激励信号。实验验证了新方法能够生成任意波形的激励信号，生成的测试激励信号可提高待测电路的故障可诊性，该方法适合线性电路和非线性电路的测试诊断。
（2）提出一种基于模糊故障概率的测试节点选择方法。传统的测试节点选择方法中±0.7V的统一模糊区间并不能符合每类电路和故障的特性，而且单一的测点选择标准也不能保证得到全局最优的测试节点集合。为此，研究一种基于模糊故障概率的测试节点选择技术，根据故障样本的正态分布曲线设置故障模糊区间，融合模糊样本交叉曲线面积和隔离故障度的双重信息，选择最优测试节点集合。实验结果表明该方法在最少的测试节点下能诊断出最多的故障，并且模糊故障的误诊概率最低。
（3）提出一种基于分数阶小波变换的故障特征提取方法。模拟电路中模糊故障的输出特征很相似，难以正确诊断和定位。为了提高故障特征的可区分性，研究一种基于分数阶小波变换的模拟电路故障特征提取技术。首先，将分数阶小波变换理解为在分数阶傅立叶域的小波变换，提出一种基于核分解的离散分数阶小波变换方法。然后，基于此离散方法，提出一种基于多阶分数阶小波变换的故障特征提取方法。实验结果表明新方法提取多阶分数阶空间中的故障信息能够增加故障特征的多样性和可区分性。
（4）设计一种基于Vague集信息融合的VSVDD(Vague SVDD, VSVDD)分类算法，并应用到模拟电路故障诊断中。模拟电路是多类故障诊断问题，如果描述球体边界松弛，模糊故障会落入多个球体的交叉区域，无法正确诊断，为此提出一种基于Vague集信息融合的VSVDD的模拟电路故障诊断方法。根据在球体空间的分布为测试样本赋予真假隶属度值，并根据球体的大小为每个球体赋予权值，采用最小加权Vague集距离判断样本类别。实验结果表明新方法能够提高模拟电路故障诊断的精度，球体Vague集信息的融合能够改善球体分类器的性能。
Analog circuits are important parts of aerospace, defense and modern industry. With theintegration and the increasing scale of electronic devices, modern electronic equipments has higher andhigher requirement on the reliability and testability for analog circuits. The research of analog circuitfault testing diagnosis is of great significance for enriching the modern circuit theory system, andreducing the losses caused by circuit faults and electronic equipments maintenance, and promoting thetechnology development of electronic system fault prediction and health management. In addition toabsorb the advantages of traditional methods, several new methods, such as testability analysis theory,fractional signal processing, support vector data description, are used to diagnose analog circuit faults.This paper deals with four key technologies of test stimulus selection, test node selection, fault featureextraction and intelligent fault diagnosis method. The contributions of this thesis are that:
(1) The thesis proposes a test stimulus generation method based on signal waveformdecomposition. Optimal test stimulus selection is a NP hard problem due to infinite options of testsignals and parameters in theory. In order to reduce the complexity of optimal test stimulus selectionand improve the testability of the circuits under test, this paper proposes a new stimulus selectionmethod based on signal waveform decomposition. Two approaches based on Fourier series expansionand wavelet decomposition are introduced to generate random signal. Moreover, the maximum distancewithin/between fault samples is the optimization object to select the optimal stimulus, which isevolved by genetic algorithm. The test results show that the proposed method is effective when isapplied to both of linear circuits and nonlinear circuits and can improve the testability of the circuitunder test.
(2) The thesis proposes a node selection method based on fault fuzzy probability. For thetraditional fault dictionary method, the fault fuzzy interval is calculated based on the interval of±0.7V,and the test nodes are selected according to a single standard. However, this traditional method does notensure to obtain the global optimal test node set. In our paper, a test node selection method based onfault fuzzy probability is proposed. The fault fuzzy interval is calculated based on the normaldistribution curve of each class fault. The optimal test node set is selected according to the fusioninformation of normal curve area and fault isolation value. The test results show that the proposedmethod can diagnose most of faults with the least amount of test nodes and reduce the misdiagnosisradio of fuzzy faults.
(3) The thesis proposes a fault feature extraction method based on fractional wavelet transform. The fuzzy faults with similar features for analog circuits are difficult to diagnose correctly. In order toimprove the testability of fault, a feature extraction method for analog circuit is introduced in this paper,which is based on fractional wavelet transform theory. Firstly, as fractional wavelet transform is a kindof wavelet transform in fractional Fourier domain, a new discrete algorithm is proposed. Then, a newmethod based on multiple-fractional wavelet transform is proposed to extract fault features of analogcircuits. The diagnosis results show that the extracted fault features in multiple fractional domains canimprove the testability of faults.
(4) The thesis designs a VSVDD classifier method based on Vauge set information fusion. Theproposed method is applied to diagnose multiple-class faults for analog circuits. If the descriptionsphere boundary is not compact, fuzzy faults located in crossed spheres would be misdiagnosed. AVSVDD diagnosis method is introduced based on vague set information fusion. Each test sample isassigned a truth membership value and a false membership value according to the sample spacedistribution. Moreover, each description sphere is assigned a weight value based on the size of sphere.Then, the test samples are diagnosed through the decision rule of minimum weight vague distance. Thediagnosis results improve that this method can improves the performance of sphere classification foranalog circuit fault diagnosis.
（1）提出一种基于信号波形分解的测试激励生成方法。由于可选的测试激励信号及参数有无穷组合，理论上选择最优激励信号是一个NP难问题。为了降低测试激励选择的复杂度，提高待测电路故障特征的可诊性，研究了基于信号波形分解的最优测试激励生成技术。根据信号的傅立叶级数和小波基分解原理提出了两种任意波形激励信号的实现方法，将样本的最大类内类间距离作为故障可诊性的判断依据，由遗传算法进化得到最优激励信号。实验验证了新方法能够生成任意波形的激励信号，生成的测试激励信号可提高待测电路的故障可诊性，该方法适合线性电路和非线性电路的测试诊断。
（2）提出一种基于模糊故障概率的测试节点选择方法。传统的测试节点选择方法中±0.7V的统一模糊区间并不能符合每类电路和故障的特性，而且单一的测点选择标准也不能保证得到全局最优的测试节点集合。为此，研究一种基于模糊故障概率的测试节点选择技术，根据故障样本的正态分布曲线设置故障模糊区间，融合模糊样本交叉曲线面积和隔离故障度的双重信息，选择最优测试节点集合。实验结果表明该方法在最少的测试节点下能诊断出最多的故障，并且模糊故障的误诊概率最低。
（3）提出一种基于分数阶小波变换的故障特征提取方法。模拟电路中模糊故障的输出特征很相似，难以正确诊断和定位。为了提高故障特征的可区分性，研究一种基于分数阶小波变换的模拟电路故障特征提取技术。首先，将分数阶小波变换理解为在分数阶傅立叶域的小波变换，提出一种基于核分解的离散分数阶小波变换方法。然后，基于此离散方法，提出一种基于多阶分数阶小波变换的故障特征提取方法。实验结果表明新方法提取多阶分数阶空间中的故障信息能够增加故障特征的多样性和可区分性。
（4）设计一种基于Vague集信息融合的VSVDD(Vague SVDD, VSVDD)分类算法，并应用到模拟电路故障诊断中。模拟电路是多类故障诊断问题，如果描述球体边界松弛，模糊故障会落入多个球体的交叉区域，无法正确诊断，为此提出一种基于Vague集信息融合的VSVDD的模拟电路故障诊断方法。根据在球体空间的分布为测试样本赋予真假隶属度值，并根据球体的大小为每个球体赋予权值，采用最小加权Vague集距离判断样本类别。实验结果表明新方法能够提高模拟电路故障诊断的精度，球体Vague集信息的融合能够改善球体分类器的性能。
Analog circuits are important parts of aerospace, defense and modern industry. With theintegration and the increasing scale of electronic devices, modern electronic equipments has higher andhigher requirement on the reliability and testability for analog circuits. The research of analog circuitfault testing diagnosis is of great significance for enriching the modern circuit theory system, andreducing the losses caused by circuit faults and electronic equipments maintenance, and promoting thetechnology development of electronic system fault prediction and health management. In addition toabsorb the advantages of traditional methods, several new methods, such as testability analysis theory,fractional signal processing, support vector data description, are used to diagnose analog circuit faults.This paper deals with four key technologies of test stimulus selection, test node selection, fault featureextraction and intelligent fault diagnosis method. The contributions of this thesis are that:
(1) The thesis proposes a test stimulus generation method based on signal waveformdecomposition. Optimal test stimulus selection is a NP hard problem due to infinite options of testsignals and parameters in theory. In order to reduce the complexity of optimal test stimulus selectionand improve the testability of the circuits under test, this paper proposes a new stimulus selectionmethod based on signal waveform decomposition. Two approaches based on Fourier series expansionand wavelet decomposition are introduced to generate random signal. Moreover, the maximum distancewithin/between fault samples is the optimization object to select the optimal stimulus, which isevolved by genetic algorithm. The test results show that the proposed method is effective when isapplied to both of linear circuits and nonlinear circuits and can improve the testability of the circuitunder test.
(2) The thesis proposes a node selection method based on fault fuzzy probability. For thetraditional fault dictionary method, the fault fuzzy interval is calculated based on the interval of±0.7V,and the test nodes are selected according to a single standard. However, this traditional method does notensure to obtain the global optimal test node set. In our paper, a test node selection method based onfault fuzzy probability is proposed. The fault fuzzy interval is calculated based on the normaldistribution curve of each class fault. The optimal test node set is selected according to the fusioninformation of normal curve area and fault isolation value. The test results show that the proposedmethod can diagnose most of faults with the least amount of test nodes and reduce the misdiagnosisradio of fuzzy faults.
(3) The thesis proposes a fault feature extraction method based on fractional wavelet transform. The fuzzy faults with similar features for analog circuits are difficult to diagnose correctly. In order toimprove the testability of fault, a feature extraction method for analog circuit is introduced in this paper,which is based on fractional wavelet transform theory. Firstly, as fractional wavelet transform is a kindof wavelet transform in fractional Fourier domain, a new discrete algorithm is proposed. Then, a newmethod based on multiple-fractional wavelet transform is proposed to extract fault features of analogcircuits. The diagnosis results show that the extracted fault features in multiple fractional domains canimprove the testability of faults.
(4) The thesis designs a VSVDD classifier method based on Vauge set information fusion. Theproposed method is applied to diagnose multiple-class faults for analog circuits. If the descriptionsphere boundary is not compact, fuzzy faults located in crossed spheres would be misdiagnosed. AVSVDD diagnosis method is introduced based on vague set information fusion. Each test sample isassigned a truth membership value and a false membership value according to the sample spacedistribution. Moreover, each description sphere is assigned a weight value based on the size of sphere.Then, the test samples are diagnosed through the decision rule of minimum weight vague distance. Thediagnosis results improve that this method can improves the performance of sphere classification foranalog circuit fault diagnosis.
引文
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