模拟电路故障诊断的特征提取及支持向量机集成方法研究
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摘要
模拟电路故障诊断的研究是电路测试领域中极具挑战性的前沿和热点研究课题。由于模拟电路响应的连续性、非线性性和元件参数的容差性等固有的特点以及模拟电路故障的多样性和复杂性,使得传统的故障诊断理论和方法在实际的模拟电路故障诊断中难以达到预期的效果。因此研究高效的、适应模拟电路发展的故障诊断理论和方法尤为重要。近年来迅速发展并在模式识别等领域得到成功应用的集成学习和多重分形分析方法为模拟电路故障诊断提供了好的思路。模拟电路故障诊断的主要任务是首先提取和选择有效的故障特征,然后采用有效方法对故障进行识别和定位。结合现代测试技术、信号处理和模式识别等理论和技术,本文深入研究了模拟电路的故障特征提取及基于支持向量机(SVM)集成和多重分形分析的模拟电路故障诊断方法。论文的主要研究工作及成果如下:
(1)模拟电路故障诊断的特征提取方法研究。快速有效地提取反映电路状态的故障特征,是模拟电路故障诊断的重要环节。针对有效采样点特征提取方法、离散小波变换特征提取方法和小波包分解特征提取方法等常用方法的不足,提出了联合时频域(JTFD)和双树复小波分形(DTCWTF)两种特征提取方法。JTFD方法通过提取信号在时域和频域内的高阶统计特征作为特征向量,很好地反映电路在各种状态下的不同特性,有利于区分不同种类的故障且计算方法简单。DTCWTF方法充分利用了小波和分形两种方法具有自相似性和认识事物由粗到细的过程一致性本质及双树复小波能够同时提取信号的幅值信息和相位信息的优点。DTCWTF方法提取的故障特征具有一定抗噪声能力,具有分类器设计简单,故障分类率较高等优点。
(2)模拟电路故障诊断的多重分形分析研究。非线性元件的参数不仅随着元件本身的伏安特性变化,而且还随着工作点的变化而变化等原因,使非线性电路的故障诊断比线性电路的故障诊断更加复杂和困难。本文研究了基于多重分形分析的非线性模拟电路故障诊断方法,提出了多重分形消除趋势波动分析和小波领袖多重分形分析两种方法,并将其应用于非线性模拟电路的故障分析与诊断。以( ) ( )??αmin ,αmax ,Δα, fαmin , fαmax,Δf??向量作为故障特征进行非线性模拟电路故障诊断,并与基于小波包特征、JTFD特征和DTCWTF特征的故障诊断方法的结果进行比较,实验结果表明基于多重分形特征的故障诊断方法获得更高的故障诊断率。其中,Δα=αmax ?αmin, ( ) ( )Δf = fαmax ? fαmin,α是奇异性指数, f是奇异谱函数。
(3)基于特征选择和SVM参数优化的模拟电路故障诊断方法研究。在实际的故障诊断中,采集的故障数据往往包含很多与故障信息不相关或冗余的变量,严重影响SVM的分类性能,同时过多的变量也增加了计算代价,导致实时性变差;SVM参数选择合适与否也会严重影响分类的效果。本文提出了用混合粒子群优化算法和交叉熵方法解决模拟电路故障诊断中的特征选择和SVM参数优化问题。对模拟电路进行故障诊断实验,结果显示两种方法都可以较好地选择最优特征子集和优化SVM参数;使用优化的特征子集和SVM参数进行故障诊断,获得较高的故障诊断正确率。交叉熵方法比混合粒子群优化算法有更高的效率和故障诊断正确率。
(4)基于SVM集成的模拟电路故障诊断方法研究。简单采用一对多(OAA)等策略构造的多分类SVM不能满足模拟电路故障诊断的要求。为提高多分类SVM的故障诊断正确率,提出了三种基于SVM集成的故障诊断方法:融合层次支持向量机(HSVM)和Dempster-Shafer(D-S)理论方法、融合支持向量数据描述(SVDD)和D-S理论方法及改进的AdaBoost-SVM方法。在融合HSVM和D-S理论的故障诊断方法中,通过D-S理论融合多个不同决策消除HSVM各层次节点的累积误差,提高预测精度,改善了HSVM的分类性能。在实际故障诊断中经常遇到正常状态下的样本容易获得而故障状态下的样本不易获得的状况,融合SVDD和D-S理论的故障诊断方法利用SVDD只需使用正常状态下的样本进行训练就能够对测试样本进行预测的优点;同时应用D-S证据理论消除SVDD构造的多分类器产生判决的混叠问题,提高了故障诊断正确率。改进的AdaBoost-SVM故障诊断方法利用故障诊断正确率和差异度共同作用的度量函数来评价SVM集成分类器的泛化能力;采用具有随机性和遍历性的Logistic混沌映射改变SVM的核参数和正则化参数,构造出大差异度的成员SVM,使集成的分类器不仅有较高的分类正确率而且有较好的泛化能力。对模拟电路进行故障诊断,实验结果证明提出的三种SVM集成故障诊断方法能够获得比OAA-SVM和HSVM等多分类方法更好的故障诊断性能。
Fault diagnosis for analog circuit is still a challenging subject in the circuit test research field. Due to the inherent characteristics of analog circuit, such as its nonlinearity, continuous response and tolerance on component parameters, etc, inducing the diversity and complexity of fault types of the circuit, it is difficult for the conventional fault diagnosis theories and methods to achieve the expected results in practical engineering. Hence, it is very important to explore some efficient fault diagnosis theories and methods to meet the development of analog circuit. Ensemble learning and multi-fractal analysis, which have been successfully applied in pattern identification, provide a promising solution for fault diagnosis for analog circuit. Combined with the modern test theory, signal processing and pattern identification theory, the fault diagnosis method for analog circuit based on support vector machine (SVM) ensemble learning and multi-fractal analysis is deeply researched in this dissertation. The main research contents and achievements of the dissertation are as follows:
(1) The study on feature extraction for analog circuit fault diagnosis. How to quickly and efficiently extract the fault features, which reflect the state of analog circuit, is difficult and critical in fault diagnosis for analog circuit. Considering that the imperfect of feature extraction method commonly used in fault diagnosis for analog circuit, the joint time-frequency domain (JTFD) and dual-tree complex wavelet transform fractal (DTCWTF) feature extraction methods are proposed. The feature vectors, extracted by the JTFD method, represent the high order statistical characteristics of signal in time domain and frequency domain. They can reflect the different circuit state which helps to identify different fault types, and computing complexity of the JTFD method is low. The DTCWTF method, based on dual-tree complex wavelet transform and fractal analysis, makes full use of the consistency process in understanding things from coarse-to-fine and the same nature of self-similarity, and it can extract both the amplitude and phase information. The feature extracted by DTCWTF method has a certain anti-noise capability which makes it easy to design classifier.
(2) The research on fault diagnosis for analog circuit based on multi-fractal analysis. Due to the bias shifting of nonlinear device and lacking generally fault model of the nonlinear circuit, fault diagnosis for nonlinear analog circuit is more difficult than that for linear analog circuit. The traditional theories and methods are not capable of solving the complex fault diagnosis for nonlinear analog circuit, but the recent development of multi-fractal theory provides a good idea to this problem. In the dissertation, the multi-fractal detrended fluctuation analysis (MF-DFA) method and the wavelet leader multi-fractal analysis (WL-MFA) method for fault diagnosis for nonlinear analog circuit are proposed. Multi-fractal attributes are estimated from each fault types of the nonlinear circuit and used as classification features within SVM classification procedure. Experiment results show that the multi-fractal features has higher fault diagnosis accuracy than other features do, such as the WP features, the JTFD features and the DTCWTF features, etc.
(3) The study on fault diagnosis for analog circuit based on feature selection and SVM parameters optimization. The irrelevant and redundant features variables in actual fault diagnosis system for analog circuit spoil SVM's classification performance seriously. Too many variables increase computing times and result in real-time performance degradation. The appropriateness of selecting SVM parameters also affects the classification result. In this dissertation, two methods based on the hybrid particle swarm optimization (HPSO) and the cross entropy methods (CEM) have been proposed to choose the effective feature and to optimize SVM parameters. The two methods can perform feature selection and optimize SVM parameters concurrently. Analog circuit fault diagnosis experiments verify the effectiveness of the proposed two optimization method.
(4) The study on fault diagnosis for analog circuit based on SVM ensemble method. Multi-class support machine classifier by combining of multiple binary SVMs using One-Against-All (OAA) or One-Against-One (OAO) strategy can not meet classification performance requirement in fault diagnosis for analog circuit. In the dissertation, three kinds of fault diagnosis method for analog circuit based on SVM ensemble are proposed. The first method combines hierarchical support vector machine (HSVM) with Dempster-shafer (D-S) theory to improve classification results compared to the simple decision-tree-like method. The second method combines support vector data description (SVDD) with D-S theory, which describe each class only need one class data and can deal with outlier sensitivity problem. The third method is based on improved AdaBoost-SVM, which solves the accuracy/diversity dilemma in AdaBoost algorithm by selecting more diverse weak learners, meanwhile overcomes the difficulty of selection of weak learners’parameters by logistic chaotic mapping. Fault diagnosis experiments on analog circuit show that the proposed three kinds of method have competitively learning ability and acquire better fault diagnosis accuracy than traditional fault diagnosis method based on multi-class SVM do.
(1)模拟电路故障诊断的特征提取方法研究。快速有效地提取反映电路状态的故障特征,是模拟电路故障诊断的重要环节。针对有效采样点特征提取方法、离散小波变换特征提取方法和小波包分解特征提取方法等常用方法的不足,提出了联合时频域(JTFD)和双树复小波分形(DTCWTF)两种特征提取方法。JTFD方法通过提取信号在时域和频域内的高阶统计特征作为特征向量,很好地反映电路在各种状态下的不同特性,有利于区分不同种类的故障且计算方法简单。DTCWTF方法充分利用了小波和分形两种方法具有自相似性和认识事物由粗到细的过程一致性本质及双树复小波能够同时提取信号的幅值信息和相位信息的优点。DTCWTF方法提取的故障特征具有一定抗噪声能力,具有分类器设计简单,故障分类率较高等优点。
(2)模拟电路故障诊断的多重分形分析研究。非线性元件的参数不仅随着元件本身的伏安特性变化,而且还随着工作点的变化而变化等原因,使非线性电路的故障诊断比线性电路的故障诊断更加复杂和困难。本文研究了基于多重分形分析的非线性模拟电路故障诊断方法,提出了多重分形消除趋势波动分析和小波领袖多重分形分析两种方法,并将其应用于非线性模拟电路的故障分析与诊断。以( ) ( )??αmin ,αmax ,Δα, fαmin , fαmax,Δf??向量作为故障特征进行非线性模拟电路故障诊断,并与基于小波包特征、JTFD特征和DTCWTF特征的故障诊断方法的结果进行比较,实验结果表明基于多重分形特征的故障诊断方法获得更高的故障诊断率。其中,Δα=αmax ?αmin, ( ) ( )Δf = fαmax ? fαmin,α是奇异性指数, f是奇异谱函数。
(3)基于特征选择和SVM参数优化的模拟电路故障诊断方法研究。在实际的故障诊断中,采集的故障数据往往包含很多与故障信息不相关或冗余的变量,严重影响SVM的分类性能,同时过多的变量也增加了计算代价,导致实时性变差;SVM参数选择合适与否也会严重影响分类的效果。本文提出了用混合粒子群优化算法和交叉熵方法解决模拟电路故障诊断中的特征选择和SVM参数优化问题。对模拟电路进行故障诊断实验,结果显示两种方法都可以较好地选择最优特征子集和优化SVM参数;使用优化的特征子集和SVM参数进行故障诊断,获得较高的故障诊断正确率。交叉熵方法比混合粒子群优化算法有更高的效率和故障诊断正确率。
(4)基于SVM集成的模拟电路故障诊断方法研究。简单采用一对多(OAA)等策略构造的多分类SVM不能满足模拟电路故障诊断的要求。为提高多分类SVM的故障诊断正确率,提出了三种基于SVM集成的故障诊断方法:融合层次支持向量机(HSVM)和Dempster-Shafer(D-S)理论方法、融合支持向量数据描述(SVDD)和D-S理论方法及改进的AdaBoost-SVM方法。在融合HSVM和D-S理论的故障诊断方法中,通过D-S理论融合多个不同决策消除HSVM各层次节点的累积误差,提高预测精度,改善了HSVM的分类性能。在实际故障诊断中经常遇到正常状态下的样本容易获得而故障状态下的样本不易获得的状况,融合SVDD和D-S理论的故障诊断方法利用SVDD只需使用正常状态下的样本进行训练就能够对测试样本进行预测的优点;同时应用D-S证据理论消除SVDD构造的多分类器产生判决的混叠问题,提高了故障诊断正确率。改进的AdaBoost-SVM故障诊断方法利用故障诊断正确率和差异度共同作用的度量函数来评价SVM集成分类器的泛化能力;采用具有随机性和遍历性的Logistic混沌映射改变SVM的核参数和正则化参数,构造出大差异度的成员SVM,使集成的分类器不仅有较高的分类正确率而且有较好的泛化能力。对模拟电路进行故障诊断,实验结果证明提出的三种SVM集成故障诊断方法能够获得比OAA-SVM和HSVM等多分类方法更好的故障诊断性能。
Fault diagnosis for analog circuit is still a challenging subject in the circuit test research field. Due to the inherent characteristics of analog circuit, such as its nonlinearity, continuous response and tolerance on component parameters, etc, inducing the diversity and complexity of fault types of the circuit, it is difficult for the conventional fault diagnosis theories and methods to achieve the expected results in practical engineering. Hence, it is very important to explore some efficient fault diagnosis theories and methods to meet the development of analog circuit. Ensemble learning and multi-fractal analysis, which have been successfully applied in pattern identification, provide a promising solution for fault diagnosis for analog circuit. Combined with the modern test theory, signal processing and pattern identification theory, the fault diagnosis method for analog circuit based on support vector machine (SVM) ensemble learning and multi-fractal analysis is deeply researched in this dissertation. The main research contents and achievements of the dissertation are as follows:
(1) The study on feature extraction for analog circuit fault diagnosis. How to quickly and efficiently extract the fault features, which reflect the state of analog circuit, is difficult and critical in fault diagnosis for analog circuit. Considering that the imperfect of feature extraction method commonly used in fault diagnosis for analog circuit, the joint time-frequency domain (JTFD) and dual-tree complex wavelet transform fractal (DTCWTF) feature extraction methods are proposed. The feature vectors, extracted by the JTFD method, represent the high order statistical characteristics of signal in time domain and frequency domain. They can reflect the different circuit state which helps to identify different fault types, and computing complexity of the JTFD method is low. The DTCWTF method, based on dual-tree complex wavelet transform and fractal analysis, makes full use of the consistency process in understanding things from coarse-to-fine and the same nature of self-similarity, and it can extract both the amplitude and phase information. The feature extracted by DTCWTF method has a certain anti-noise capability which makes it easy to design classifier.
(2) The research on fault diagnosis for analog circuit based on multi-fractal analysis. Due to the bias shifting of nonlinear device and lacking generally fault model of the nonlinear circuit, fault diagnosis for nonlinear analog circuit is more difficult than that for linear analog circuit. The traditional theories and methods are not capable of solving the complex fault diagnosis for nonlinear analog circuit, but the recent development of multi-fractal theory provides a good idea to this problem. In the dissertation, the multi-fractal detrended fluctuation analysis (MF-DFA) method and the wavelet leader multi-fractal analysis (WL-MFA) method for fault diagnosis for nonlinear analog circuit are proposed. Multi-fractal attributes are estimated from each fault types of the nonlinear circuit and used as classification features within SVM classification procedure. Experiment results show that the multi-fractal features has higher fault diagnosis accuracy than other features do, such as the WP features, the JTFD features and the DTCWTF features, etc.
(3) The study on fault diagnosis for analog circuit based on feature selection and SVM parameters optimization. The irrelevant and redundant features variables in actual fault diagnosis system for analog circuit spoil SVM's classification performance seriously. Too many variables increase computing times and result in real-time performance degradation. The appropriateness of selecting SVM parameters also affects the classification result. In this dissertation, two methods based on the hybrid particle swarm optimization (HPSO) and the cross entropy methods (CEM) have been proposed to choose the effective feature and to optimize SVM parameters. The two methods can perform feature selection and optimize SVM parameters concurrently. Analog circuit fault diagnosis experiments verify the effectiveness of the proposed two optimization method.
(4) The study on fault diagnosis for analog circuit based on SVM ensemble method. Multi-class support machine classifier by combining of multiple binary SVMs using One-Against-All (OAA) or One-Against-One (OAO) strategy can not meet classification performance requirement in fault diagnosis for analog circuit. In the dissertation, three kinds of fault diagnosis method for analog circuit based on SVM ensemble are proposed. The first method combines hierarchical support vector machine (HSVM) with Dempster-shafer (D-S) theory to improve classification results compared to the simple decision-tree-like method. The second method combines support vector data description (SVDD) with D-S theory, which describe each class only need one class data and can deal with outlier sensitivity problem. The third method is based on improved AdaBoost-SVM, which solves the accuracy/diversity dilemma in AdaBoost algorithm by selecting more diverse weak learners, meanwhile overcomes the difficulty of selection of weak learners’parameters by logistic chaotic mapping. Fault diagnosis experiments on analog circuit show that the proposed three kinds of method have competitively learning ability and acquire better fault diagnosis accuracy than traditional fault diagnosis method based on multi-class SVM do.
引文
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