摘要
模拟电路测试与故障诊断在电路设计、设备生产和仪器维护中发挥着关键作用,是目前学术研究者和工程师在电子测试领域中具有挑战性的重要课题。随着电子技术的迅速发展,模拟电路的复杂度和密集度不断增长,对模拟电路运行的可靠性提出了更为严格的要求。在模拟电路测试和故障诊断中,传统的故障诊断理论和方法不能很好地解决因元件容差性、输出响应的连续性和非线性等电路固有属性而造成的故障多样性和复杂性问题,研究切合实用、高效高性能的现代智能故障诊断理论和方法尤为迫切。故障特征提取和故障的有效识别是智能故障诊断中的两个关键环节,本文以现代测试技术为基础,结合小波分析和以支持向量机为代表的智能计算技术,深入研究了模拟电路的特征提取优化选择和分类器的优化设计在模拟电路智能故障诊断中的问题。本文取得了如下的成果:
研究了小波分析的模拟电路故障识别灵敏度及故障特征优化方法。小波变换在信号分析中具有良好时频局部性,比单纯的时域或频域分析方法更适合表示故障电路的特征,但不同的小波函数在故障识别中具有不同的性能。本文提出用均方根(RMS)测度量化小波在故障测试中的灵敏度,以类可分性准则选择提取故障特征的最优小波函数,达到故障特征的优化。实验结果说明本方法可有效地提高故障的识别精度。
研究了基于小波神经网络的模拟电路故障诊断方法的优化问题。针对传统的神经网络方法在模拟电路故障诊断中存在收敛速度慢、易于陷入局部极小的不足,将小波分析与神经网络结合,构建小波神经网络的模拟电路的故障诊断模型,利用小波函数具有的多分辨性等特点有效地提高了网络的收敛速度和降低了误诊率。为了解决网络结构冗余而造成训练收敛方向偏离全局最优点,使推广能力降低的问题,提出用遗传算法对小波神经网络的结构和参数进行优化的方法,该方法可获得小波神经网络的最佳结构,并提高了故障的诊断效率。
研究了提升小波-支持向量机的模拟电路故障诊断方法。由于小波神经网络在故障类型数目较大时,故障的分类和诊断受网络结构复杂性和样本复杂性的影响较大,提出了基于支持向量机(SVM)的故障诊断方法。支持向量机在处理小样本学习、样本的非线性和高维模式识别方面具有优势,极大地减少故障分类的计算时间。同时,将不受傅立叶分析限制、易实现整数小波分解的提升小波分析方法用于模拟电路的特征提取,能够精确地反应故障响应信号的特征信息。采用提升小波变换(Lifting Wavelet Transform,LWT)优化故障特征,可获取分类性能更高的故障特征向量。LWT和SVM的有效结合,可获得很好的故障诊断精度和效率。
研究了二叉决策树支持向量机(DBT-SVM)优化的模拟电路层级故障诊断方法。支持向量机是模拟电路故障诊断的有效方法,但其用于模拟电路多故障诊断时,多分类扩展策略与诊断的效率、正确率密切相关。当故障类型数目较多及存在不同故障模式的特征向量相似度高甚至混叠的情况下,常规策略构造的多分类SVM因未考虑故障特征的聚类特性,在故障的误识率增加的同时诊断效率也下降。DBT-SVM在减少不可分区域及提高分类效率方面有一定的优势,但需要对结构进行优化才能获得更好的性能。本文研究了两种DBT-SVM优化方法,分别用最小生成树SVM(MST-SVM)和故障特征融合的模糊聚类二叉树(FDBT-SVM)方法优化SVM多分类的组合策略,根据故障类之间的可分性测度,形成故障子类之间具有较大分类间隔的DBT-SVM,优化的DBT-SVM方法解决了不可分故障区域问题,有效地提高故障诊断的准确率和效率。
Analog circuit testing and fault diagnosis play key roles in many fields such as circuit design, equipment manufacture and instrument maintenance. Currently, the study on the two aspects is still a challenging topic for academic researchers and engineers in electronic testing area. As the electronic technology develops rapidly, complexity and integration scale on analog circuits are increased simultaneously. More strict requirements for running reliability are desired also. It is difficult for traditional fault diagnosis theories and methods to solve the problems of fault diversity and complicacy, which are caused by continuous response, nonlinearity, and tolerance on component parameters due to the characteristics of circuits. Intensive study on intelligent fault diagnosis theories and methods with practicality and high performance is an urgent task. The fault feature extraction and effective fault identification methods are two critical parts for intelligent fault diagnosis. The optimization of feature extraction and design for classifiers for analog circuit fault diagnosis system are discussed deeply in this dissertation, based on modern testing technology with combination of wavelet analysis and support vector machine (SVM), which typically represent intelligent computing techniques. The main research contents and achievements are summarized as following:
Wavelet analysis based analog circuit fault detecting sensitivity and feature extraction optimization method are studied. Good localization on time-frequency domain of wavelet transformation provides better feature representation for faulty circuits than unitary time or frequency domain analysis. However, different wavelets present diverse resolution for fault recognition. Root mean square (RMS) score is proposed for measuring fault detecting sensitivity of wavelets for analog circuits in this paper. According to separating distance for classification, a measure criterion is presented for optimal wavelet basis selection, which leads to acquire better fault feature extraction. The experimental results show that this method can effectively improve fault identification accuracy.
Optimized analog circuit fault diagnosis approach based on wavelet neural network is researched. Aiming at the fact that conventional BP neural network usually converge to local minimum in fault diagnosis, wavelet analysis and neural network are combined to construct wavelet neural network (WNN) frame for fault diagnosis. WNN achieves high convergence speed and low fault diagnosis error rate due to the multi-resolution property of wavelet function with adaptive learning algorithm. To avoid the network gegenerality degradation caused by training convergence direction deviating from globally optimal point for structure redundance, genetic algorithm is used to optimize the structure and parameters of the network. Optimal WNN is obtained with simplified structure and higher fault diagnosis efficiency.
Lifting wavelet transform and support vector machines based analog circuit fault diagnosis method is studied thirdly. As WNN structure and pattern complexity greatly impact the results of fault classification and diagnosis under condition of large number of fault categories, support vector machine (SVM) algorithm is proposed to fault diagnosis. SVMs outperform neural network in many ways such as learning ability for small number of patterns, tackling nonlinearity and high dimension pattern recognition. It shows less time-consumption for computing in the same instance. Simultaneously, lifting wavelet analysis method, which is independent of Fourier analysis and easy to implement integral wavelet analysis, is applied to fault feature extraction of analog circuits for precise expression of faulty information. Lifting wavelet transform (LWT) presents better fault feature quality with higher partition characteristic. The presented LWT-SVM fault diagnosis approach acquires higher performance on diagnosis veracity and efficiency than neural network.
Optimized binary tree decision SVM (DBT-SVM) based hierarchical fault diagnosis methods for analog circuits are researched. SVMs are proved as an effective approach for fault diagnosis. When SVMs are used for multi-fault diagnosis in analog circuits, the diagnosis accuracy and efficiency largely depend on the extension strategies for multi-classification. On condition that there are large numbers of fault categories and high similarity even overlapping among various fault classes occur, conventional SVM combination strategies for fault diagnosis cannot get good results on fault recognition accuracy and efficiency due to not considering the clustering properties of fault features. DBT-SVM is able to decrease inseparable area to gain classification performance improvement, but it need structure optimization. This paper studies two ways for optimized DBT-SVM methods: minimum spanning trees SVM and fuzzy clustering optimized DBT-SVM (FDBT-SVM) with wavelet fault feature fusion. According to the separability measure of fault classes, multi-classification DBT-SVMs with large margins are constituted to avoid the inseparable fault area. The presented methods largely enhance fault diagnosis accuracy and efficiency comparing with conventional multi-classification SVM algorithms.
引文
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