【作者】 刘全喜；

【导师】 何怡刚； 谢宏； 周炎涛；

【作者基本信息】 湖南大学 ， 电工理论与新技术， 2006， 硕士

【摘要】 模拟电路故障诊断是一门不断发展中的综合交叉性学科,是在不断吸收其它学科和领域的新理论、新技术和新方法的基础上向前发展的一门应用技术。虽然经过了四十多年的发展,也已经形成了一系列的诊断理论和方法,但由于模拟电路特别是有容差的大规模模拟电路故障的多样性和复杂性,使得可用于诊断容差模拟电路的故障和大规模模拟电路软故障的方法还十分有限。人工神经网络理论近年来取得了快速发展,已开始在各个研究领域广泛应用。本文采用前向人工神经网络,根据模式识别原理,较系统地阐述了模拟电路故障诊断的神经网络方法,并在PC机上实现了无限元子隐层神经网络的核心算法。本文在现有文献理论研究的基础上实现了采用BP算法前向多层神经网络对直流测试下模拟电路硬故障的诊断方法。其特点是采用少量典型特征样本作为BP网络的训练样本,获得训练样本的代价小,减少了测前工作量,同时诊断速度快,在考虑元件容差时仍有好的诊断效果。文中介绍了线性电路单一软故障和双软故障所具有的电压增量空间特性和统一特征概念。根据这一概念,本文实现了采用子隐层型BP网络实现线性电路软故障诊断的方法,获得了较好的诊断效果。文中给出的线性电路单故障特征可根据电路正常状态下参数值来计算;给出的线性电路双故障特征可由元件的单故障特征来获得,简化了测前模拟工作。根据实际项目的要求,在PC机上实现了实用的BP算法。经过改进,实现了支持无限元的子隐层BP神经网络的核心算法。该算法程序功能强大,使用方便简单,具有很强的兼容性和扩展性。这个算法允许使用者根据实际应用项目的复杂程度自行设计神经网络的结构。更多还原

【Abstract】 The fault diagnosis of the analog circuits is an advanced synthesis intercrossed subj ect,which is an application technology absorbing the new theory,technology and method in other subjects and fields.In the past 40 years,it has developed a great deal of theory and approaches.But the approaches are limited to deal with fault diagnosis because of the variety and complexity of the analog circuits,especially the large-scale analog Circuits with the tolerance or the SOft fault.ArtifiCial Neural Networks (ANN)which have been one of the most active research areas recently can be contributed to solve problems in various practical fields.In this paper, analog circuit fault diagnosis approach based on pattern recognition,and ANN is expatiated.In the paper,based on the existing literature research foundation an analog circuit catastrophic fault location approach by using feedforward networks with back—propagation learning is realized.By this approach,the simulation require ments before test are reduced because fewer training samples are needed,and the fault location process is fast.This method is very efficient in location of single hard fault wit component tolerances.The measureme nt space feature and the general characterization concept of single and double soft fault in linear circuits are presented.According to this concept,a linear circuits soft fault location approach using subhidden layer BPNN is established with element tolerance,and it is shown that this approach is successful in fault location.A double fault feature extraction., method for linear circuits with element toleranceS are alSO presented.By this method,the double fault general characterization can be calculated by single fault general characterization which can be calculated by single fault feature.This method makes simulation before test more simple.According to the actual project requirements,practical BP algorithm is presented and realized on personal computer.After further optimization and improvements, a subhidden layer BPNN algorithm which support unlimited units is realized. The algorithm is powerful,simple and user—friendly,highly compatibility and expansion . According to the complexity of actual project , this algorithm allow users designing the structure of the neural network.更多还原