基于PSpice与LM算法的模拟电路故障诊断方法

采用小波神经网络与Levenberg-Marquardt算法相结合的方法,对模拟电路进行故障诊断;用小波对冲击响应信号进行多尺度分解,进行归一化后,提取故障特征信息作为神经网络的输入而进行分类。将PSpice与Matlab结合不但能有效的诊断模拟电路,且在收敛性和故障准确性上有了大幅提高。实验仿真表明,通过该方法构造的样本集训练出的网络稳定性高于传统方法,适用于神经网络。     

基于仿真和编码理论的数模混联电路故障诊断方法研究

数模混联电路的设计被广泛运用于各种电路系统。而模拟电路和数字电路在故障模式、测试方法上的显著差别给数模混联电路的测试带来了很大困难。基于对输出电平的16进制编码,将传统的故障字典法推广到可以诊断数模混联电路的新故障字典法。利用EDA的辅助分析,在PSpice仿真环境下,从故障建模、故障注入,到电路仿真,数据分析,再到建立故障字典以及故障诊断,建立了一套有效的基于仿真的数模混联电路的测试诊断方法。并给出了仿真实例,对数、模混联电路的故障诊断具有推广意义。     

基于SLPS的模拟电路故障样本自动获取技术

故障样本数据的获取是模拟电路故障诊断中最基本的步骤。为了实现短时间内多次进行故障注入、获取大量样本数据,提出了基于SLPS的样本数据自动获取技术。利用SLPS将PSpice与Matlab结合,采用Matlab编程,实现故障模拟电路仿真数据获取的自动化。实际应用表明该方法操作简便,自动化程度高。     

粒子群算法优化特征和神经网络的模拟电路故障诊断

模拟电路受到自身特性和外界环境的影响,故障变化具有非线性、时变性,针对当前模拟电路故障诊断模型的特征和分类器参数不匹配的难题,提出一种粒子群算法选择特征和神经网络的模拟电路故障诊断模型。首先对当前模拟电路故障诊断现状进行分析,指出它们存在的缺陷;然后提取模拟电路故障诊断特征,利用神经网络作为模拟电路故障诊断分类器;最后采用粒子群算法对模拟电路故障特征与神经网络参数进行优化,在Matlab 2012平台进行了仿真实验。结果表明,该模型的模拟电路故障诊断性能要远远优于其他参比模型,具有广泛的应用前景。     

开关电流电路故障诊断技术的初步研究

开关电流(SI)技术是模拟采样数据处理技术。介绍了开关电流电路的基本单元结构,讨论了影响故障诊断精度的主要因素,重点分析了电荷注入误差。对于开关电流电路故障诊断的仿真,介绍了一种故障模型,仿真后通过误差电流的不同来进行故障定位,但在PSpice仿真上实现还存在一定困难。     

基于LMD和SVM算法的模拟电路故障诊断

对模拟故障电路进行特征提取与分类是模拟电路诊断的两个重要环节。现有方法多对时域响应信号进行小波变换以提取故障特征,并用神经网络或支持向量机方法实现对故障进行分类。为提高模拟电路故障诊断率,提出一种局域均值分解(LMD)与SVM相结合的新算法。该算法运用局域均值算法(LMD),将其自适应地分解为一系列单分量调幅-调频信号(PF),通过提取电路正常和故障状态的特征,运用SVM对其分类,获得诊断效率。仿真实验结果表明,该方法对模拟电路的故障诊断精度达到98%以上,适用于模拟电路的故障诊断。     

基于小波神经网络的模拟电路故障诊断研究

文章提出了一种基于小波神经网络的模拟电路故障诊断方法。这种方法采用正弦信号作为被测电路的输入激励,在时域中对输出信号采样来构造神经网络的训练和测试样本,将自适应学习率及附加动量BP算法训练后的小波神经网络应用于容差模拟电路故障诊断中。仿真试验表明,该方法减少了故障诊断时间和提高了网络的平均诊断正确率。     

模拟数字电路故障诊断新方法

利用小波变换与神经网络相结合的方法,采用能量分布特征提取方法和改进BP算法,给出了一种基于小波变换和BP神经网络相结合的模拟电路故障诊断方法.用正弦信号仿真模拟电路,应用小波变换对模拟电路的采样信号进行多尺度分解,再进行能量分布特征提取,然后利用神经网络对各种状态下的特征向量进行分类识别,实现模拟电路故障诊断.在用神经网络诊断模拟电路的基础上,进行了将神经网络用于数字电路单故障诊断的研究.对两者的实例电路仿真结果表明,神经网络可以有效、方便地实现电路的故障检测和定位,准确率高,为故障诊断的研究提供了一种新思路.     

基于BP神经网络的模拟电路诊断系统研究

以现代测试技术、信号处理、信息融合等理论为基础,以神经网络在模拟电路故障诊断中的应用为主线,详细讨论BP神经网络在模拟电路故障诊断中的应用和故障特征提取方法.采用多频组合法建立了故障样本集.对选定的待测电路在元件存在容差的条件下,仿真验证了BP神经网络应用于模拟电路故障诊断的可行性.     

基于电路传递函数的故障诊断方法研究

本文提出的故障诊断方法主要是针对模拟电路的故障诊断,首先应用拉氏变换确定模拟电路的传递函数,其次根据已确定的正常电路传递函数,由不同故障类型计算相应的故障传递函数,生成故障诊断表。在进行故障诊断时,根据输入输出值计算当前的电路传递函数,把该函数与故障诊断表进行比对完成故障诊断。本文最后利用Multisim软件仿真了一个典型的模拟电路,并对该电路进行故障设置,计算了故障诊断表,应用软件仿真结果与理论计算的结果进行了比较,验证了该方法有效性。     

Analog Fault Diagnosis Using Conic Optimization and Ellipsoidal Classifiers

This paper introduces a new fault diagnosis strategy for analog circuits based on conic optimization and ellipsoidal classifiers. Ellipsoidal classifiers are trained for efficient and accurate fault classification of the circuit under test (CUT). In the testing phase, the output of the ellipsoidal classifiers is used to isolate the actual CUT fault. The constructed classifiers exhibit high classification rate with competitive computational complexity even if the CUT has overlapping faults. Experimental results demonstrate the superior performance of the ellipsoidal classifiers in analog fault diagnosis.     

基于分数阶相关的模拟集成电路软故障诊断* Deng Yong(邓勇), Shi Yibing(师奕兵)and Zhang Wei(张伟)

针对模拟集成电路软故障诊断的难题,提出了基于分数阶相关的方法。首先,利用分数阶小波包将待测试电路(CUT)的Volterra级数进行分解,计算出分数阶相关函数。然后,用得到的分数阶相关函数构造出待测试电路的故障特征。通过对故障特征的比较,可以将待测试电路的各种软故障状态进行辨识并对故障实现定位。标准电路的仿真实验描述了这一方法并验证了该方法对模拟集成电路软故障诊断的有效性。     

Soft Fault Diagnosis in Analog Circuits Based on Bispectral Models

Aiming at the problem to locate soft faults in analog circuits, a new approach based on bispectral models is proposed. First, the Volterra kernels of the circuit under test (CUT) are calculated. Then, the Volterra kernels are used to construct bispectral models. By comparison with the fault features of the constructed models, soft faults of linear and weak nonlinear components in the analog circuit are identified and the faults are located. Simulations and experiments show the effectiveness of the proposed method in analog circuits.,     

Selection of Input Stimulus for Fault Diagnosis of Analog Circuits Using ARMA Model

The paper addresses the problem of fault diagnosis of analog circuits based on dictionary approach. The proposed approach first identifies an adequate set of test frequencies to optimize the process of detection and isolation of simulated fault scenarios. The circuit under test (CUT) is then excited by an input stimulus composed of a set of sinusoidal waveforms with the selected test frequencies. The circuit response, at different fault scenarios, is preprocessed by an autoregressive moving average (ARMA) model to yield a set of features formulating the fault dictionary. Collected features are utilized to train and test a back-propagation (BP) neural network (NN) based classifier. Demonstrative results from soft fault simulation of two active circuit examples prove the excellent effectiveness of the proposed algorithm.     

Fault detection in analogue circuits using hybrid evolutionary algorithm and neural network

With the development of analog integrated circuits technology and due to the complexity, and various types of faults that occur in analog integrated circuits, fault detection is a new idea, has been studied in recent decades. In this paper a three amplifier state variable filter is used as circuit under test (CUT) and, a hybrid neural network is proposed for soft fault diagnosis of the CUT. Genetic algorithm (GA) has the powerful ability of searching the global optimal solution, and back propagation (BP) algorithm has the feature of rapid convergence on the local optima. The hybrid of two algorithm will improve the evolving speed of neural network. GA-BP scheme adopts GA to search the optimal combination of weights in the solution space, and then uses BP algorithm to obtain the accurate optimal solution quickly. Experiment results show that the proposed GA-BP scheme is more efficient and effective than BP algorithm.     

基于闭环集成运放的模拟电路模块级软故障诊断

基于斜率故障模型,提出了一种诊断模拟电路中基于闭环集成运算放大器的模块级软故障的字典法.在由闭环运放组成的模拟电路中,通过对电路以闭环运放及与其输入直接相连的元件看作一个整体划分模块,对各个模块中的任一元件或进行宏模型替代之后的运放等效电路,利用电路中的两节点电压增量计算出的斜率作为统一故障特征,建立故障字典,实现电路中相应模块包含的运放和所有元件的软故障诊断.给出了运放的等效宏模型和模块级软故障的诊断步骤,并用仿真实例证明了该诊断方法的有效性.     

A New Analog Circuit Fault Diagnosis Method Based on Improved Mahalanobis Distance

This paper presents a new analog circuit fault diagnosis method based on improved Mahalanobis Distance. The Mahalanobis Distance is improved according to the characteristics of analog circuit, and then introduced into analog circuit fault detection. First, the circuit testability was analyzed, and the relation of ambiguity groups was determined on the basis of the test matrix, and then the separable potential faulty components under the assumption of single fault were also determined. Finally, the suspicious components could be classified using the improved Mahalanobis Distance according to the feature values of the test points, so as to reduce the number of classes and enhance the speed when classifying faults. The experiment shows that the method can achieve fast analog circuit fault diagnosis and better results of analog circuit diagnosis detection.     

OBIST methodology incorporating modified sensitivity of pulses for active analogue filter components

In this paper, oscillation-based built-in self-test method is used to diagnose catastrophic and parametric faults in integrated circuits. Sallen–Key low pass filter and high pass filter circuits with different gains are used to investigate defects. Variation in seven parameters of operational amplifier (OP-AMP) like gain, input impedance, output impedance, slew rate, input bias current, input offset current, input offset voltage and catastrophic as well as parametric defects in components outside OP-AMP are introduced in the circuit and simulation results are analysed. Oscillator output signal is converted to pulses which are used to generate a signature of the circuit. The signature and pulse count changes with the type of fault present in the circuit under test (CUT). The change in oscillation frequency is observed for fault detection. Designer has flexibility to predefine tolerance band of cut-off frequency and range of pulses for which circuit should be accepted. The fault coverage depends upon the required tolerance band of the CUT. We propose a modification of sensitivity of parameter (pulses) to avoid test escape and enhance yield. Result shows that the method provides 100% fault coverage for catastrophic faults.