基于PSO-RBF神经网络的模拟电路故障诊断研究

针对模拟电路故障诊断的难点和传统诊断方法的不足之处,提出了一种基于PSO算法优化的RBF神经网络模拟电路故障诊断方法。为了约简网络结构从而提高诊断效率,采用主成分分析方法对故障特征进行有效提取。针对RBF网络传统训练算法中隐层节点中心及基函数宽度选取困难问题,提出采用PSO算法来优化训练RBF网络,以提高网络的训练速度和泛化性能。最后,通过电路仿真对所提方法的有效性进行了验证。     

基于BP神经网络的模拟电路故障诊断

模拟电路的可靠性决定了电子设备的可靠性.针对目前BP网络在模拟电路故障诊断中存在的容差和非线性等问题,提出多频组合法,对电路中的软故障进行诊断.通过在电路输入端施加3 V的正弦激励,选取不同的测试频率,对测试点进行测试,得到各待测元件的故障值.该系统解决了模拟电路中的容差问题,提高了诊断率,并在仿真中得以证明.     

模拟电路故障诊断中的特征信息提取

在运用BP神经网络进行模拟电路故障诊断过程中,代表故障特征的网络输入至关重要。分析了常见特征信息提取和故障诊断方法,提出一种基于多测试点、多特征信息原始样本集的新方法。运用这种方法构造原始故障特征集,然后作为BP神经网络的输入对网络进行训练,仿真结果表明,通过该方法构造的样本集训练出来的网络对模拟电路故障诊断的正确率优于传统方法,证明了该方法在模拟电路故障诊断中的可行性,为模拟电路的故障诊断提供了一种新方法。     

容差模拟电路的软故障诊断的小波方法

给出了容差模拟电路软故障诊断的小波与量子神经网络方法,利用小波分析,取其能反映故障信号特征的成分做为电路故障特征,再输入给量子神经网络,不仅解决了一个可测试点问题,并提高了辨识故障类别的能力,而且在网络训练之前,利用主元分析降低了网络输入维数。实验证明了这种方法的可行性与适用性。     

基于小波包和自组织网络的模拟电路故障诊断

提出了一种基于小波包变换和SOFM网络相结合的模拟电路故障诊断方法.利用小波包分解作为预处理和消噪工具,对电路采样信号进行多尺度分解和消噪,然后进行能量特征提取、归一化,作为样本输入给神经网络.用SOFM网络竞争层的神经元,对样本数据进行故障识别分类,克服了BP网络隐层神经元选取困难的缺点.详细叙述了诊断原理和步骤,并给出了诊断实例.     

粒子群RBF神经网络电力电子电路故障诊断

为检测和诊断电力电子电路中的故障,获得更高的诊断精确度,提出粒子群算法优化RBF神经网络的故障诊断方法.与基本RBF神经网络相比,粒子群RBF神经网络可以提高系统的收敛速度和精度.把通过特征提取获得的电力电子电路故障特征量作为神经网络的输入,利用训练好的粒子群优化后的RBF神经网络进行故障诊断.仿真结果表明,实际输出与期望输出基本吻合,具有良好的分类效果,能够提高诊断精确度,对于电力电子电路的故障诊断是一种有效的方法.     

三维片上网络故障及拥塞感知的容错路由器设计

 三维片上网络中路由器的输入端口和交叉开关出现故障,将严重影响整个网络的性能,因此文章提出了一种故障及拥塞感知的容错路由器.通过增加一个冗余的输入端口和旁路总线,不仅能实现对输入端口和交叉开关容错的目的,而且还能在没有端口故障的情况下使用冗余端口有效地解决拥塞问题.实验表明此容错机制能够使得网络在故障路由器多、拥塞严重的情况下,仍然保持良好的性能.     

RBF网络与BP网络在非线性电子器件建模中的性能比较

针对BP网络在非线性电子器件建模应用中的不足,尝试了利用RBF网络建模的新方法,即首先用RBF网络逼近器件的非线性特性,然后再将得出的网络结构用Pspice电路描述语言描述来建立非线性电子器件的模型,并对这两种网络在建模时的优缺点进行了比较.     

IP网络分组传送性能讲座第2讲:IP网络性能参数与网络服务质量

1 IP网络性能测试的环境与条件 为了在IP网络中对IP业务的性能进行测试,要规定一些环境条件,并且定义网络性能的测试点(MP,Measurement Point).如图1所示.     

电子产品测试点优化综述

电子产品故障预测是故障预测与健康管理技术的关键技术之一.基于此,对面向电子产品故障预测与健康管理的测试点选择及优化方法进行回顾.首先对该技术发展状况及相关技术进行综述,随后详细介绍了电子产品测试点的选取和优化方法,包括测试点优化过程中涉及的故障模式和危害性分析技术、电子设备的故障检测率与失效率的计算方法、电子产品测试点优化的一般步骤、测试点优化过程中需要解决的关键问题、电子产品的电路故障诊断方法的选取以及测试点优化算法和优化模型的选取问题,最后给出我国积极开展电子产品故障预测与健康管理技术的建议.     

Fault Diagnosis of Nonlinear Analog Circuits Using Neural Networks with Wavelet and Fourier Transforms as Preprocessors

A neural-network based analog fault diagnostic system is developed for nonlinear circuits. This system uses wavelet and Fourier transforms, normalization and principal component analysis as preprocessors to extract an optimal number of features from the circuit node voltages. These features are then used to train a neural network to diagnose soft and hard faulty components in nonlinear circuits. Our neural network architecture has as many outputs as there are fault classes where these outputs estimate the probabilities that input features belong to different fault classes. Application of this system to two sample circuits using SPICE simulations shows its capability to correctly classify soft and hard faulty components in 95% of the test data. The accuracy of our proposed system on test data to diagnose a circuit as faulty or fault-free, without identifying the fault classes, is 99%. Because of poor diagnostic accuracy of backpropagation neural networks reported in the literature (Yu et al., Electron. Lett., Vol. 30, 1994), it has been suggested that such an architecture is not suitable for analog fault diagnosis (Yang et al., IEEE Trans. on CAD, Vol. 19, 2000). The results of the work presented here clearly do not support this claim and indicate this architecture can provide a robust fault diagnostic system.     

基于PCA和PNN的模拟电路故障诊断

为了解决模拟电路故障识别困难的问题,提出一种基于主成分分析和概率神经网络的模拟电路故障诊断方法。该方法对采集到的模拟电路故障信息进行特征提取,将提取的故障特征归一化处理后输入概率神经网络,进行训练和故障模式的分类识别。实验结果表明,该方法是有效的,具有较高的故障诊断率。     

Evolutionary derivation of optimal test sets for neural network based analog and mixed signal circuits fault diagnosis approach

Testing issues are becoming more and more important with the quick development of both digital and analog circuit industry. In this paper, we study the utilization of evolutionary algorithms for optimal input vectors derivation of neural network based analog and mixed signal circuits fault diagnosis approach and compare the results with normal method. We have introduced a new procedure which uses the n-detection test set concept and selects the input samples in a way that for each case of fault injection, there will be at least n sample to activate that fault. This procedure performs the optimization in two ways. The first one called speed method generates samples in a way that acceptable decision strength and lower training phase duration would be achieved. The second one called stamina method generates samples in a way that best decision strength and higher training phase duration would be achieved. Experimental results demonstrate that the obtained input voltages yields fault diagnosis with increased fault coverage and high decision strength.     

Fault Detection of Analog Circuits Using Network Parameters

A new method to detect component faults in analog circuits is proposed in this paper. Network parameters like driving point impedance, transfer impedance, voltage gain and current gain are used to detect component faults in analog circuits as these network parameters are sensitive to the components of the circuit. Using montecarlo simulation each component of the circuit is varied within its tolerance limit and the minimum and the maximum values of each network parameter are found for fault free circuit. At the time of testing, the network parameters are found for the injected fault and if any one or more network parameters is exceeding its predetermined bound limits then the circuit is confirmed faulty. The proposed method is validated through second order Sallenkey band pass filter and fourth order Chebyshev low pass filter circuits. Numerical results are presented to clarify the proposed method and prove its efficiency.     

基于小波神经网络和相位差的模拟电路故障诊断

根据模拟电路中存在噪声的问题,提出利用相位差来进行故障诊断。通过正常模式和故障模式下相位差和幅值差的特征提取,建立故障字典。然后利用小波神经网络对故障电路建模,基于该网络学习收敛快,对网络输入不太敏感的特点,实现故障诊断。通过实例证明,该方法不但诊断准确,而且很切合实际模拟电路。     

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。     

基于SOM神经网络的航舵故障分类方法

为解决航舵故障诊断的复杂非线性模式分类问题,提出一种基于自组织特征映射(SOM)神经网络的航舵故障诊断方法,构造一个2层SOM神经网络,训练后多个权值向量位于输入向量聚类中心,实现快速有效的自适应分类.仿真结果表明:SOM网络经过100次训练即可实现聚类,对有限故障测试样本分类准确率可达90％,对航舵故障诊断具有一定的参考价值.     

Fault Diagnosis of Analog Circuits Using Bayesian Neural Networks with Wavelet Transform as Preprocessor

We have developed an analog circuit fault diagnostic system based on Bayesian neural networks using wavelet transform, normalization and principal component analysis as preprocessors. Our proposed system uses these preprocessing techniques to extract optimal features from the output(s) of an analog circuit. These features are then used to train and test a neural network to identify faulty components using Bayesian learning of network weights. For sample circuits simulated using SPICE, our neural network can correctly classify faulty components with 96% accuracy.