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.,     

Diagnosis of Incipient Faults in Weak Nonlinear Analog Circuits

Aiming at the problem to diagnose incipient faults in weak nonlinear analog circuits, an approach is presented in this paper. The approach calculates the fractional Volterra correlation functions beforehand. The next step is to use the fractional Volterra correlation functions and different angle parameters of the fractional wavelet packet transform (FRWPT) to extract the fault signatures. Meanwhile, the computational complexity is analyzed. Then the variables of the fault signatures are constructed, which are used to form the observation sequences of the hidden Markov model (HMM). HMM is used to accomplish the fault diagnosis. The simulations show that the presented method can significantly improve the incipient fault diagnosis capability.     

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.     

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.     

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.     

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。     

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。     

基于网格搜索的改进SVM模拟电路故障诊断方法

针对模拟电路故障识别与诊断问题,提出了一种基于K最近邻的一对一SVM分类器(KNN-OSVM)的故障诊断方法。将K最近邻算法与用网格搜索法优化后的一对一SVM模型相结合,建立KNN-OSVM模型,有效解决了SVM因存在不可分域造成的误分问题,提高了故障诊断率。采用小波分析法提取输出端电压信号作为故障特征值,采用网格搜索对核函数、惩罚参数寻优。采用两个模拟电路进行仿真实验,并将改进的SVM与传统SVM进行对比。结果证明了该故障诊断方法的可行性。     

基于RS-PSO-SVM集成的模拟电路软故障诊断

针对模拟电路软故障诊断准确度不高的问题,提出一种基于粗糙集(RS)-粒子群算法(PSO)-支持向量机(SVM)集成的模拟电路软故障诊断方法。首先利用粗糙集理论对采集的模拟电路软故障特征信息进行维数约简,然后利用粒子群算法对支持向量机的参数进行优化,以提高支持向量机分类器的诊断性能,最后进行故障诊断。对四运放双二次高通滤波器进行仿真,实验结果表明,基于RS-PSO-SVM集成的模拟电路软故障诊断方法是有效的。与其他常用方法相比,该诊断方法具有更好的故障诊断性能。     

模拟电路模块级软故障特征的提取与故障诊断

介绍了一种模拟电路故障诊断中功能模块级的单模块软故障模型的建立理论和特征提取的方法,运用该方法提取的单模块故障特征不仅可以有效区分单模块电路中的各类故障,而且单模块电路的故障特征还可以在多模块电路的单故障和多故障的情况下应用。仿真结果表明,通过采样电路输出并进行相应的信号处理来提取故障模型,可以准确地定位多模块故障电路的故障部位。     

Fault Diagnosis of Analog Circuits Using Systematic Tests Based on Data Fusion

An analog fault diagnosis approach using a systematic step-by-step test is proposed for fault detection and location in analog circuits with component tolerance and limited accessible nodes. First, by considering soft faults and component tolerance, statistics-based fault detection criteria are established to determine whether a circuit is faulty by measuring accessible node voltages. For a faulty circuit, fuzzy fault verification is performed using the accessible node voltages. Furthermore, using an approximation technique, the most likely faulty elements are identified with a limited number of circuit gain measurements at selected frequencies. Finally, employing the D-S evidence theory, synthetic decision is made to locate faults according to the results of fault verification and estimation. Unlike other methods which use a single diagnosis method or a particular type of measurement information, the proposed approach makes use of the redundancy of different types of measurement information and the combined use of different diagnosis methods so as to improve diagnosis accuracy.     

基于小波分析和人工免疫算法的模拟电路故障诊断

受生物免疫系统自己-非己识别过程的启发,提出了一种基于小波分析和人工免疫算法的模拟电路故障诊断的新方法。该方法首先利用小波变换,归一化和主元分析法作为预处理提取模拟电路的最优故障特征向量,然后利用反面选择算法的检测器对故障信息进行检测,从而实现模拟电路故障的分类。计算机仿真证明该方法是可行的。     

基于DSP的模拟电路诊断系统的实现

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,深入研究了模拟电路的故障特征提取和故障诊断方法。用TMS320F2812对选定的待测电路在元件存在容差的条件下,实现了模拟电路软故障诊断。验证了使用DSP实现模拟电路故障诊断系统的可行性。     

A Novel Approach for Diagnosis of Analog Circuit Fault by Using GMKL-SVM and PSO

This paper presents a novel analog circuit fault diagnosis approach using generalized multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimization (PSO) algorithm. First, the wavelet coefficients’ energies of impulse responses are generated as features. Then, a diagnosis model is constructed by using GMKL-SVM method based on features. Meanwhile, the PSO algorithm yields parameters for the GMKL-SVM method. Sallen-Key bandpass filter and two-stage four-op-amp biquad lowpass filter fault diagnosis simulations are given to demonstrate the proposed diagnose procedure, and the comparison simulations reveal that the proposed approach has higher diagnosis precision than the referenced methods.     

A Novel Approach for Analog Circuit Fault Prognostics Based on Improved RVM

In order to estimate the remaining useful performance (RUP) of analog circuits precisely in real time, an analog circuit fault prognostics framework is proposed in the paper. Output voltages are extracted from circuit responses as features to calculate cosine distance which can reflect the health condition of analog circuits. Relevance vector machine (RVM) which has been improved by particle swarm optimization (PSO) algorithm is applied to estimate the RUP. Twelve case studies involving bandpass filter, highpass filter and nonlinear circuit have validated the predict performance of the approach. Simulation results demonstrate that the proposed approach has higher prediction precision.     

可诊断容差模拟电路软故障的新故障字典法

基于支路屏蔽的原理,本文提出一种可以诊断容差模拟电路软故障的新故障字典法.文中讨论了该方法的诊断原理和字典的建立方法,给出了容差情况下的仿真诊断例子.     

Analog circuit soft-fault diagnosis based on sensitivity analysis with minimum fault number rule

This paper proposes a new single or multiple soft analog circuit fault diagnosis approach based on the minimum fault number rule. It is based on the consideration that the fact that the probability of a single soft fault is much greater than that of a multiple fault if the related fault modes are independent. In this way, a new diagnostic strategy based on the circuit sensitivity analysis is proposed. The proposed strategy is an optimization-based one, whose objective is to find the minimum value of unaccepted parameter deviations which satisfy all those constraints, and the constraints equations are actually the voltage increment equations in all test nodes and the changing range of each element. The diagnosis process can fulfill the requirement of fault detection and fault isolation. It enables a fast or a real-time diagnosis in practical engineering. A DC circuit example and an AC circuit example are presented to demonstrate the effectiveness of the proposed approach.     

模拟电路故障实时诊断的滤波器方法

本文讨论了一种模拟电路故障实时诊断的滤波器方法,该方法首先用状态空间模型来描述模拟电路,然后利用强跟踪滤波器对模型进行状态与参数联合估计,再利用修正的Bayes分类算法对故障进行检测与分离,从而实现故障的实时诊断.实验结果证明了本方法是有效的.     

Fault Diagnosis of Linear Analog Electronic Circuit Based on Natural Response Specification using K-NN Algorithm

This paper reports a novel method for parametric fault diagnosis in linear analog electronic circuits using distance weighted cosine K-Nearest Neighbours (K-NN) algorithm that performs data classification on the basis of cosine similarity between data features or attributes. In this approach the analog electronic Circuit Under Test (CUT) is represented in the form of a transfer function model and natural response specifications of the system such as damping ratio, natural frequency and static gain of the system are extracted as features from this model. For experimentation purpose a second order Sallen-Key band pass filter and a fourth order Chebychev Type 1 low pass filter is considered, the corresponding fault classes are created for each of the circuit. The parameter values of the passive components in the system have been varied to derive the features, and each component whose tolerance varied is labelled with a corresponding fault class. The proposed methodology classifies faulty classes with accuracy greater than 95%.

     

Structural Fault Modeling and Fault Detection Through Neyman–Pearson Decision Criteria for Analog Integrated Circuits

A new approach for analog fault modeling and simulation is presented. The proposed approach utilizes the sensitivity of the circuit’s DC node voltages to the process variations and consequently the current deviance so as to differentiate the faulty behavior. A systematic method is proposed for the fault discrimination to minimize the probability that the circuit is accepted as a fault-free when it is faulty. Tests are generated and evaluated taking into account the potential fault masking effects of process spread on the faulty circuit responses. The introduced fault model is validated on a time-interleaved sample-and-hold circuit. Simulation results demonstrate the effectiveness of the model.