Fault isolation in analog circuits using a fuzzy inference system

This paper introduces a fuzzy inference system (FIS) for single analog fault diagnosis. The ability of fuzzy logic to encode structured knowledge in a numerical framework is exploited in isolating faults in analog circuits. A training set that simulates the behaviour of the circuit due to a set of anticipated single faults as well as the fault-free situation is first constructed. For each anticipated fault, this set relates the circuit measurements to the corresponding deviation in the faulty circuit element from its nominal. These measurements and the deviations in circuit elements are both fuzzified into appropriate linguistic fuzzy values. A fuzzy rule base for each fault that characterizes the circuit response by linking symptoms to causes is built. The outputs of the fuzzy rule bases are then defuzzified to recover crisp values for the deviations in circuit elements. A fault diagnosis procedure that utilizes the proposed FIS is also presented along with a brief analysis and comparison with a number of existing artificial intelligence-based techniques. A test example that demonstrates the potential of this procedure in fault isolation is illustrated.     

Statistical Fault Analysis

Statistical Fault Analysis, or Stafan, is proposed as an alternative to fault simulation of digital circuits. This method defines Controllabilities and observabilities of circuit nodes as probabilities estimated from signal statistics of fault-free simulation. Special Procedures deal with these quantities at fanout and feedback nodes. The computed probabilities are used to derive unbiased estimates of fault detection probabilities and overall fault coverage for the given set of input vectors. Among Stafan's advantages, fault coverage and the undetected fault data obtained for actual circuits are shown to agree within five percent of fault simulator results, yet CPU time and memory demands fall far short of those required in fault simulation. The Computational complexity added to a fault-free simulator by Stafan grows only linearly with the number of circuit nodes.     

Model-based fault diagnosis using fuzzy matching

A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant     

一种基于神经网络的电路故障诊断方法

本文提出了利用遗传算法优化神经网络各层权值的方法，并以实际电路为例进行了仿真分析，从仿真结果可以看出，本文方法能对电路故障进行准确的诊断，而且具有较强的抗噪声能力，为电路故障的诊断应用提供了一定的有意义的研究。     

The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.     

Fault diagnosis based on identified discrete-event models

Fault diagnosis of Discrete-Event Systems consists of detecting and isolating the occurrence of faults within a bounded number of event occurrences. Recently, a new model for discrete-event system identification with the aim of fault detection, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT), has been proposed in the literature. The model is computed from observed fault-free paths, and represents the fault-free system behavior. In order to obtain compact models, loops are introduced in the model, which implies that sequences that are not observed can be generated leading to an exceeding language. This exceeding language is associated with possible non-detectable faults, and must be reduced in order to use the model for fault detection. After detecting the fault occurrence, its isolation is carried out by analyzing residuals. In this paper, we present a fault diagnosis scheme based on the DAOCT model. We show that the proposed fault diagnosis scheme is more efficient than other approaches proposed in the literature, in the sense that the exceeding language can be drastically reduced, reducing the number of non-detectable fault occurrences, and, in some cases, reducing also the delay for fault diagnosis. A practical example, consisting of a plant simulated by using a 3D simulation software controlled by a Programmable Logic Controller, is used to illustrate the results of the paper.     

利用门限进行电路故障诊断

如何设定故障门限对电路故障诊断结果影响很大。简述了电路故障诊断基本概念 ,重点介绍了如何利用故障门限进行电路故障检测和故障隔离。介绍了如何通过电路仿真分析来设置故障门限 ,并结合实例进行了具体分析     

The incremental agreement

To achieve reliable distributed systems, the fault-tolerance must be studied. One of the most important problems of fault-tolerance issues lies in the Byzantine Agreement (BA) problem. The primary issue surrounding BA is that fault-free processors must obtain common agreement even in cases where faults persist. In this field, the fault diagnosis protocol has been proposed so that each fault-free processor detects/locates a common set of faulty processors. However, in this study, the incremental agreement is invoked to make each processor able to agreement upon executing the fault diagnosis protocol using minimal rounds of message exchange in the presence of dual failure characteristics of processors.     

超立方体并行计算机的一个新型故障诊断算法

提出了超立方体并行计算机的一个新型系统级故障诊断算法.与现有诊断算法相比,该算法能够在系统中存在较多故障处理器的情况下,正确定位全部故障处理器（代价是至多误诊断三个无故障处理器）.另外,该算法的时间复杂度与最好的现有算法相当.     

约束分段仿射系统的鲁棒一步预测控制

针对一类具有附加有界扰动的离散时间约束分段仿射系统,提出了一种鲁棒低复杂性的模型预测控制方法,即鲁棒一步控制。首先,基于最大鲁棒正不变集,计算系统的最大鲁棒可稳定集并作为第一步预测状态的约束集,使得产生的滚动时域控制器可以在较小的预测时域内控制最大鲁棒可稳定集。然后,在最大鲁棒正不变集外,通过构建线性矩阵不等式来寻找而二次李雅普诺夫函数以证明其鲁棒稳定性。两个步骤分别确保控制器的可行性和闭环系统的鲁棒稳定性。大量的仿真例子表明,和已有的控制方法相比,所得的鲁棒一步控制器具有更低的复杂性。     

模拟电路仿真与故障字典生成方法

针对模拟电路故障诊断需求，对模拟电路仿真和故障字典生成的实现方法进行了研究。首先，设计软件实现总体结构，从软件界面层、运行层、数据访问层、数据层等方面搭建过程中需要的电路知识管理、工程管理、电路仿真设置、仿真运行、故障字典生成和辅助故障诊断等功能框架；进而通过创建被测电路仿真原理图、新建仿真工程、仿真设置、故障注入等详细设计，实现被测电路的功能仿真和故障仿真；然后根据仿真结果，利用故障字典生成器生成所有故障模式下的整数编码故障字典；最终辅助诊断功能，验证故障字典是否可用。通过对演示板12个故障注入后的仿真数据做故障字典生成训练，并完成了对实测数据的故障定位，验证了该方法的可行性，为模拟电路故障诊断提供了实用的测试方法和手段，具有较好的实用价值。     

基于观测器的发电机功角与故障估计

针对发电机功角与故障估计这个问题,提出一种利用观测器的方法.该方法通过对系统数学模型进行状态变换,将数学模型分为含故障与不合故障两部分.对不合故障部分设计观测器,然后经过状态变换可以得到功角信息.在故障估计过程中,通过增广误差系统设计增广观测器可以得到故障的幅值.最后通过Matlab仿真分析,验证基于观测器功角与故障估计方法的有效性,结果表明该方法具有速度快、误差小的优点.     

PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

Fault diagnosis of analog circuits is a key problem in the theory of circuit networks and has been investigated by many researchers in recent decades. In this paper, an active filter circuit is used as the circuit under test (CUT) and is simulated in both fault-free and faulty conditions. A modular neural network model is proposed in this paper for soft fault diagnosis of the CUT. To optimize the structure of neural network modules in the proposed scheme, particle swarm optimization (PSO) algorithm is used to determine the number of hidden layer nodes of neural network modules. In addition, the output weight optimization–hidden weight optimization (OWO-HWO) training algorithm is employed, instead of conventional output weight optimization–backpropagation (OWO-BP) algorithm, to improve convergence speed in training of the neural network modules in proposed modular model. The performance of the proposed method is compared to that of monolithic multilayer perceptrons (MLPs) trained by OWO-BP and OWO-HWO algorithms, K-nearest neighbor (KNN) classifier and a related system with the same CUT. Experimental results show that the PSO-optimized modular neural network model which is trained by the OWO-HWO algorithm offers higher correct fault location rate in analog circuit fault diagnosis application as compared to the classic and monolithic investigated neural models.     

Genetically Evolved Neural Networks for Fault Classification in Analog Circuits

A new fault classification system for analog circuits is presented. The proposed system utilises the pattern recognition potential of neural networks and the population-based search strategy of genetic algorithms in detecting and isolating faults in analog circuits. Features that characterise the circuit behaviour under fault-free and fault situations are first simulated or measured. An unsupervised fault-grouping algorithm that estimates the overlaps between different faults in the features space is then introduced. Accordingly, a suitable training set is constructed and employed to train a population of genetically evolved neural networks to recognise circuit faults. A two-phase analog fault classification strategy is also developed. Experimental results demonstrate the high classification accuracy of the proposed system. ID="A1" Correspondence and offprint requests to: M.A. El-Gamal, Department of Engineering Physics Mathematics, Cairo University, Giza, Egypt Email: mhgamal@alpha1-eng.cairo.eun.eg     

非线性电路的神经网络故障诊断方法

针对非线性动态电子电路,提出一种基于神经网络的故障诊断方法。通过故障字典的建立,对电路故障响应进行预处理后得到的故障特征作为神经网络的输入,然后利用神经网络对各种状态下的特征向量进行分类决策,对故障类别进行辨识,并对电路进行了可测性分析,从而实现非线性电路的故障诊断。详细的仿真过程及结果表明, 该方法有效地解决了非线性电路辨识难的问题,能较好地对故障模式进行分类,取得了满意的诊断效果。     

两种固体氧化物燃料电池系统的故障建模与仿真对比研究

固体氧化物燃料电池(SOFC)系统的建模方式较多,而基于机理模型的故障诊断是能够通过系统的动态趋势辨别故障的有效手段之一,但该方法对机理模型的准确性有要求.此外,不同的燃料供给系统采用的系统结构也是有差异的,进而导致在相同故障下SOFC系统的动态响应也是不同的.因此,本文基于两种燃料供应方式,提出了分别以纯氢气和天然气作为燃料的SOFC系统结构,并基于原有机理知识进行MATLAB/Simulink系统建模.经与真实SOFC系统实验对比,搭建的系统模型能够有效模拟系统在无故障状态下的动态变化;另外,在无故障模型的基础上,分别加入两类常见故障,其一为风机故障,其二为燃料供应管路泄露.最后通过仿真分析,明确了所搭建模型的合理性,且发现了两种燃料供应对SOFC系统热响应特性是不同的,对SOFC系统故障的检测和设备选型具有重要意义.     

液压系统故障注入与故障诊断仿真研究

通过机-电-液联合仿真等技术手段,构建了基于AMESim和MATLAB软件构建故障诊断数字仿真验证系统,实现机电液系统的故障模拟、注入与诊断仿真功能。并以某典型液压举升回路系统为对象,完成故障模式及诊断方法的研究,建立仿真模型、完成故障注入和诊断过程的演示验证。结果表明,该仿真系统可应用于类似系统、不同工况、不同故障模式的故障注入与故障诊断仿真,极大程度上提高了液压系统的可靠性的设计。     

Node grouping in system-level fault diagnosis

With the popularization of network applications and multiprocessor systems,dependability of systems has drawn considerable attention.This paper presents a new technique of node grouping for system-level fault diagnosis to simplify the complexity of large system diagnosis.The technique transforms a complicated system to a group network,where each group may consist of many nodes that are either fault-free or faulty.It is proven that the transformation leads to a unique group network to ease system diagnosis.Then it studies systematically one-step t-faults diagnosis problem based on node groupling by means of the concept of independent point sets and gives a simple sufficient and necessary condition.The paper presents a diagnosis procedure for t-diagnosable systems.furthermore,an efficient probabilistic diagnosis algorithm for practical applications is proposed based on the belief that most of the nodes in a system are fault-free.The result of software simulation shows that the probabilistic diagnoisis provides high probability of correct diagnosis and low diagnosis cost,is suitable for systems of any kind of topology.     

一种大规模模拟电路快速故障诊断新方法

针对传统大规模模拟电路故障诊断方法在多故障条件下的故障定位过程复杂、测前工作量大等问题, 提出了一种新的故障诊断方法——成组撕裂法。将大规模模拟电路按照拓扑特性和成组撕裂准则进行撕裂, 得到低维度的故障特征向量; 基于模式识别思想, 选用具有高度并行分类能力的神经网络作为分类器, 隐含层传递激发函数选择具有快速收敛特性的小波函数。经仿真验证该方法能实现故障特征向量的快速分类并得出故障诊断结果。与目前已有的互校验（multiple-test-condition, MTC）和交叉撕裂搜索法相比, 该方法有测前工作量小、诊断次数和计算量少、对多故障检测能力和工程实践性强等特点。     

用于可重构硬件容错过程的辅助布线电路设计

目前可重构硬件的容错机制大多采用重新布局布线的方法,但是需要很长的重布线时间,难以满足工程应用的需要.为此,提出一种支持可重构单元阵列快速容错的辅助布线电路,该电路结构由二维的辅助布线模块构成,每个辅助布线模块可以读取并修改所在可重构单元的可编程开关配置数据.可重构单元阵列容错时,辅助布线电路代替外部软件执行故障线网的取消和线网重布线过程.以4位并行乘法器为例,证明了在系统容错时辅助布线电路可以有效地加速故障线网取消和重布线过程.