容差电路k故障诊断理论和方法

本文在理论上揭示了偏差-故障电路可及点电压变化阵方向能量与故障信息阵、偏差干扰阵方向能量间的定量关系,分离出最大方向能量子空间,并提出了该子空间与故障信息子空间的最大接近度概念。技术上,本文给出了定位准确度与当今最精良的非线性最小二乘法相当,而定位速度快300～500倍的多故障定位新方法广义反投影法。     

THEORY ON THE DIAGNOSABILITY OF THE FAULT CLASSIFICATION APPROACH

This paper describes, by means of a Voronoi hypersphere, the nearest neighbor relations of all the feature submatrices in the fault classification space and analyses the deviation disturbance angles between fault feature submatrices and a k-dimension unitary matrix of the measured voltage change matrix. With the above two concepts, this paper discusses the diagnos-ability in the fault classification approach. The paper also classifies and defines the fault diagnosis problems. Finally, the paper derives the corresponding necessary and sufficient conditions for correct location of faults.     

A survey on fault diagnosis of analog circuits: Taxonomy and state of the art

Due to the wide range of applications of electronic circuits in the recent years, the fault diagnosis in electronic circuits is a foremost problem. The main purpose of the fault diagnosis technique is isolating the faults present in the electronic circuits and also, detecting the fault which affects the safety and performance of the system. For various real-time applications of fault diagnosis, literature presents several techniques for detecting the faults in electronic circuits. In this paper, reviews on the research based on the fault diagnosis techniques which are all gained much attention are comprehended. Accordingly, 114 research papers related to the fault diagnosis are reviewed and analyzed based on the various objectives. In this review, we present the taxonomy of the fault detection in analog circuits and discuss the state of the art algorithms with various advantages and major drawbacks. The comprehensive analysis is carried out on finding the coverage of the publishers, faults, circuits, methods, simulation tools, and metrics. This critical review finally discusses the research challenges that are still available in the existing techniques and the way to extend the current research is also examined.     

An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits

This paper presents a new approach for faults classification in analog integrated circuits using a multiclass adaptive neuro fuzzy inference system classifier. This is carried out to assist analog circuit's faults diagnosis suffering from inaccurate faults classification on one hand, and to lessen computational burden on the other hand. This has been achieved from features number reduction. These features serving as input feature vector are extracted from the selected circuits (CUT) frequency and transient responses under both fault free and faulty conditions. The considered faults are resistors and capacitors values variations of about 50% low and high from their nominal ones. The method accuracy has been validated with three experiment circuits, the Sallen Key band-pass, the four opamp biquad high-pass and the leapfrog filters. The obtained results reveal a high level of efficiency with an accuracy average reach to 99.76%. Hence, the proposed method has shown a good performance in term of fault classification accuracy when compared with those of both the Artificial Neural Networks (ANN) approach and the fractional Fourier transform (FRFT) method based on a statistical property.     

Self-tuning fault diagnosis of MEMS

Multiple-model adaptive estimation techniques have been previously successfully applied to fault diagnosis of microsystems. Their diagnosis performance highly depends on the accuracy of modeling techniques used in representing faults. This paper presents the application of a self-tuning forgetting factor technique in the modeling of faults in MEMS and its effects on diagnosis performance compared with the application of Kalman filters and fixed gain estimation techniques. The self-tuning-based modeling used in the diagnosis algorithm was experimentally implemented. It demonstrated superior results compared to Kalman filter and fixed gain estimation techniques by accelerating the diagnosis process.     

G参数诊断准则的理性分析

本文研究一种模拟电路故障诊断经典方法（即Ｇ参数诊断法）的正确性和合理性，研究表明：（１）估计Ｇ参数的数学模型是错误的；（２）由错误模型所得的Ｇ参数估计集和定是真实Ｇ参数集的有偏估计；（３）假如不满足严格的约束条件；Ｇ参数诊断法将给出错误的诊断结论，本文还给出了Ｇ准则能否正确诊断的预报公式，该公式很容易由电路标称数据计算。     

模拟电路的多频灵敏度故障诊断方法

文章在灵敏度故障诊断方法的基础上提出多频灵敏度参数识别故障诊断方法，并给出选择测试频率的一般原则。该方法能够适用于可及测试节点较少的电路。针对模拟电路中一般只存在部分元件故障的情况，进一步提出只识别部分故障元件参数的多频灵敏度故障诊断方法，使该方法能适用于更大规模的电路。电路仿真结果验证了所提方法的有效性。     

THE EFFECTIVE RANGE OF K-FAULT DIAGNOSIS OF-LINEAR CIRCUITS

In view of K-fault testability,the topological construction of a practical circuitis far from ideal.In order to improve the testability of a circuit,we may increase the numberof accessible nodes or use the multi-excitation method.Effectiveness of these methods and thefeasibility of choosing accessible nodes are discussed in detail.The conditions for multi-excitationtestability are presented.     

故障验证体系的可诊性理论

本文首先通过Voronoi超球面描述了故障验证方法体系中全部特征子阵的最近邻关系,再通过摄动理论分析了测量电压变化阵k维最大方向能量子空间的酉基阵对故障特征子阵的偏差干扰角。然后在此基础上讨论了故障验证方法体系可诊性问题,导出了该方法体系在诊断模拟电路故障时的可诊充要条件,并据可诊性质对诊断问题进行了分类界定。     

Canonical piecewise—Linear analysis for nonlinear DC fault circuits

This paper uses canonical piecewise—linear analysis method to analyze nonlinear DC fault circuits and solve for the values of the test port voltages which are selected beforehand. The method needs less memory storages, obtains the results in finite steps and has high efficiency in computation. It can be applied to the circuits containing multiport nonlinear elements. It is a good method of pre—test analysis for fault circuits in simulation—before—test aproach in analogue circuit diagnosis.     

基于信息融合的控制系统故障诊断

介绍了多传感器数据融合的基本概念，主要优点，结构以及它的方法和应用，对故障诊断进行了简要叙述，介绍了故障诊断信息融合过程；分析了多传感器数据融合和故障诊断的特点以及适合于控制系统故障诊断的信息融合的层次结构，并在此基础上提出了一种基于信息融合和故障诊断系统的结构。     

基于探针的端到端设备故障检测模式

随着宽带、VoIP、IPTV、大客户网络等应用的大范围普及，用户感知层面的服务保障要求越来越高。基于主动发现式网络检测，降低投诉量，帮助客户服务人员改善一次性派单成功率，集成整合现网诊断手段，智能排障，降低故障定位和解决时间。本文基于中国联通的接入网现状，以软件系统辅助探针部署的方式，探索一种有效的主动式端到端设备故障检测模式，丰富并完善运营商故障检测诊断手段。     

AN ALGORITHM FOR FAULT DIAGNOSIS BASED ON COMBINATORIAL DESIGN APPROACH FOR TESTING

Much research has been done mainly in testcase generation and its effect for combinatorial design approach for testing.This letter presents an algorithm for fault diagnosis based on the approach.It can conclude that the factors,which cause errors,must be in a very small range through analyzing the test cases after testing,and retesting with some complementary test cases.The algorithm can provide a very efficient and valuable guidance for the debugging and testing of software.     

数据挖掘技术在建模、优化和故障诊断中的应用

数据挖掘技术是当今智能系统理论的重要研究内容,它综合运用人工智能、计算智能（人工神经网、遗传算法）、模式识别、数理统计等先进技术从大量数据中挖掘和发现有价值和隐含的知识。文中介绍数据挖掘技术的原理以及在建模、优化和故障诊断中的应用和,包括：基于遗传算法的模糊规则生成,基于粗糙集的规则挖掘和基于混合模型的邦联建模。     

A survey on fault injection methods of digital integrated circuits

One of the most popular methods for reliability assessment of digital circuits is Fault Injection (FI) in which the behavior of the circuit is simulated in presence of faults. In this paper, we present a survey of FI techniques as well as classifying these techniques considering different aspects and criteria to bring out their similarities and differences. The goal of this paper is to help the researchers and reliable circuit designers in gaining insights into the state-of-art in FI techniques and motivate them to further improve these techniques for more efficient reliability evaluation of digital circuit designs of tomorrow.     

K故障诊断可测性的公树条件

模拟电路K故障诊断有两种方法：超定偏差方程相窖性判断法和双线性求值判断法。本文从拓朴上论述了使用这两种方法时电路的全局可测性和局部可测性。提出了简明的称为公树条件的评判方法。公树条件易于用来从可测性角度对两种诊断方法进行比较。在电路可测性设计时也方便使用。最后以一个例子说明双线性求值判断法的可测性要好得多。     

Test, diagnosis and fault simulation of embedded RAM modules in SRAM-based FPGAs

This paper presents a unique scheme for testing and locating multiple stuck at faults in the embedded RAM modules of SRAM-based FPGAs. The RAM modules are tested using the MATS++ algorithm. The interconnection scheme makes it possible to test all the cells within the RAM modules in the FPGA in just one test configuration. We also develop a diagnosis scheme capable of locating the faulty RAM cells and the CLB in which it is located. In this research, emphasis is also laid on reducing the testing time, which is achieved by partitioning the FPGA into two halves.     

神经网络在齿轮箱诊断中的应用

在认真学习了Elman网络的理论框架的基础上,总结分析了Elman神经网络用于故障诊断的不足,提出了改进的Elman神经网络,并将其应用于齿轮箱故障的故障诊断。建立了改进的Elman神经网络并进行仿真,经过一系列训练与测试,仿真结果表明：改进后的elman网络在稳定性及收敛速度等方面有明显优势,是可以在实际工程故障诊断中应用的一种非常有效的方法。