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.     

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。     

On constructing the minimum orthogonal convex polygon for the fault-tolerant routing in 2-D faulty meshes

The rectangular faulty block model is the most commonly used fault model for designing fault-tolerant, and deadlock-free routing algorithms in mesh-connected multicomputers. The convexity of a rectangle facilitates simple, efficient ways to route messages around fault regions using relatively few or no virtual channels to avoid deadlock. However, such a faulty block may include many nonfaulty nodes which are disabled, i.e., they are not involved in the routing process. Therefore, it is important to define a fault region that is convex, and at the same time, to include a minimum number of nonfaulty nodes. In this paper, we propose an optimal solution that can quickly construct a set of minimum faulty polygons, called orthogonal convex polygons, from a given set of faulty blocks in a 2-D mesh (or 2-D torus). The formation of orthogonal convex polygons is implemented using either a centralized, or distributed solution. Both solutions are based on the formation of faulty components, each of which consists of adjacent faulty nodes only, followed by the addition of a minimum number of nonfaulty nodes to make each component a convex polygon. Extensive simulation has been done to determine the number of nonfaulty nodes included in the polygon, and the result obtained is compared with the best existing known result. Results show that the proposed approach can not only find a set of minimum faulty polygons, but also does so quickly in terms of the number of rounds in the distributed solution.     

Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network

Wireless sensor networks are susceptible to failures of nodes and links due to various physical or computational reasons. Some physical reasons include a very high temperature, a heavy load over a node, and heavy rain. Computational reasons could be a third-party intrusive attack, communication conflicts, or congestion. Automated fault diagnosis has been a well-studied problem in the research community. In this paper, we present an automated fault diagnosis model that can diagnose multiple types of faults in the category of hard faults and soft faults. Our proposed model implements a feed-forward neural network trained with a hybrid metaheuristic algorithm that combines the principles of exploration and exploitation of the search space. The proposed methodology consists of different phases, such as a clustering phase, a fault detection and classification phase, and a decision and diagnosis phase. The implemented methodology can diagnose composite faults, such as hard permanent, soft permanent, intermittent, and transient faults for sensor nodes as well as for links. The proposed implementation can also classify different types of faulty behavior for both sensor nodes and links in the network. We present the obtained theoretical results and computational complexity of the implemented model for this particular study on automated fault diagnosis. The performance of the model is evaluated using simulations and experiments conducted using indoor and outdoor testbeds.     

一个基于并行集团的系统级故障概率诊断算法

概率诊断算法是系统级故障诊断研究的一个重要方面,本文提出了一种基于并行集团的概率诊断算法-PGSFPD算法,并设计了一个系统级故障诊断软件仿真系统,对诊断算法进行仿真,分析比较各算法的性能,仿真结果表明PGSFPD算法性能优于经典的概率诊断算法-Somani & Agrawal算法,可在只需较少测试数的情况下,在保持很高诊断正确率的同时,大大降低系统的规模.     

A new swarm-SVM-based fault diagnosis approach for switched current circuit by using kurtosis and entropy as a preprocessor

This paper presents a new fault diagnosis method for switched current (SI) circuits. The kurtoses and entropies of the signals are calculated by extracting the original signals from the output terminals of the circuit. Support vector machine (SVM) is introduced for fault diagnosis using the entropies and kurtoses as inputs. In this technique, a particle swarm optimization is proposed to optimize the SVM to diagnose switched current circuits. The proposed method can identify faulty components in switched current circuit. A low-pass SI filter circuit has been used as test beached to verify the effectiveness of the proposed method. The accuracy of fault recognition achieved is about 97 % although there are some overlapping data when tolerance is considered. A comparison of our work with Long et al. (Analog Integr Circuit Signal Process 66:93–102, 2011), which only used entropy as a preprocessor, reveals that our method performs well in the part of fault diagnostic accuracy.     

基于虚拟可及测试节点的模拟电路故障诊断

针对网络撕裂方法诊断模拟电路故障过程中撕裂节点必须是可及节点的限制,提出了虚拟可及测试节点的方法.利用网络拓扑结构和基尔霍夫电流定律计算一类不可及测试节点故障电压,让其成为虚拟可及测试节点.然后在可及或虚拟可及测试节点对网络进行撕裂,再根据故障电压和故障判据定位故障至更小的区域,从而进一步定位故障元件.这种新方法降低了待诊断电路中对可及节点数目的要求,增加了撕裂的灵活性.通过仿真实例验证了该方法的有效性.     

Fault dictionary based switched current circuit fault diagnosis using entropy as a preprocessor

A novel method based on a fault dictionary that uses entropy as a preprocessor to diagnose faulty behavior in switched current (SI) circuit is presented in the paper. The proposed method uses a data acquisition board to extract the original signal form the output terminals of the circuit-under-tests. These original data are fed to the preprocessors for feature extraction and finds out the entropies of the signals which are a quantitative measure of the information contained in the signals. The proposed method has the capability to detect and identify faulty transistors in SI circuit by analyzing its output signals with high accuracy. Using entropy of signals to preprocess the circuit response drastically reduces the size of fault dictionary, minimizing fault detect time and simplifying fault dictionary architecture. The result from our examples showed that entropies of the signals fall on different range when the faulty transistors^` Transconductance G_m value varying within their tolerances of 5 or 10%, thus we can identify the faulty transistors correctly when the response do not overlap. The average accuracy of fault recognition achieved is more than 95% although there are some overlapping data when tolerance is considered. The method can classify not only parametric faults but also catastrophic faults. It is applicable to analog circuits as well as SI ones. A low-pass and a band-pass SI filter and a Clock feedthrough cancellation circuit have been used as test beached to verify the effectiveness of the proposed method. A comparison of our work with Yuan et al. (IEEE Trans Instrum Meas 59(3):586–595, 2010), which used entropy and kurtosis as preprocessors, reveals that our method requiring one feature parameter reduces the computation and fault diagnosis time.     

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.     

树型电力网单故障分支识别的零泛器替代法

本文提出一种树型电力网单一故障分支的识别方法零泛器替代法。这种方法的特点是诊断所需要的端口变量易于测量,对于短路和断线故障都适用,测后计算量较少。适用于多分支的树型电力网单一故障分支的识别。经计算机模拟,证明了所提识别方法是有效的。     

线性模拟电路的双故障诊断及人工神经网络的实现

本文提出了一种进行线性模拟电路双故障定位的原理及以此为基础的人工神经网络实现方法。文中论述了双故障元件参数在零到无穷大间变化时，测量值在增量空间所具有的一般特性。当可测端对各元件的转移阻抗或各元件单故障空间特性已知时．则根据两次独立测试结果，即可对双重故障定位。这一方法减小了传统K故障法所需的在线计算量，算例表明此法有好的诊断效果。     

Method for Analog Circuit Soft-Fault Diagnosis and Parameter Identification Based on Indictor of Phase Deviation and Spectral Radius

The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits. A new fault location and parameter prediction approach for soft-faults diagnosis in analog circuits is presented in this paper. The proposed method extracts the original signals from the output terminals of the circuits under test (CUT) by a data acquisition board. Firstly, the phase deviation value between fault-free and faulty conditions is obtained by fitting the sampling sequence with a sine curve. Secondly, the sampling sequence is organized into a square matrix and the spectral radius of this matrix is obtained. Thirdly, the smallest error of the spectral radius and the corresponding component value are obtained through comparing the spectral radius and phase deviation value with the trend curves of them, respectively, which are calculated from the simulation data. Finally, the fault location is completed by using the smallest error, and the corresponding component value is the parameter identification result. Both simulated and experimental results show the effectiveness of the proposed approach. It is particularly suitable for the fault location and parameter identification for analog integrated circuits.     

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.     

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R² of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.     

Induction machine fault diagnostic analysis with wavelet technique

A wavelet transform based method was developed for diagnosing machine faults operating at different rotating speeds. This paper shows that machine fault diagnosis can be effectively performed when an appropriate narrow-band filter is used to extract the required spectra components. A wavelets-transform-based technique is used to design specified narrow filter banks. This enables effective machine fault diagnostic analysis to be performed in the frequency domain. Gaussian-enveloped oscillation-type wavelet is employed. By matching the wavelet basis functions with the associated faulty signals, the required narrow filter banks are obtained. As a result, the detection and diagnosis of machine faults operating at different rotating speeds are made possible. The proposed technique was thoroughly tested at different rotating speeds.     

统计模式识别在相控阵天线故障诊断中的应用研究

相控阵天线已广泛应用于雷达系统,而阵列单元的快速诊断日益成为难题。针对相控阵天线阵元故障难以检测的问题,提出了一种基于统计模式识别的方法。首先阐述了相控阵诊断原理,用矩量法构建了仿真环境,并提取了时域特征和小波特征。为增大类间平均距离,建立了故障树诊断模型以减小判别问题的规模,在时域特征空间中用投影聚类算法划分了子空间,在叶节点处用小波特征进行判别,实现了故障阵元的定位。仿真实验表明,该方法在信噪比较低时,比非层次方法优势明显,信噪比大于8时,识别率达到95%以上,且随着规模的增加,识别率并未明显下降,证明该方法理论上能够有效诊断相控阵阵元故障。实际应用中,只须对阵列的行或列逐次诊断即可获知整个阵面的故障信息。      

Current-mode synthesis using node expansion techniques

This paper derives node expansion methods by which a given passive network and its nodal admittance matrix are modified by expanding a node into two nodes and introducing a nullor or a dependent source between the newly created nodes. Node expansion provides a systematic method to introduce active elements in a network. The elements of the admitance matrix are modified, but the dimensions of the matrix are unchanged. These methods, which can be applied repetitively, are used to derive filters and oscillators from parental passive networks in a systematic manner.     

Fast algorithms for selection of test nodes of an analog circuit using a generalized fault dictionary approach

Selection of test nodes is an important phase of the fault dictionary approach. It is demonstrated in this paper that the techniques used for this purpose in other approaches of analog fault diagnosis like fault analysis and fault verification are not in general suitable for the fault dictionary approach. The ambiguity set is a simple and effective concept for choosing test nodes in the context of dictionaries. These sets are formed such that each faulty condition lies in only one ambiguity set. Deviating from this thinking, overlapping ambiguity sets are proposed in this paper, giving rise to a generalized fault dictionary. These sets use information more fully and hence reduce the number of test nodes. The concept of hashing is applied in this paper for selecting test nodes. This gives a linear time algorithm (linear in the number of fault voltage specificationsf) and it isf times faster than the existing methods. It is not possible to select test nodes faster than this. This technique can also be used to select test nodes by the process of elimination of nodes. This is also linear inf per node elimination. Even a group of nodes can be eliminated or selected within the same computation. This freedom is not possible with the existing methods.     

非线性容差模拟电阻电路故障诊断神经网络方法

将线性电路故障定位 l1 范数最优化算法推广到非线性电路的故障定位 ,由于测后计算是基于神经网络计算机环境 ,所需时间较少 ,能满足现代工业实时性需要。实例和计算机模拟结果表明所提方法是可行的     

Engine Fault Analysis: Part II?Parameter Estimation Approach

The general fault analysis problem can be divided into two parts: fault detection and diagnosis (location). Fourier series, autocorrelation, and other techniques have been used for fault detection. However, these approaches cannot be utilized for locating the faults. In this paper a methodology is presented to locate faulty cylinder(s). The procedure involves the development of a mathematical model of the engine dynamics. This model takes into consideration the cylinder gas pressure, engine inertia, and load. The resultant torque is computed by using parameter estimation techniques. The parameter estimation technique employed can determine time-varying parameters without prior knowledge of the structure of the parameter. In the problem at hand, this is an important requirement. The resultant torque is the net of the cylinder gas torque and the frictional torque. The model and the estimation procedure have been verified by performing tests on a single-cylinder engine. A discriminant function has been defined to classify the performance of each cylinder. Our results indicate that the amplitude of the resultant torque can be used to identify the faulty cylinder(s). We have verified this approach by tests and studies on a six-cylinder engine. In our experiments we have studied cases involving one or two faulty cylinders.