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1.
A new fault model, called the X-fault model, is proposed for fault diagnosis of physical defects with unknown behaviors by using X symbols. An efficient X-fault simulation method and an efficient X-fault diagnostic reasoning method are presented. Fault diagnosis based on the X-fault model can improve the accuracy of failure analysis for a wide range of physical defects in complex and deep submicron integrated circuits.  相似文献   

2.
Quantized fault detection for sensor/actuator faults of networked control systems (NCSs) with time delays both in the sensor-to-controller channel and controller-to-actuator channel is concerned in this paper. A fault model is set up based on the possible cases of sensor/actuator faults. Then, the model predictive control is used to compensate the time delay. When the sensors and actuators are healthy, an H ∞ stability criterion of the state predictive observer is obtained in terms of linear matrix inequality. A new threshold computational method that conforms to the actual situation is proposed. Then, the thresholds of the false alarm rate (FAR) and miss detection rate (MDR) are presented by using our proposed method, which are also compared with the ones given in the existing literatures. Finally, some numerical simulations are shown to demonstrate the effectiveness of the proposed method.  相似文献   

3.
The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Conver- gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demon- strate the effectiveness of the proposed algorithm.  相似文献   

4.
In this paper, the conception and the development of the fault diagnosis technology are discussed, and the problems of fault diagnosis technology is solved in power plants by analyzing the actual and existing problems in the field of power plants fault diagnosis technology. Then we reveal the reliable technique to diagnose software by using BPNN in power plants fault diagnosis process. The experiment shows that complex model can be constructed by using this method and parameter estimation is done easily. This method is also fit for different datum sets, and it has less error. It is an efficient method in power plants fault diagnosis.  相似文献   

5.
The problem of fault estimation for a class of non-uniformly sampled-data systems is investigated from the time delay point of view in this paper.Firstly,the output delay approach is employed to model the sampled-data system as a continuous-time one with time-varying delay output.Then,based on the analysis of the inapplicability of the adaptive fault diagnosis observer in such class of time-delay systems,a novel augmented fault estimation observer design method is proposed to guarantee the exponential convergence of the estimation errors.Furthermore,an extension to the case of time varying fault estimation for the noisy sampled-data systems is studied.Finally,simulation results of a flight control system are presented to demonstrate the effectiveness of the proposed method.  相似文献   

6.
Node Grouping in System-Level Fault Diagnosis   总被引:7,自引:0,他引:7       下载免费PDF全文
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.  相似文献   

7.
This paper presents a novel approach to detect and diagnose faults in the dynamic part of a class of stochastic systems . the Such a group of systems are subjected to a set of crisp inputs but the outputs considered are the measurable probability density functions (PDFs) of the system output, rather than the system output alone. A new approximation model is developed for the output probability density functions so that the dynamic part of the system is decoupled from the output probability density functions. A nonlinear adaptive observer is constructed to detect and diagnose the fault in the dynamic part of the system. Conver-gency analysis is performed for the error dynamics raised from the fault detection and diagnosis phase and an applicability study on the detection and diagnosis of the unexpected changes in the 2D grammage distributions in a paper forming process is included.  相似文献   

8.
In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.  相似文献   

9.
Design of a bilinear fault detection observer for singular bilinear systems   总被引:2,自引:0,他引:2  
A bilinear fault detection observer is proposed for a class of continuous time singular bilinear systems subject to unknown input disturbance and fault. By singular value decomposition on the original system, a bilinear fault detection observer is proposed for the decomposed system via an algebraic Riccati equation, and the domain of attraction of the state estimation error is estimated. A design procedure is presented to determine the fault detection threshold. A model of flexible joint robot is used to demonstrate the effectiveness of the proposed method.  相似文献   

10.
Improved method to generate path-wise test data   总被引:4,自引:0,他引:4       下载免费PDF全文
Gupta et al.,propsed a method ,which is referred to as the Iterative Relaxation Method ,to generate test data for a given path in a program by linearizing the predicate functions.In this paper,a model language is presented and the properties of static and dynamic data depen-dencies are investigated ,The notions in the Interative Relaxation Method are defined formally.The predicate slice proposed by Gupta et al.is extended to path-wise static slice.The correctness of the constructional algorithm is proved afterward The improvement shows that the constructions of predicate slice and input dependency set can be omitted .The equivalence of systems of constraints generated by both methods is proved ,The prototype of path-wise test data generator is presented in this paper,The experiments show shat our method is practical ,and fits the path-wise automatic generation of test data for both whicte -bos testing and black-blx testing.  相似文献   

11.
在多处理机系统的系统级故障诊断中,一个重要的研究课题是确定最可能故障处理机集,该问题可以归结为NP一完全的整数线性规划问题。连续Hopfietd神经网络能够近似求解最优化问题,因此是解决这类问题的可选路径。文中主要研究如何构建连续Hopfield神经网络,以在三值PMC模型下近似地确定最可能故障集,相比于常用的二值诊断模型,能得到更准确的诊断结果。在超立方体结构上进行了一系列的数值实验,仿真结果表明:该方法具有实用性。  相似文献   

12.
Chwa & Hakimi故障模型方程诊断的理论基础   总被引:5,自引:0,他引:5  
针对基于对称比较的系统级故障模型———Chwa&Hakimi模型 ,建立起“方程诊断”的有关概念 ,把该模型等价地转换为一个方程 (或方程组 )。对于一类特殊的Chwa&Hakimi模型找到了求全体相容故障模式的具体算法 ,该算法为寻求一般情形下Chwa&Hakimi模型的全体相容故障模式奠定了理论基础和算法基础。  相似文献   

13.
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.  相似文献   

14.
Malek故障模型的方程诊断算法设计   总被引:1,自引:0,他引:1  
首先给出Malek模型的方程组定义形式,然后在不以“t-可诊断性”和“相信大多数”作前提假设的情况下,通过引入“集团”的概念给出了求其全体相容故障模式的具体方法—方程诊断算法,丰富了Malek模型的故障诊断方式。  相似文献   

15.
Chwa & Hakimi故障模型的方程表示   总被引:4,自引:1,他引:3  
宣恒农 《计算机工程》2001,27(10):39-40,44
把基于对称比较的系统级故障模型-Chwa&Hakimi模型等价地转换为一个方程(或方程组),这将给此类故障模型的诊断带来极大便利  相似文献   

16.
人工免疫系统(AIS)已被广泛的应用在许多领域,如数据分析、多峰函数优化、故障检测等。文章将人工免疫方法引入到PMC模型下网络故障诊断中,文中主要研究如何将AIS应用于系统级故障诊断。理论分析和实验结果表明,基于人工免疫系统的网络故障诊断方法在平均和最差情况下均优于传统的方法。  相似文献   

17.
采用方程模式来描述Malek故障模型,把求该模型全体相容故障模式的问题转换为求方程组的解集,并通过实例讲解如何求解方程组的解集和以此得到Malek模型的诊断结果,为系统研究这类模型的方程诊断算法提供了典型示范和奠定了理论基础。  相似文献   

18.
罗立宇  张大方 《计算机工程》2003,29(9):35-36,74
网格是一种高性能分布式计算环境,它是构筑在Inernet之上的一种新型的信息技术基础设施。为保证网格可靠高效地运行,利用系统级故障诊断的方法研究网格的可靠性是很有意义的。文章提出了网格结构中的系统级故障诊断方法,并给出了一种面向网格结构的系统级故障诊断集团算法。  相似文献   

19.
提出了基于Chwa&Hakimi故障模型的集团诊断,定义了绝对故障集和最终诊断图,由此能找到所有的基于Chwa&Hakimi故障模型的相容故障模式。从仿真结果看大大减少了系统级故障诊断的复杂度,特别是对强t-可诊断系统,具有较大的实际意义。  相似文献   

20.
提出了基于Chwa&Hakimi故障模型的系统级故障诊断的方程解决方案,根据实际系统中存在大可靠块的实事,在方程解决中定义了集团的概念,给出了集团的许多性质,由此能找到所有基于Chwa&Hakimi故障模型的相容故障模式。  相似文献   

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