Fault diagnosis in hypercube multiprocessor systems

Hypercubes are viewed as good candidates for parallel processing, because a number of topologies, such as rings, trees, and meshes, can be mapped onto the hypercubes. In this paper, we study a system level diagnosis method for clustered faults in hypercube systems. We investigate the local and global performance of the method under the Bernoulli failur distribution. We demonstrate that the diagnosis scheme can identify almost all processors successfully even if the percentage of fault-free processors is low (much lower than 50%) while almost all processors are guaranteed to be correctly identified.     

Fault diagnosis of networked control systems

Networked control systems (NCS) are feedback systems closed through data networks. NCS have many advantages compared with traditional systems; however, the network-induced delay and other characteristics of data networks may degrade the performance of feedback systems designed without taking the network into account. Supported by the National Nature Science Foundation of China, we studied the fault diagnosis and fault-tolerant control theory for NCS in recent years. This paper summarizes our main ideas and results on fault diagnosis of NCS, including the fundamentals of fault diagnosis for NCS with information-scheduling, fault diagnosis approaches based on the simplified time-delay system models, and the quasi T-S fuzzy model and fault diagnosis for linear and nonlinear NCS with long delay.     

Fault diagnosis in discrete-event systems: incorporating timing information

A framework is introduced for fault diagnosis in timed discrete-event systems. In this approach, the required estimates for system condition are updated only when the output changes or when deadlines associated with output changes expire. Thus updates at every clock tick are not required. This in many cases results in reduction in online computing requirements and in the size of the diagnosis system, at the expense of more offline design calculations. The issue of failure diagnosability is also discussed.     

Fault diagnosis in discrete-event systems: framework and model reduction

A state-based approach for online passive fault diagnosis in systems modeled as finite-state automata is presented. In this framework, the system and the diagnoser (the fault detection system) do not have to be initialized at the same time. Furthermore, no information about the state or even the condition (failure status) of the system before the initiation of diagnosis is required. The design of the fault detection system, in the worst case, has exponential complexity. A model reduction scheme with polynomial time complexity is introduced to reduce the computational complexity of the design. Diagnosability of failures is studied, and necessary and sufficient conditions for failure diagnosability are derived.     

Fault diagnosis in dynamical systems: a graph theoretic approach

Fault diagnosis is a vital aspect in the design of operational control systems for large-scale systems with stringent requirements on safety and reliability. In this paper, we develop graph representations for the failure propagation in large-scale systems. Using this model, we present efficient algorithms for failure source identification for single and multiple faults, for diagnosis of faulty alarms, and for forewarning and fault simulation. All these algorithms are analysed for their worst-case complexities. The treatment is algorithmic and graph theoretic and no reference is made to the underlying physical systems.     

Fault diagnosis in discrete time hybrid systems - A case study

A method of analysing diagnosability of discrete time hybrid systems (DTHS), which are similar to the simple n-rate timed automata [R. Alur, C. Courcoubetis, T.A. Henzinger, P. Ho, Hybrid automata: an algorithmic approach to the specification and verification of hybrid systems, in: Hybrid Systems, LNCS 736, Springer Verlag, 1993, pp. 209-229], has been proposed. A state based fault modeling formalism is used. The properties of the DTHS model, under measurement limitations due to inadequacy or non-availability of sensors, are discussed. A definition of diagnosability for DTHS models has been adopted from the one proposed in [M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, D. Teneketzis, Diagnosability of discrete-event systems, IEEE Transactions on Automatic Control 40 (9) (1995) 1555-1575] for discrete-event system (DES) models. Based on the measurement limited DTHS models, an algorithm for construction of a diagnoser is presented. It is next demonstrated through an example of a chemical reaction chamber that the diagnosability condition (over the diagnoser), which has been shown to be necessary and sufficient for DES diagnosability, fails to hold for many systems. This is so because the abstraction employed in DES modeling obliterates an important feature of the transitions namely fairness. Exploiting the explicit continuous dynamics of the DTHS models, the fairness of transitions is identified and used to demonstrate diagnosability. The diagnosability condition over the diagnoser is suitably modified to encompass the situations typified by the example.     

Fault diagnosis for high order systems based on model decomposition

Fault detection observer and fault estimation filter are the main tools for the model based fault diagnosis approach. The dimension of the observer gain normally depends on the system order and the system output dimension. The fault estimation filter traditionally has the same order as the monitored system. For high order systems, these methods have the potential problems such as parameter optimization and the real time implementation on-board for applications. In this paper, the system dynamical model is first decomposed into two subsystems. The first subsystem has a low order which is the same as the fault dimension. The other subsystem is not affected by the fault directly. With the new model structure, a fault detection approach is proposed where only the residual of the first subsystem is designed to be sensitive to the faults. The residual of the second subsystem is totally decoupled from the faults. Moreover, a lower order fault estimation filter (with the same dimension of the fault) design algorithm is investigated. In addition, the design of a static fault estimation matrix is presented for further improving the fault estimation precision. The effectiveness of the proposed method is demonstrated by a simulation example.     

Fault diagnosis of timed discrete event systems using Dioid Algebra

This paper deals with the fault diagnosis problem in a concurrent Timed Discrete Event System (TDES). In a TDES, concurrency leads to more complexity in the diagnoser and appears where, at a certain time, some user must choose among several resources. To cope with this problem, a new model-based diagnoser is proposed in this paper. This diagnoser uses Durational Graph (DG), a main subclass of timed automata for representing the time evolution of the TDES. The proposed diagnoser predicts all possible timed-event trajectories that may be generated by the DG. This prediction procedure is complicated for nondeterministic DG’s that are obtained for concurrent TDES’s. To solve this problem, a new Dioid Algebra, Union-Plus Algebra is introduced in this paper. Based on this Algebra, a reachability matrix is defined for a DG that plays an essential role in predicting the time behavior of TDES. By using reachability matrix, a prediction procedure is carried on via an effective equation set that is similar to linear system state equations in ordinary algebra. These results provide a suitable framework for designing an observer-based diagnoser that is illustrated by an example.     

Fault detection and diagnosis for delay-range-dependent stochastic systems using output PDFs

International Journal of Control, Automation and Systems - This paper investigates a new fault detection and diagnosis(FDD) scheme for delay-range-dependent stochastic systems. Compared with...     

基于故障跟踪估计器的非线性时滞系统故障诊断

提出一种可有效检测和估计一类非线性时滞系统故障的故障跟踪估计器.根据预测控制和迭代学习控制的思想,在所选取的优化时域长度内,通过迭代算法调节故障跟踪估计器中的可调参数,使之逼近系统中实际发生的故障.与以往基于观测器的故障诊断方法不同的是,故障跟踪估计器可同时检测和估计系统中发生的故障,而且针对不同类型的故障亦有很好的适应性.仿真结果表明了所提出算法的可行性和有效性.     

Fault diagnosis and fault-tolerant control for non-Gaussian nonlinear stochastic systems via entropy optimisation

ABSTRACT

In this paper, the fault diagnosis (FD) and fault tolerant control (FTC) problems are studied for non-linear stochastic systems with non-Gaussian disturbance and fault. Unlike classical FD algorithms, the minimum entropy FD is adopted to minimise the residual entropy and control the shape of the probability density function (PDF) of the residual signal. The observation error system can be proved to be locally and ultimately bounded in the mean square sense. Since entropy can be used to characteriSe the uncertainty of the tracking error for non-Gaussian stochastic systems, the FTC controller is obtained by minimising the performance function with regard to the entropy of the tracking error in this paper. The PDF of the output tracking error is approximated by the B-spline model. An illustrative example is utilised to demonstrate the effectiveness of the FD and FTC algorithm, and satisfactory results have been obtained.     

Fault diagnosis and optimal fault-tolerant control for systems with delayed measurements and states

This note deals with the problems of fault diagnosis and fault-tolerant control for systems with delayed measurements and states. The main contribution consists in two aspects. First, by solving the Riccati equation and Sylvester equation, an optimal fault-tolerant control law is designed for systems with delayed measurements and states. The existence and uniqueness of the optimal fault-tolerant control law are proved. Second, the physically unrealizable problem of the optimal fault-tolerant control law is solved by proposing a novel fault diagnoser for systems with delayed measurements and states. Finally, a numerical example is given to demonstrate the feasibility and validity of the proposed schemes.     

Fault diagnosis and fault tolerant control for non-Gaussian time-delayed singular stochastic distribution systems

Stochastic distribution control (SDC) is a new branch of stochastic system control that the system output is the probability density function (PDF) of the output. In practice, some algebraic relations exist between the input and the weights of SDC systems, leading to a singular state space model between the weights and the control input which increases the complexity of the system. The ignorance of time delay in practical systems will make the effectiveness of the fault diagnosis (FD) and fault tolerant control (FTC) be reduced. In this paper, the linear B-spline basis functions are used to approximate the output PDF. A FD approach based on the adaptive observer is established to diagnose the size of fault in the singular time-delayed SDC system. With the fault diagnosis information, a fault tolerant controller based on PI tracking control scheme is constructed to make the post-fault PDF still track the given distribution. The post-fault closed-loop stability analysis with the practical fault tolerant controller is carried out based on the Lyapunov stability theorem. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed approach.     

Fault detection in digital filter systems

The use of a parameter identification procedure to detect faults in hardware used to implement a broad class of linear algorithms defined as digital filters is presented. Using the filter coefficient estimates produced by the identifier, a method of measuring the acceptability of the filtering algorithm is suggested and a numerical example is given.     

Fault accommodation in nonlinear dynamic systems

The problem of fault accommodation in nonlinear dynamic systems is solved by constructing a control law that provides full decoupling with respect to fault effects. Existence conditions are found, and calculating relations are given for the control law.     

Fault containment in weakly stabilizing systems

Research on fine tuning stabilization properties has received attention for more than a decade. This paper presents probabilistic algorithms for fault containment. We demonstrate two exercises in fault containment in a weakly stabilizing system, which expedite recovery from single failures, and confine the effect of any single fault to the constant-distance neighborhood of the faulty process. The most significant aspect of the algorithms is that the fault gap, defined as the smallest interval after which the system is ready to handle the next single fault with the same efficiency, is independent of the network size. We argue that a small fault gap increases the availability of the fault-free system.     

Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with conditional Gaussian network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault.