离散事件系统基于模式的安全故障诊断

基于模式的故障诊断方法能将触发系统故障的事件串诊断出来,但在诊断期间系统仍然可能执行被禁止的不安全操作.为此,提出了一种离散事件系统基于S型和T型模式的安全诊断方法.先对离散事件系统基于模式的安全可诊断性进行形式化,再通过构造非法语言识别器和安全诊断器对系统发生的故障模式实施安全诊断,最后分别得到了一个关于S型和T型模式的系统安全可诊断性的充分必要条件,实现了离散事件系统基于模式的安全故障诊断.     

模糊离散事件系统的可测性

针对模糊离散事件系统的可测性问题,提出一种基于不可区分串的可测性判定方法。首先引入不可区分串概念以刻画对任一极小可观测事件具有相同的可观察行为;然后构造一个基于极小可观测事件的观察器,建立不可区分串与观察器中非单元循环的一一对应关系,并由此得到模糊可测性的充要条件。同时,也给出一个验证模糊离散事件系统可测性算法。通过实例分析,说明模糊离散事件系统可测性的应用。     

离散事件系统最小故障诊断基的求解与应用

针对离散事件系统(DESs)的最小故障故障诊断基(MDB)求解问题,提出了一种基于事件集树的求解算法.首先在诊断器的基础上构建了一种新型测试自动机,并求得离散事件系统可诊断性的充分必要条件,实现了对DESs的故障诊断.在此基础上,提出了通过构造事件集树求解离散事件系统MDB的算法,并对算法的复杂度进行了分析.最后,通过网络系统中关键网路的选取实例对算法进行了分析.与现有算法相比,所提算法不仅复杂度更低,而且可用于在线诊断和离线诊断,适用性更广.     

离散事件系统间歇性故障的安全诊断

离散事件系统的间歇性故障诊断能够将系统中发生的间歇性故障及时诊断出来,但在诊断期间的系统可能会执行不安全操作.针对间歇性故障在诊断期间的安全性问题,提出一种基于事件的安全诊断方法.首先对发生间歇性故障的离散事件系统进行建模,并给出系统间歇性故障的安全可诊断性的形式化定义.然后通过构造非法语言识别器对系统的非法操作进行识别,并在此基础上构建一个安全验证器,由此得到一个关于系统间歇性故障安全可诊断性的充分必要条件,实现离散事件系统对间歇性故障的安全诊断.这种安全诊断既保证了间歇性故障一旦发生即能被及时诊断出来,又确保了在故障诊断期间系统不会执行任何不安全操作.     

模糊离散事件系统监督控制理论

为了将经典离散事件系统 (Discrete event systems, DES) 中的监控理论扩展到模糊离散事件系统 (Fuzzy discrete event systems, FDES) 中, 总结了目前 FDES 建模和监控理论方面的成果, 进一步讨论了 FDES 的能控性, 给出了非阻塞监控的条件. 对 FDES 的能控子语言与优化监控进行了分析, 得到了能控子语言的性质. 以模糊自动机作为监控的一种形式, 给出了监督控制具体的实现方法, 这对于进一步开展 FDES 的研究工作是有益的.     

分布式模糊离散事件系统的故障预测

本文研究分布式模糊离散事件系统的故障预测问题.先根据系统的模糊特性,提出一种分布式模糊离散事件系统的协同可预测性的形式化方法,使分布式模糊离散事件系统的协同可预测度不小于各分站点的局部可预测度.通过构造协同预测验证器,提出一种基于协同预测验证器的协同预测算法,并得到一个关于分布式模糊离散事件系统协同可预测性的充分必要条件.     

基于动态观测的随机离散事件系统故障诊断

近年来,离散事件系统故障诊断研究引起国内外学者广泛关注.鉴于此,研究动态观测下随机离散事件系统的故障诊断.首先引入一种动态观测,使事件的可观测性随着系统的运行而动态变化;然后分别对基于动态观测的随机离散事件系统的单故障可诊断性和模式故障可诊断性进行形式化;最后通过构造相应的诊断器,分别得到关于单故障可诊断性和模式故障可...     

基于规则的模糊离散事件系统建模与控制研究

用模糊离散事件系统（FDES）从离散事件的角度描述一类包含“具有确定性的不确定／模糊”问题以及与人的主观观察／判断密切相关的复杂系统。提出了基于规则的FDES描述方法及自寻优监督控制策略,为FDES的实际应用提供了一种方法,提高了FDES在具体应用中的可操作性,并介绍了其在纺织染整控制过程中的具体应用．     

离散事件系统的学习控制初探

本文建立了包含状态持续时间的离散事件系统的自动机模型，然后引入学习算法用以确定描述闭环系统进行了语言K,从而当控制指标是以时间形式给出时，仍能利用监控理论来设计散事件系统。     

Active diagnosis of discrete-event systems

The need for accurate and timely diagnosis of system failures and the advantages of automated diagnostic systems are well appreciated. However, diagnosability considerations are often not explicitly taken into account in the system design. In particular, design of the controller and that of the diagnostic subsystem are decoupled, and this may significantly affect the diagnosability properties of a system. The authors present an integrated approach to control and diagnosis. More specifically, they present an approach for the design of diagnosable systems by appropriate design of the system controller. This problem, which they refer to as the active diagnosis problem, is studied in the framework of discrete-event systems (DESs); it is based on prior and new results on the theory of diagnosis for DESs and on existing results in supervisory control under partial observations. They formulate the active diagnosis problem as a supervisory control problem where the legal language is an “appropriate” regular sublanguage of the regular language generated by the system. They present an iterative procedure for determining the supremal controllable, observable, and diagnosable sublanguage of the legal language and for obtaining the supervisor that synthesizes this language. This procedure provides both a controller that ensures diagnosability of the closed-loop system and a diagnoser for online failure diagnosis. The procedure can be implemented using finite-state machines and is guaranteed to converge in a finite number of iterations. The authors illustrate their approach using a simple pump-valve system     

Observability and decentralized control of fuzzy discrete-event systems

Fuzzy discrete-event systems as a generalization of (crisp) discrete-event systems have been introduced in order that it is possible to effectively represent uncertainty, imprecision, and vagueness arising from the dynamic of systems. A fuzzy discrete-event system has been modeled by a fuzzy automaton; its behavior is described in terms of the fuzzy language generated by the automaton. In this paper, we are concerned with the supervisory control problem for fuzzy discrete-event systems with partial observation. Observability, normality, and co-observability of crisp languages are extended to fuzzy languages. It is shown that the observability, together with controllability, of the desired fuzzy language is a necessary and sufficient condition for the existence of a partially observable fuzzy supervisor. When a decentralized solution is desired, it is proved that there exist local fuzzy supervisors if and only if the fuzzy language to be synthesized is controllable and co-observable. Moreover, the infimal controllable and observable fuzzy superlanguage, and the supremal controllable and normal fuzzy sublanguage are also discussed. Simple examples are provided to illustrate the theoretical development.     

From classic observability to a simple fuzzy observability for fuzzy discrete-event systems

In order to determine uncertainties from restricted available information, fuzzy discrete-event systems (FDESs), or fuzzy discrete-event dynamic systems (FDEDSs), were recently proposed. These frameworks include fuzzy states and events occurring simultaneously with different membership degrees. Fuzzy states and events have been used to describe uncertainties that occur often in practical problems, such as treatment planning for HIV/AIDS patients, sensory information processing for robotic control, and fault diagnosis problems. In order to measure information associated with FDESs or FDEDSs, the classical discrete event system (DES) observability has been turned into fuzzy observability for FDESs or FDEDSs. The newly proposed method allows ease of defining degrees of observability so that uncertainties in FDESs or FDEDSs can be dealt with effectively. This gives an opportunity to design better decision-making systems. To calculate the observability degree, a simple fuzzy observability checking method is introduced, and two examples are elaborated upon to illustrate the presented method. Finally, the newly proposed method is tested on a heating, ventilating, and air-conditioning (HVAC) system.     

The relationship of controllability between classical and fuzzy discrete-event systems

We have known that the controllability of classical discrete-event systems has already been extended into fuzzy discrete-event systems. In this paper, firstly, we recall some related definitions and results of the controllability for classical and fuzzy discrete-event systems, respectively. Secondly, we are concerned with the relationship of the controllability between classical and fuzzy discrete-event systems. In particular, we show that there is an equivalence of the controllability between the two systems to some extent, some special proofs are also presented. Finally, we use this equivalence to extend some general controllability properties of classical discrete-event systems into fuzzy ones.     

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.     

离散事件系统拟同余关系的改进算法

针对离散事件系统, 本文主要研究计算最优拟同余关系时减少时间复杂度的算法. 基于Paige & Tarjan提出且Fernandez修改的、可有效计算最粗粒度划分问题的算法, 本文给出一种时间复杂度为O(mlog n)的计算最优拟同余关系的算法. 该算法适用于离散事件系统比较复杂, 尤其是可观事件很少的情况. 与Ramadge和Wonham提出的时间复杂度为O(mn)的算法相比, 该算法计算过程耗时较短. 本文还讨论了计算拟同余关系的边界情况的改进方法. 仿真结果表明所提出算法的有效性.     

A diagnosis system based on fuzzy classification

Diagnosis is seen from a cybernetical point of view because it is supposed that the system under investigation is a large scale one. Experimental results form the information basis. They are measuring results or linguistic expressions given by an expert about the history of the system (new state, working conditions, observations) and actual informations from tests, investigations and so on. The theoretical basis is classification as a fundamental ordering operation. Classes may be defined as semantic ones by an expert (teacher) or by means of cluster procedures as natural classes. The additional uncertainty of elementary and global informations is taken into consideration. One theoretical basis is, therefore, that of fuzzy sets. Data structure is described by an effective concept of a uniform parametric membership function. The structure and strategy of the computer assisted diagnosis system are shown. Some information about software instruments for digital- and microcomputers is given. An illustrative practical example demonstrates the use of the system.     

A fuzzy connectionist expert system for visual pattern classification

In this paper, a connectionist model to integrate knowledge-based techniques into neural network approaches for visual pattern classification is presented. We propose a new structure of connectionist model which has rule-following capability as well as instance-based learning capability. Each node of the proposed network is doubly linked by two types of connections: positive connection and negative connection. Such connectionism provides a methodology to construct the classifier from the rule base and allows the expert knowledge to be utilized for the effective learning. For visual pattern classification, we present the techniques for knowledge representation and utilization using the concepts of fuzzy rules and fuzzy relations. We also discuss in this paper some advantageous characteristics of the model: result explanation capability and rule refinement capability. From the experimental results of the handwritten digit classification, the feasibility of the proposed model is evaluated.