分层离散事件系统的不透明性

针对离散事件系统（DES）的不透明性问题，结合具有分层（hierarchical）结构的自动机模型，提出了分层离散事件系统的不透明性.对分层离散事件系统进行标准化，给出了分层离散事件系统不透明性和K延迟不透明性两个概念.为了对分层离散事件系统的两种不透明性进行验证，分别构造了两种相应的不透明性验证器，得到了关于不透明性和K延迟不透明性的充分必要条件，并对构造不透明性验证器的复杂度进行了分析.     

基于粗糙集理论的离散事件系统不透明性的验证算法

近年来,离散事件系统的不透明性研究引起了国内外众多学者的广泛关注.本文针对离散事件系统的不透明性,提出了一种将粗糙集理论作为知识提取工具来处理离散事件系统不透明性验证的方法.先对离散事件系统的不透明性进行形式化,再利用粗糙集理论对离散事件系统以信息表及决策表的形式进行表示,得到一个关于离散事件系统不透明性的充分必要条件.在此基础上,给出一个验证离散事件系统不透明性算法.与现有方法相比,该验证算法既适用于对强不透明性的验证,又适用于对弱不透明性的验证,并且在时间复杂度上也有较明显改进.     

离散事件系统不透明性监督控制研究综述

离散事件系统不透明性是指外部观察者无法分辨系统的一系列行为是否为系统所发生的.而离散事件不透明性监督控制则是构建监督器控制系统行为,使系统满足不透明性的一种方法.离散事件系统不透明性与信息安全有着紧密的联系,并得到了广泛的应用.首先对离散事件系统做了简要的概述,然后介绍了不透明性监督控制算法的研究现状,最后进行了总结和展望.     

一类分布式系统的模块化诊断方法

提出了一种针对一类大规模分布式系统的模块化诊断方法. 该方法基于离散事件动态系统的诊断理论, 其模块化特征不但使得诊断器的构造不用考虑整个系统的情况, 而且大大减少了系统构成的变化对已构建好的诊断器的影响.     

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.     

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Petri网诊断器的离散事件系统模式故障的在线诊断

本文研究基于Petri网诊断器的离散事件系统模式故障的在线诊断问题.先构建一种用于模式故障在线诊断的自动机,给出了基于这种自动机的在线诊断方法.然后将自动机转换为Petri网并进一步构造了可用于S型模式故障或T型模式故障在线诊断的Petri网诊断器,提出了基于Petri网诊断器的模式故障在线诊断算法.通过分析算法的复杂性,得到了该算法具有多项式空间复杂性的结论.     

不完备离散事件系统的可诊断性

在离散事件系统的建模过程中,由于系统行为的复杂,存在物理系统向逻辑系统映射的不完全性,因此产生了不完备模型的概念.提出在模型不完备的前提下,判断模型可诊断性的方法.提出可诊断性的在线判定方法,同时将不完备的行为加入模型,使模型完备.用经典的双树方法判断离线可诊断性,根据观测序列的时序及语言的前缀判断并处理不完备行为.提出判定不完备行为的方法,向模型中添加不完备行为,并根据不完备行为增量地在双树中判定在线可诊断性.通过在线的可诊断性判定,当前系统能够得到在有限观测内唯一判定故障发生与否的结论.该方法适用于具有离散性质的系统.     

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.     

离散事件系统的可测性

讨论基于自动机/形式语言模型的离散事件系统（DES）的可测性问题。可测性即为根据系统的可观事件和状态输出的信息估计系统的当前状态。定义了四种可测性：强可测性,弱可测性,强周期可测性,弱周期可测性。给出了这些可测性的充要条件,这些充要条件可通过构建观测器进行有效的判定。     

Probabilistic approaches to fault detection in networked discrete event systems

In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example.     

A bridged diagnostic method for the monitoring of polymorphic discrete-event systems.

Diagnosis of discrete-event systems (DESs) is a challenging problem that has been tackled both by automatic control and artificial intelligence communities. The relevant approaches share similarities, including modeling by automata, compositional modeling, and model-based reasoning. This paper aims to bridge two complementary approaches from these communities, namely, the diagnoser approach and the active system approach, respectively. The more significant shortcomings of such approaches are, on the one side, the need for the generation of the global system model and, on the other, the lack of monitoring capabilities. The former makes the application of the diagnoser approach prohibitive in real contexts, where the system model is too large to be generated, even offline. The latter requires the completion of the system observation before starting the diagnostic task, thereby, making the monitoring of the system. impossible. The bridged diagnostic method subsumes, to a large extent on the peculiarities of the two approaches and is capable of coping with an extended class of DESs that integrate both synchronous and asynchronous behavior. The bridge is built by extending the active system approach by means of several enhanced techniques, which eventually, allow the efficient monitoring of polymorphic DESs. Upon the occurrence of each system message, two pieces of diagnostic information are generated, namely, the snapshot and historic diagnostic sets. While the former accounts for the faults pertinent to the newly generated message only, the latter is based on the whole sequence of messages yielded by the system during operation.     

Failure diagnosis of discrete-event systems with linear-time temporal logic specifications

The paper studies failure diagnosis of discrete-event systems (DESs) with linear-time temporal logic (LTL) specifications. The LTL formulas are used for specifying failures in the system. The LTL-based specifications make the specification specifying process easier and more user-friendly than the formal language/automata-based specifications; and they can capture the failures representing the violation of both liveness and safety properties, whereas the prior formal language/automaton-based specifications can capture the failures representing the violation of only the safety properties (such as the occurrence of a faulty event or the arrival at a failed state). Prediagnosability and diagnosability of DESs in the temporal logic setting are defined. The problem of testing prediagnosability and diagnosability is reduced to the problem of model checking. An algorithm for the test of prediagnosability and diagnosability, and the synthesis of a diagnoser is obtained. The complexity of the algorithm is exponential in the length of each specification LTL formula, and polynomial in the number of system states and the number of specifications. The requirement of nonexistence of unobservable cycles in the system, which is needed for the diagnosis algorithms in prior methods to work, is relaxed. Finally, a simple example is given for illustration.     

Decentralized Diagnosis of Stochastic Discrete Event Systems

We investigate the decentralized diagnosis of stochastic discrete event systems (SDESs) by using multiple local stochastic diagnosers, each possessing its own sensors to deal with different information. We formalize the notions of decentralized diagnosis for SDESs by defining the concept of codiagnosability for stochastic automata, in which any communication among the local stochastic diagnosers or to any coordinators is not involved. These notions are weaker than the corresponding notions of decentralized diagnosis of classical DESs. A stochastic system being codiagnosable means that a fault can be detected by at least one local stochastic diagnoser within a finite delay. We construct a codiagnoser from a given stochastic system with a finite number of projections whose each diagnosis component uses the complete model of the system. We also deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition of the codiagnosability for SDESs is presented, which generalizes the corresponding results of centralized diagnosis for SDESs. Also, we give a computing method in detail to check the codiagnosability of SDESs. As an application of our results, some examples are described.     

A unified concurrent-composition method to state/event inference and concealment in labeled finite-state automata as discrete-event systems

Discrete-event systems (DESs) usually consist of discrete states and transitions between them caused by spontaneous occurrences of labeled events. In this review article, we study DESs modeled by labeled (nondeterministic) finite-state automata (LFSAs). Due to the partially-observed feature of DESs, fundamental properties therein can be classified into two categories: state/event-inference-based properties (e.g., strong detectability, diagnosability, and predictability) and state-concealment-based properties (e.g., opacity). Intuitively, the former category describe whether one can use observed output sequences to infer the current and subsequent states, past occurrences of faulty events, or future certain occurrences of faulty events; while the latter describe whether one cannot use observed output sequences to infer whether secret states have been visited (that is, whether the DES can conceal the status that its secret states have been visited). Over the past two decades these properties were studied separately using different methods, and particularly, in most works inference-based properties were verified based on two fundamental assumptions of deadlock-freeness and divergence-freeness, where the former implies that an automaton will always run, the latter implies that an automaton has no reachable unobservable cycle, hence the running of such an automaton will always be eventually observed. In this article, for LFSAs, a unified concurrent-composition method is shown to verify all above inference-based and concealment-based properties, resulting in a unified mathematical framework for the two categories of properties. In addition, compared with the previous methods in the literature, the concurrent-composition method does not depend on assumptions and is currently the most efficient. Finally, based on concurrent composition, we represent the negations of the above inference-based properties as linear temporal logic (LTL) formulae; by combining the concurrent composition and an additional tool called observer (i.e., the classical powerset construction for LFSAs), we also represent the above concealment-based properties as LTL formulae. Although LTL formulae model checking algorithms do not provide more efficient verification for these inference-based and concealment-based properties, the obtained LTL formulae show common similarities among these properties.