不完备模型下的离散事件系统诊断方法

提出不完备模型的两种不完备性:模型定义不完备和因果关系不完备.在模型定义不完备条件下,用在线观测与模型共同约束的方法处理观测乱序及未定义事件,得到可行的诊断轨迹.相对于基于完备模型假设下不能诊断的结论,该方法扩展了诊断方法的适用范围,放松了对模型的约束要求,在因果不完备条件下,提出用因果图联系部件,解决分布式诊断中由于部件独立建模而导致的不彻底诊断,提高了诊断的精确性.通过实验验证,两种条件下的诊断方法均能在相应的不完备模型中得到预期诊断结果,并对模型进行局部修订,提高模型的完备性.     

离散事件系统基于模型诊断的研究进展

近年来,针对离散事件系统的基于模型诊断方法在大型通讯网络、电网故障诊断等领域获得了成功应用,成为人工智能与控制工程领域的热门研究课题。介绍了针对离散事件系统的基于模型诊断的基本思想与建模方法,从不同的角度对使用自动机建模的各种主要诊断方法进行了评析与比较;讨论了系统可诊断性判定方法的研究进展。从系统建模、分布式在线诊断、不完备模型下的诊断以及系统实现等方面,展望了针对离散事件系统的基于模型诊断领域中有待解决的问题。     

一种基于冲突的增量诊断方法

增量诊断是一种在离散事件系统中进行诊断的重要方法,因其能够根据新观测和原有诊断的结果进行进一步诊断,在长时间复杂行为的系统上有着良好的运行效率和诊断特性.文章提出了一种带有回溯的增量诊断方法.在离线定义的自动机链模型上根据在线观测进行局部诊断,用轨迹概率选出最可能诊断的同时,保存可行的候选诊断作为回溯点.若增量过程产生冲突,则回溯并根据新观测动态重新对候选诊断选择排序.既避免了不完全可诊断性条件下,增量诊断所面临的多候选选择,亦避免了由于仅保留最优结果导致的重复诊断.     

不确定观测下离散事件系统的可诊断性

从系统诊断的角度来看,可诊断性是离散事件系统的一个重要性质.其要求系统发生故障后经过有限步的观测可以检测并隔离故障.为简单起见,对离散事件系统可诊断性的研究大都假定观测是确定的,即观测到的事件序列与系统实际发生的可观测事件序列一致.而在实际应用中,由于感知器的精度、信息传输通道的噪声等原因,获取的观测往往是不确定的.本文重点研究观测不确定条件下离散事件系统的可诊断性问题.首先,扩展了传统可诊断性的定义,定义了观测不确定条件下的可诊断性.接着,分别给出各类观测不确定条件下的可诊断性判定方法.而在更一般的情况下,各类观测不确定可能共同存在.因此,最后给出一般情况下的可诊断性判定方法.     

基于扩展窗口的时序不完备诊断方法研究

离散事件系统诊断中,由于系统复杂度较高,对系统建模时要获得系统的完备行为非常困难。传统的诊断方法往往基于模型完备的假设,在模型不完备时会出现得不到诊断解释的问题。针对模型定义不完备中的一种情况——事件顺序定义不完备,提出了一种基于扩展窗口的时序不完备诊断方法,该方法利用相关事件无序信息,在增量诊断时通过动态改变观测窗口大小,结合两个观测窗口的观测序列,在一定程度上解决了不完备的诊断问题。该方法不仅扩展了模型完备条件的约束,得到了合理的诊断结果,而且改进了观测延迟导致的观测乱序情况,扩大了模型诊断的适用范围。最后,通过算法分析和实验结果证明该诊断方法在复杂度较低的情况下能够得到合理的诊断结果。     

基于时序的离散事件系统的可诊断性

提出一种基于事件之间的时序关系判定可诊断性的方法。首先通过添加通讯事件把全局模型分解成几个局部模型来缩减模型的规模,删除局部模型中的无用路径以降低状态空间;其次利用通讯事件和可观测事件之间的时序关系,对受限局部模型的可诊断性进行判定,得出几个判定性质,然后把这些性质运用到局部模型的可诊断性判定中,以避免同步操作的高复杂性;最后通过实例对可诊断性判定的过程进行分析。     

基于模型的诊断问题分解及其算法

对诊断问题的分解进行了研究，给出了基于模型诊断问题分解的判定定理，刻画了利用系统观测值和参量假定例化值分解诊断问题，提出了有条件可分解诊断问题的概念，进一步刻画了基于模型的诊断问题分解，对如何利用参量假定例化值分解诊断问题给出了最可能优先算法，并对该算法的正确性、完备性及复杂性进行了证明，文中的工作为具有树型结构的系统诊断效率的提高提供了理论依据。     

基于不确定观测下离散事件系统可诊断性的研究

在实际应用系统中,由于传感器故障、传感器限制和网络中的数据包丢失等原因,事件的可观测值变得不确定,使得观测系统行为变得尤为复杂。针对离散事件系统中,同个事件串可能有多个观测值以及不同状态下同个事件观测值也可能不同的问题,提出一种不确定观测下故障诊断验证的方法。首先对不确定观测的离散事件系统的可诊断性进行形式化,然后构建出用于上述故障诊断验证的验证器;基于验证器提出了系统基于不确定观测下可诊断的充要条件及验证算法;最后,实例说明不确定观测下故障诊断验证算法的应用。与现有研究相比,提出的方法对故障事件的观测值没有约束,可以为0个或多个观测值,使此方法应用的场景更为广泛。     

通过诊断图分析的快速诊断算法

基于模型诊断的主要思想是:根据系统的逻辑模型以及系统的输入,通过逻辑的推理理论能推导出系统在正常情况下的预期行为,如果观测到的系统实际行为与系统预期行为有差异,则说明系统存在故障.当系统故障时,可通过逻辑的推理理论来确定引发故障的元件集合.由于经典的基于模型诊断采用的是逻辑推理的手段来产生诊断集合,这导致了传统的基于模型诊断算法的效率非常低下.文中在原有模型诊断基础上,重新定义了诊断,并提出了一种用于诊断的诊断图的数据结构.在此基础上给出了一种基于诊断图分析的快速诊断算法.由于文中的诊断方法是一种过程化的方法,与Reiter的模型诊断的基于逻辑的方法有着本质的不同.因此,文中的方法能很好地克服经典模型诊断效率过低的问题,为诊断问题的求解带来新的前景.实验结果证明了这种新的诊断方法的高效性.     

具有多项式时间复杂性的离散事件系统安全诊断

离散事件系统的故障诊断能将已发生的不可观故障事件及时诊断出来,但往往容易忽略故障诊断期间系统的安全性.为解决这一问题,提出了一种具有多项式时间复杂性的安全故障诊断方法.先对离散事件系统的安全可诊断性进行了形式化,再通过构造一个非法语言识别器对系统被禁止操作序列进行识别,并在此基础上构建了一个对系统实施安全诊断的安全验证器,得到了一个关于离散事件系统安全可诊断性的充分必要条件,实现了对系统的安全故障诊断.同时,通过对安全验证器的构建与安全可诊断性的判定的复杂性分析,得到了该安全故障诊断方法可在多项式时间内实现等结论.     

Diagnosability of a class of discrete event systems based on observations

The diagnosability of discrete event systems has been a topic of interest to many researchers. The diagnosability conditions for various systems have evolved based on a regularity condition that is imposed on faulty traces with respect to their observable continuations. Improving upon this weak but necessary condition, a new model of diagnosability that is based on sensor outputs, which are called observations, upon a command input is proposed in this paper. Necessary and sufficient conditions are derived for the proposed diagnosability model. The search performance of the proposed diagnosability condition is of linear complexity in terms of the power set of the system events and observations, compared to the exponential complexity of the search with the existing diagnosability regularity condition. Moreover, a system that is not diagnosable according to the existing diagnosability condition may be diagnosable in the proposed diagnosability model, which includes observations.     

Diagnosability of fair discrete event systems

Failure diagnosability has been widely studied using discrete event system (DES) models. It is, however, shown in this work by means of a counterexample that the diagnosability condition, which has been shown to be necessary and sufficient in the DES context, fails to hold for many real‐world hybrid systems. This is because the abstraction employed in formulating the DES models obliterates the continuous dynamics. In the present work, a new failure diagnosability mechanism has been developed for discrete time hybrid system (DTHS) models to alleviate this problem. A new diagnosability condition is proposed and its necessity and sufficiency with respect to the diagnosability definition are established formally. Finally, the method of A‐diagnosability, which can also be used to circumvent this problem and which needs additional probabilistic information for diagnosability analysis, has been shown to have a higher computational complexity than the DTHS model based method proposed in this paper. Further, it is also highlighted that the DTHS model based diagnosability analysis technique is capable of diagnosing faults that degrade the temporal performance of the system, which cannot be handled by the A‐diagnosability analysis mechanism. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Diagnosability of fuzzy discrete event systems

In this paper, discrete event systems (DESs) are reformulated as fuzzy discrete event systems (FDESs) and fuzzy discrete event dynamical systems (FDEDSs). These frameworks include fuzzy states, events and IF-THEN rules. In these frameworks, all events occur at the same time with different membership degrees. Fuzzy states and events have been introduced to describe uncertainties that occur often in practical problems, such as fault diagnosis applications. To measure a diagnoser’s fault discrimination ability, a fuzzy diagnosability degree is proposed. If the diagnosability of the degree of the system yields one a diagnoser can be implemented to identify all possible fault types related to a system. For any degree less than one, researchers should not devote their time to distinguish all possible fault types correctly. Thus, two different diagnosability definitions FDEDS and FDES are introduced. Due to the specialized fuzzy rule-base embedded in the FDEDS, it is capable of representing a class of non-linear dynamic system. Computationally speaking, the framework of diagnosability of the FDEDS is structurally similar to the framework of diagnosability of a non-linear system. The crisp DES diagnosability has been turned into the term fuzzy diagnosability for the FDES. The newly proposed diagnosability definition allows us to define a degree of diagnosability in a class of non-linear systems. In addition, a simple fuzzy diagnosability checking method is introduced and some numerical examples are provided to illustrate this theoretical development. Finally, the potential applications of the proposed method are discussed.     

离散事件系统的同步诊断算法

针对传统系统建模方法需要假设模型完备的缺点,提出一种通过同步各部件模型的方法来解决不完备建模所导致的不完全诊断。对于离散事件系统的动态诊断进行优化,利用分布式的思想与Petri网的性质,使得各部件可以独立、并行地进行诊断,提高了诊断的速度。同时对提出的同步方法进行了可行性分析和简单实现,得到了较好的结果。     

Distributed Diagnosis Under Bounded-Delay Communication of Immediately Forwarded Local Observations

We study distributed failure diagnosis under a -bounded communication delay, where each local site transmits its observations to other sites immediately after each observation and the transmitted observation is received within at most more event executions of the plant. A notion of diagnosability is introduced so that any failure can be diagnosed within a bounded delay of its occurrence by one of the local sites using its own observations and the -bounded delayed observations received from other local sites. The local sites communicate among each other using an ldquoimmediate observation passing (iop)rdquo protocol, forwarding any observation immediately up on its occurrence. We construct models for the -bounded communication delay and use them to extend the system and nonfault specification models for capturing the effect of bounded-delay communication. By using the extended system and specification models, the distributed diagnosis problem under the immediate observation passing protocol is then converted to a decentralized diagnosis problem of our previous work, where the results are applied for verifying diagnosability and for synthesizing local diagnosers. Methods by which complexity of testing diagnosability and of online diagnosis can be reduced are presented. Finally, we compare the notions of diagnosability, codiagnosability, and diagnosability.     

Robust diagnosis of discrete event systems against intermittent loss of observations

In the usual approaches to fault diagnosis of discrete event systems it is assumed that not only all sensors work properly but also all information reported by sensors always reaches the diagnoser. Any bad sensor operation or communication failure between sensors and the diagnoser can be regarded as loss of observations of events initially assumed as observable. In such situations, it may be possible that either the diagnoser stands still or report some wrong information regarding the fault occurrence. In this paper we assume that intermittent loss of observations may occur and we propose an automaton model based on a new language operation (language dilation) that takes it into account. We refer to this problem as robust diagnosability against intermittent loss of observations (or simply robust diagnosability, where the context allows). We present a necessary and sufficient condition for robust diagnosability in terms of the language generated by the original automaton and propose two tests for robust language diagnosability, one that deploys diagnosers and another one that uses verifiers. We also extend the results to robust codiagnosability against intermittent loss of observations.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

On the history of diagnosability and opacity in discrete event systems

This paper presents historical remarks on key projects and papers that led to the development of a theory of event diagnosis for discrete event systems modeled by finite-state automata or Petri nets in the 1990s. The goal in event diagnosis is to develop algorithmic procedures for deducing the occurrence of unobservable events, based on a formal model of the system and on-line observations of its behavior. It also presents historical remarks on the early works on the property of opacity, which occurred about ten years later. Opacity can be seen as a strong version of lack of diagnosability and it has been used to capture security and privacy requirements. Finally, diagnosability is connected with the property of observability that arises in supervisory control. This paper is part of set of papers that review the emergence of discrete event systems as an area of research in control engineering.     

离散事件系统部分可诊断性分析

针对离散事件系统部分可诊断性问题,提出一种量化评价与分析方法。该方法以树状结构的故障模型为基础,引入可诊断度与可诊断深度指标,能够从可诊断故障覆盖程度与精确程度两个方面对系统可诊断性进行评价,其优点是评价结果量化表示,能为部分可诊断系统的进一步评价、分析与对比提供参考。此外,还讨论了故障模型对系统可诊断度与可诊断深度两个评价指标的影响,并给出了故障模型构造的一般原则。实例分析与讨论结果表明,所提出的可诊断度与可诊断深度指标能够准确反映系统在特定故障模型下的部分可诊断状态。所提出的部分可诊断性评价方法能为基于离散事件模型的复杂系统设计与评价提供依据,并能够进一步为智能、自适应和自愈系统的设计提供参考。