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.     

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诊断性的充分必要条件,实现动态观测下随机离散事件系统的故障诊断.     

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.     

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。     

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.     

部分可观Petri网结构信息在故障诊断中的应用

针对离散事件系统的故障诊断问题,本文提出了一种基于部分可观Petri网结构信息的诊断方法.它包括两个部分,第1部分利用故障变迁的可诊断子网确定故障变迁的可诊断性.第2部分在故障可诊断的基础上提出一种在线故障诊断方法:首先,利用Petri网的几种基本子网来分析故障变迁的可诊断子网的结构信息;其次,根据给定的可观测变迁序列和可诊断子网的结构特征来描述子网内部托肯的流动形式;最后,定义故障函数,并结合具体实例来描述故障变迁的发生情况.该故障诊断的方法基于部分可观Petri网结构信息,无需遍历系统状态空间,免去多项式级的计算复杂性,能够满足实时性的要求.     

基于Petri网的故障诊断研究理论的综述

故障诊断是离散事件系统中一项重要的研究内容,对于保障系统安全具有积极意义。基于Petri网的故障诊断相关研究主要分为故障可诊断性研究以及故障诊断器的构造理论研究,故障可诊断性又可以进一步分为一般可诊断性与K-可诊断性,而故障诊断器的设计方法又可以按照适用系统类型进一步分类。综述了故障诊断理论中可诊断性、K可诊断性的各类研究方法和研究结论,介绍了离散Petri网系统、连续Petri网系统和分布式Petri网系统中故障诊断器的设计方法,并对各类方法的特点进行了重点分析。最后,给出了基于Petri网的故障诊断进一步研究的方向与应用难点,其对今后研究有一定的指导意义。     

改进的部分可观Petri网系统在线故障诊断器设计

本文研究部分可观Petri网建模的离散事件系统的故障检测问题.针对现有的部分可观Petri网系统的在线故障诊断器存在故障诊断率较低的缺陷,本文提出了整数线性规划与广义互斥约束集成的部分可观Petri网系统在线故障诊断改进算法.假定部分可观Petri网系统的结构与初始标识为已知,故障被建模为不可观变迁.首先,算法需要观测接收事件序列,求解部分可观Petri网的整数线性规划问题,算法对系统的故障进行初步诊断.初步诊断为不确定诊断的情形,采用广义互斥约束的方法进行诊断.最后,通过离散事件系统实例分析,采用本文的算法,故障诊断率显著提高,验证了算法的有效性.     

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。     

模糊离散事件系统基于验证器的模式故障诊断

针对模糊系统在运行过程中可能出现由多个事件触发的故障,研究模糊离散事件系统模式故障的诊断问题,提出一种基于验证器的模式故障诊断方法.先对模糊离散事件系统中最常见的模式故障,引入S类型模式故障和T类型模式故障两个概念,再分别对模糊离散事件系统的S类型和T类型模式故障的可诊断性进行形式化.为验证模糊系统模式故障的可诊断性,构造一个验证器自动机,并得到一个关于模糊离散事件系统模式故障可诊断性的充分必要条件,实现对模糊系统模式故障的诊断.     

Comparative analysis of related notions of robust diagnosability of Discrete-Event Systems

In this paper, we revisit the problem of robust diagnosability of Discrete-Event Systems (DES), and present a comparative analysis between the following notions of robust diagnosability existing in the literature: (i) diagnosability of DES subject to permanent sensor failures, assuming that sensors may fail only before the first occurrence of the events they are supposed to record; (ii) diagnosability of DES subject to permanent sensor failures, assuming that sensors may fail at any time; (iii) diagnosability of DES against intermittent loss of observations; (iv) diagnosability of partially observed DES; (v) generalized robust diagnosability. We show that all of the robust diagnosability definitions are particular cases of the generalized robust diagnosability by presenting transformation mechanisms for each one of the analyzed robust diagnosability notions so as to convert it into an equivalent generalized robust diagnosability problem. We also compare the use of projections and masks in the context of language diagnosability and show that there is no loss of generality in using projections in place of masks by presenting a map that transforms the language diagnosability problem with observation mask into an equivalent one with projection.     

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.     

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.     

Design of light weight exact discrete event system diagnosers using measurement limitation: case study of electronic fuel injection system

Employing a state-based Discrete Event System (DES) modelling framework, this paper proposes a new fault diagnosis approach called measurement limitation-based abstract DES diagnosis (MLAD), which attempts to reduce state space complexity of the diagnosis process while simultaneously preserving full diagnosability. The MLAD approach carefully applies a set of distinct measurement limitation operations on the state variables of the original DES model based on fault compartmentalisation to obtain separate behaviourally abstracted DES models and corresponding abstract diagnosers with far lower state spaces. The set of measurement limitation operations are so designed that although, any single abstract diagnoser may compromise diagnosability in seclusion, the additive combination of all diagnosers running in parallel always ensures complete diagnosability. Effective measurement limitation also ensures that the combined state space of the abstract diagnosers is much lower than that of the single full diagnoser that may be derived from the original DES model. As a case study, we have employed MLAD to incorporate failure diagnosability in a practical electronic fuel injection system. Evaluations on standard practical benchmarks show that MLAD achieves significant reduction in state space as compared to conventional monolithic full diagnosis approaches.     

Diagnosability of delay-deadline failures in fair real time discrete event models

In this article a method for failure diagnosis of real time discrete event systems (RTDES) with ‘fairness of traces’ has been developed. Discrete event system (DES) modelling framework with provision for associating timing information with the transitions are required for handling real time systems. RTDES models and timed DES (TDES) models are examples of such modelling frameworks. Failure diagnosis in untimed DES models enables only the study of diagnosability of failures resulting in a change in the logical behaviour of the failed system. In addition to logical failures, failure diagnosis in RTDES and TDES models also enables diagnosability of failures that change the timing behaviour of the system but maintain the logical behaviour. Many systems exhibit fairness of traces with respect to transitions in the sense that any trace that visits a state infinitely often has infinitely many occurrences of all the transitions that emanate from that state. The abstraction employed in obtaining their (timed) DES models often obliterates this property. The RTDES and TDES diagnosability conditions, proposed in the literature and which do not consider fairness, are shown to be inadequate in this article. A new diagnosability condition is achieved by taking into account this fairness property in the RTDES models and shown to be necessary and sufficient for such systems. An analysis of time complexity for analysing the diagnosability of systems with fairness of traces is presented.     

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.     

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 discrete-event systems

Fault detection and isolation is a crucial and challenging task in the automatic control of large complex systems. We propose a discrete-event system (DES) approach to the problem of failure diagnosis. We introduce two related notions of diagnosability of DES's in the framework of formal languages and compare diagnosability with the related notions of observability and invertibility. We present a systematic procedure for detection and isolation of failure events using diagnosers and provide necessary and sufficient conditions for a language to be diagnosable. The diagnoser performs diagnostics using online observations of the system behavior; it is also used to state and verify off-line the necessary and sufficient conditions for diagnosability. These conditions are stated on the diagnoser or variations thereof. The approach to failure diagnosis presented in this paper is applicable to systems that fall naturally in the class of DES's; moreover, for the purpose of diagnosis, most continuous variable dynamic systems can be viewed as DES's at a higher level of abstraction     

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.