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.     

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.     

基于事件图的离散事件仿真模型并行检验方法

非形式化仿真模型验证方法易受主观因素的影响且具有不完备性,而传统的形式化模型检验方法由于受到状态空间爆炸问题的影响,很难处理大规模的仿真模型.并行模型检验方法以其完备性、高效性已经在工业界中得到了成功的应用,但是由于涉及到形式化规约、逻辑学以及并行计算等多项技术,应用难度较大.针对上述问题,提出了基于事件图的离散事件仿真模型并行检验方法.该方法首先对事件图在模型同步方面进行了扩展,给出了扩展事件图的形式化定义、语法及语义;然后将扩展事件图模型转换到分布并行验证环境的DVE模型,成功地将并行模型检验方法应用于仿真模型验证领域.该方法使得仿真人员无须学习新的形式化验证语言就能采用并行模型检验方法对仿真模型进行形式化验证,可降低模型并行验证的难度,从而有效提高模型验证的效率和完备性.实验结果表明了该方法的有效性,有利于扩展并行模型检验方法在仿真领域中的应用.     

离散事件系统N步稳定性分析

讨论基于自动机/形式语言模型的离散事件系统(DES)稳定性问题,引入了确定性离散事件系统N步稳定性定义,并得到了稳定性的判据定理,推导了具体的算法实现。该算法具有多项式复杂度。     

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.     

含故障模式的值传递诊断及其完备性讨论

基于模型的诊断是人工智能领域一个活跃的研究方向.基于值传递的诊断是一种高效的故障诊断方法,但在一般情况下不完备,且系统模型仅描述元件的正常行为.扩充了基于值传递的系统模型,可以描述系统元件的多种故障模式;重新定义了诊断,明确了该模型下得到的诊断与一致性诊断和溯因诊断之间的关系;同时,指出了值传递与真实诊断的关系,为诊断测试提出了新的思路;最后,给出了值传递诊断方法的完备的充分条件,对推进值传递诊断方法的实际应用有积极意义.     

判定非确定离散事件系统稳定性的梯度搜索算法

讨论基于非确定自动机/形式语言模型的非确定离散事件系统(NDES)稳定性问题.引入非确定离散事件系统稳定性的定义,并得到了稳定性的判据定理.给出了基于梯度的搜索算法,该算法可有效消除观测器的冗余,从而降低了计算复杂度.     

基于加权特性关系的扩展粗糙集模型

综合考虑不完备信息系统中信息缺失的不同情况及属性本身的重要性,提出了加权特性关系,给出了基于加权特性关系的扩展粗糙集模型及其近似集的定义和性质,并用实际例子解释了该扩展粗糙集模型的近似集计算方法。     

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。     

基于值传递的快速故障诊断

本文提出了一种基于值传递的系统模型，重新定义了诊断，该定义与Reiter经典的诊断定义等价。在此基础上，给出了一种线性时间找到一个极小诊断的算法，该方法不需要计算极小冲突而直接得到极小诊断。该算法在给出诊断的同时，还给出了系统与该诊断对应的当前行为的一种合理解释。最后，给出了该方法与诊断测试结合实现系统修复的方法。     

State estimation and detectability of probabilistic discrete event systems

A probabilistic discrete event system (PDES) is a nondeterministic discrete event system where the probabilities of nondeterministic transitions are specified. State estimation problems of PDES are more difficult than those of non-probabilistic discrete event systems. In our previous papers, we investigated state estimation problems for non-probabilistic discrete event systems. We defined four types of detectabilities and derived necessary and sufficient conditions for checking these detectabilities. In this paper, we extend our study to state estimation problems for PDES by considering the probabilities. The first step in our approach is to convert a given PDES into a nondeterministic discrete event system and find sufficient conditions for checking probabilistic detectabilities. Next, to find necessary and sufficient conditions for checking probabilistic detectabilities, we investigate the “convergence” of event sequences in PDES. An event sequence is convergent if along this sequence, it is more and more certain that the system is in a particular state. We derive conditions for convergence and hence for detectabilities. We focus on systems with complete event observation and no state observation. For better presentation, the theoretical development is illustrated by a simplified example of nephritis diagnosis.     

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。     

Decentralized fault diagnosis approach without a global model for fault diagnosis of discrete event systems

Diagnosability property ensures that a predefined set of faults are diagnosable by a centralized diagnoser built using a global model of the system, while co-diagnosability guarantees that these faults are diagnosed in decentralized manner using a set of local diagnosers. A fault must be diagnosed by at least one local diagnoser by using its proper local observation of the system. The aim of using decentralized diagnosis approaches is to overcome the space complexity and weak robustness of centralized diagnosis approaches while at the same time preserving the diagnostic capability of a centralized diagnosis. However, co-diagnosability property is stronger than diagnosability property. If a system is co-diagnosable, then it is diagnosable, while a diagnosable system does not ensure that it is co-diagnosable. Therefore, the challenge of decentralized diagnosis approaches is to perform local diagnosis and to verify that it is equivalent to the centralized one without the need for a global model. In this paper, an approach is proposed to obtain co-diagnosable decentralized diagnosis structure of discrete event systems without the use of a global model. This approach is based on the synchronization of local diagnosis decisions in order to solve the ambiguity between local diagnosers. This synchronization allows obtaining local diagnosis equivalent to the global one without the use of a global model.     

Detectability of Discrete Event Systems

In this note, we investigate the detectability problem in discrete event systems. We assume that we do not know initially which state the system is in. The problem is to determine the current and subsequent states of the system based on a sequence of observations. The observation includes partial event observation and/or partial state observation, which leads to four possible cases. We further define four types of detectabilities: strong detectability, (weak) detectability, strong periodic detectability, and (weak) periodic detectability. We derive necessary and sufficient conditions for these detectabilities. These conditions can be checked by constructing an observer, which models the estimation of states under different observations. The theory developed in this note can be used in feedback control and diagnosis. If the system is detectable, then the observer can be used as a diagnoser to diagnose the failure states of the system.     

Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era

In smart industrial systems, in many cases, a fault can be captured as an event to represent the distinct nature of subsequent changes. Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis. Most event-based diagnosis techniques rely on perfect observations of observable events. However, in practice, it is common to miss an observable event due to a problem in sensor-readings or communication/transmission channels. This paper develops a fault diagnosis tool, referred to as diagnoser, which can robustly detect, locate, and isolate occurred faults. The developed diagnoser is resilient against missed observations. A missed observation is detected from its successive sequence of events. Upon detecting a missed observation, the developed diagnoser automatically resets and then, asynchronously resumes the diagnosis process. This is achieved solely based on post-reset/activation observations and without interrupting the performance of the system under diagnosis. New concepts of asynchronous detectability and asynchronous diagnosability are introduced. It is shown that if asynchronous detectability and asynchronous diagnosability hold, the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations. The proposed technique is applied to diagnose faults in a manufacturing process. Illustrative examples are provided to explain the details of the proposed algorithm. The result paves the way towards fostering resilient cyber-physical systems in Industry 4.0 context.      

EDEN: An Intelligent Software Environment for Diagnosis of Discrete-Event Systems

A software environment, called EDEN, that prototypes a recent approach to model-based diagnosis of discrete-event systems, is presented. The environment integrates a specification language, called SMILE, a model base, and a diagnostic engine. SMILE enables the user to create libraries of models and systems, which are permanently stored in the model base, wherein both final and intermediate results of the diagnostic sessions are hosted as well. Given the observation of a physical system gathered during its reaction to an external event, the diagnostic engine performs the a posteriori reconstruction of all the possible evolutions of the system over time and, then, draws candidate diagnoses out of them. The diagnostic method is described using a simplified example within the domain of power transmission networks. Strong points of the method include compositional modeling, support for model update, ability to focus on any sub-system, amenability to parallel execution, management of multiple faults, and broad notions of system and observation.     

Solving the problem of incomplete data in medical diagnosis via interval modeling

This paper presents an approach to making accurate and high-quality decisions under incomplete information. Our comprehensive approach includes interval modeling of incomplete data, uncertaintification of classical models and aggregation of incomplete results. We conducted a thorough evaluation of our approach using medical data for ovarian tumor diagnosis, where the problem of missing data is commonly encountered. The results confirmed that methods based on interval modeling and aggregation make it possible to reduce the negative impact of lack of data and lead to meaningful and accurate decisions. A diagnostic model developed in this way proved better than classical diagnostic models for ovarian tumor. Additionally, a framework in R that implements our method was created and is available for reproduction of our results. The proposed approach has been incorporated into a real-life diagnosis support system – OvaExpert.     

Unfolding-based diagnosis of systems with an evolving topology

We propose a framework for model-based diagnosis of systems with mobility and variable topologies, modelled as graph transformation systems. Generally speaking, model-based diagnosis is aimed at constructing explanations of observed faulty behaviours on the basis of a given model of the system. Since the number of possible explanations may be huge, we exploit the unfolding as a compact data structure to store them, along the lines of previous work dealing with Petri net models. Given a model of a system and an observation, the explanations can be constructed by unfolding the model constrained by the observation, and then removing incomplete explanations in a pruning phase. The theory is formalised in a general categorical setting: constraining the system by the observation corresponds to taking a product in the chosen category of graph grammars, so that the correctness of the procedure can be proved by using the fact that the unfolding is a right adjoint and thus it preserves products. The theory should hence be easily applicable to a wide class of system models, including graph grammars and Petri nets.     

Fault detection for discrete event systems using Petri nets with unobservable transitions

In this paper we present a fault detection approach for discrete event systems using Petri nets. We assume that some of the transitions of the net are unobservable, including all those transitions that model faulty behaviors. Our diagnosis approach is based on the notions of basis marking and justification, that allow us to characterize the set of markings that are consistent with the actual observation, and the set of unobservable transitions whose firing enable it. This approach applies to all net systems whose unobservable subnet is acyclic. If the net system is also bounded the proposed approach may be significantly simplified by moving the most burdensome part of the procedure off-line, thanks to the construction of a graph, called the basis reachability graph.     

Distributed inversion in timed discrete event systems

We consider a discrete event system (DES) modeled by a timed automaton with partial state and event observations. We view the system as an input-output system, where the input is a sequence of event lifetimes, and the output is the resulting sequence of events, states, and transition epochs. We consider the problem of extracting event lifetimes (input) from observations of the output trajectory, which we callinversion. We give necessary and sufficient conditions forinvertibility, and an algorithm that extracts event lifetimes from any given output observation of an invertible system. We describe a distributed timed DES model based on the prioritized synchronous product of subsystems, and study the inversion problem in this framework. We show that invertibility in the subsystems implies invertibility in the global system. To illustrate our results, we provide an example of a tandem network.