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.     

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.     

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.     

Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

二维随机FM-II系统的状态估计

This paper is concerned with state estimation of two-dimensional (2-D) discrete stochastic systems. First, 2-D discrete stochastic system model is established by extending system matrices of the well-known Fornasini-Marchesini's second model into stochastic matrices. Each element of these stochastic matrices is second-order weakly stationary white noise sequences. Secondly, a linear and unbiased full-order state estimation problem for 2-D discrete linear stochastic model is formulated. Two estimation problems considered are the designs for the mean-square bounded estimation error and for the mean-square stochastic version of the suboptimal H∞ estimator, respectively. Our results can be seen as extensions of the 2-D linear deterministic case. Finally, illustrative examples are provided.     

元胞自动机模型在航空发动机故障诊断中的应用

航空发动机一般在高温、高压和高速转动的状态下工作,因此很难获取其全生命周期试验数据。针对无完整生命周期数据的小样本集合进行设计,提出一种基于元胞自动机的航空发动机故障诊断方法,该方法在获取发动机故障特征信息之后,利用元胞的扩散机制获取故障模式的分类边界。其优势在于:在给定的数据集前提下,可以利用较少的运行时间来约减给定的规则样本;可以利用积累或迭代的方式来分步获得原给定样本集的一致性子集。同时,算法的可积累性、运算时间可控等特点,使得该算法能连续应用在航空发动机试验样本数据集由小样本持续增加到大样本的过程中。该方法的应用对发动机的故障诊断的研究具有重要的指导意义。     

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.     

单输入/单输出非线性系统的状态观测器设计*

本文提出了一类非线性系统的状态观测器设计方法，针对单输入／单输出非线性系统，首先通过非线性状态变换，把原非线性系统变换成简单的中间非线性模型；然后对该简单的中间非线性模型设计稳定的常增益状态观测器，从而经状态逆变换得到原非一系统的非线性状态探测器，最后通过一倒立摆非线性系统的仿真研究说明了该方法的有效性和可行性。     

Multitasking Supervisory Control of Discrete-Event Systems

This paper presents an approach for functionally dealing with multiple tasks in the supervisory control of discrete-event systems (DES). The colored marking generator (CMG), a special type of Moore automaton, is introduced as a model that distinguishes classes of tasks in DES. The main results of supervisory control theory are extended to this model, allowing the synthesis of minimally restrictive supervisors, which respect the safety specifications and ensure coreachability of multiple control objectives. Reversibility is also investigated as an alternative way of ensuring liveness of multiple tasks. Two examples illustrate the convenience of this approach.     

概率有限状态自动机的代数性质

利用矩阵、同态、同构、同余等代数工具研究概率有限状态自动机的代数性质.首先定义了输入集上两个字符串同余的概念,并利用概率转移矩阵给出2个字符串同余的一些等价刻画.进而提出概率有限状态自动机同态和同构的概念,并给出了概率有限状态自动机同态定理.证明了2个概率有限状态自动机同构的充要条件是它们的概率转移矩阵可以通过第1种行列初等变换相互转化;同时提出了2个概率有限状态自动机积与和的概念,并得到了积自动机、和自动机的同态关系.最后将模糊自动机中交换的概念引入到概率有限状态自动机中,并利用概率转移矩阵给出了此类自动机交换的一些等价刻画以及和自动机、积自动机交换的充要条件.     

一种基于反向有限自动机的多模式匹配算法

在基于有限自动机的多模式匹配算法DFSA的基础上,结合改进的BM单模式匹配算法的优点,提出一种快速的多模式字符串匹配算法。在一般情况下,该算法不需要匹配目标文本串的每个字符,能充分利用匹配过程中本次匹配不成功的信息和已成功的信息,跳过尽可能多的字符。实验表明,模式串较短时,该算法需要的时间约为DFSA的1/2,模式串较长时,所需时间约为DFSA算法的1/3。     

基于有穷状态自动机原理的粉状物料运输车智能卸料控制程序

控制程序是粉状智能物料运输车完成自动卸料过程的关键,控制程序设计的好坏决定卸料过程能否顺利完成。针对新型粉状物料运输车智能卸料控制器,采用基于消息传递机制和有限状态机原理的多分支结构,程序结构简单清晰,容易编写、调试、功能扩展。     

Fault Diagnosis of Discretely Controlled Continuous Systems by Means of Discrete-Event Models

Discretely controlled continuous systems consist of continuous plants whose operation mode is switched by a feedback controller. Fault diagnosis has to use the measured switching sequence and the measured continuous movement to detect and identify faults. In order to get the diagnostic algorithm with the least possible complexity, the kind of measurement information and the granularity of the model have to be chosen in accordance with the faults to be detected. The paper presents five diagnostic methods in a uniform way, which differ with respect to the model and the measurement information used. From the hybrid model of discretely controlled continuous systems, four more abstract representations are derived, which have the form of embedded maps, semi-Markov processes, timed automata and nondeterministic automata. The validity of the diagnostic result is ensured by the claim that the models should be complete and, hence, consistent with all the input-output sequences of the discretely controlled system in the appropriate fault case. In this way a hierarchy of models and of diagnostic results is obtained. The methods are illustrated by an example.

Finding Minimum Vertex Covering in Stochastic Graphs: A Learning Automata Approach

Structural and behavioral parameters of many real networks such as social networks are unpredictable, uncertain, and have time-varying parameters, and for these reasons, deterministic graphs for modeling such networks are too restrictive to solve most of the real-network problems. It seems that stochastic graphs, in which weights associated to the vertices are random variables, might be better graph models for real-world networks. Once we use a stochastic graph as the model for a network, every feature of the graph such as path, spanning tree, clique, dominating set, and cover set should be treated as a stochastic feature. For example, choosing a stochastic graph as a graph model of an online social network and defining community structure in terms of clique, the concept of a stochastic clique may be used to study community structures’ properties or define spreading of influence according to the coverage of influential users; the concept of stochastic vertex covering may be used to study spread of influence. In this article, minimum vertex covering in stochastic graphs is first defined, and then four learning, automata-based algorithms are proposed for solving a minimum vertex-covering problem in stochastic graphs where the probability distribution functions of the weights associated with the vertices of the graph are unknown. It is shown that through a proper choice of the parameters of the proposed algorithms, one can make the probability of finding minimum vertex cover in a stochastic graph as close to unity as possible. Experimental results on synthetic stochastic graphs reveal that at a certain confidence level the proposed algorithms significantly outperform the standard sampling method in terms of the number of samples needed to be taken from the vertices of the stochastic graph.     

Diagnosis of Active Systems by Automata-Based Reasoning Techniques

This paper presents a method for the diagnosis of active systems, these being a class of distributed asynchronous discrete-event systems, such as digital networks, communication networks, and power transmission protection systems. Formally, an active system is viewed as a network of communicating automata, where each automaton describes the behavior of a system component. The diagnostic method encompasses four steps, namely system modeling, reconstruction planning, behavior reconstruction, and diagnosis generation. System modeling formally defines the structure and behavior of system components, as well as the topology of the active system. Based on optimization criteria, reconstruction planning breaks down the problem of system behavior reconstruction into a hierarchical decomposition. Behavior reconstruction yields an intensional representation of all the dynamic behaviors that are consistent with the available system observation. Eventually, diagnosis generation extracts diagnostic information from the reconstructed behaviors. The diagnostic method is applied to a case study in the power transmission network domain. Unlike other proposals, our approach both deals with asynchronous events and does not require any global diagnoser to be built off-line. The method, which is substantiated by an ongoing implementation, is scalable, incremental, and amenable to parallelism, so that real size problems can be handled.     

Maximum Correntropy Kalman Filtering for Non-Gaussian Systems With State Saturations and Stochastic Nonlinearities

This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities. The stochastic nonlinearities, which take the form of statemultiplicative noises, are introduced in systems to describe the phenomenon of nonlinear disturbances. To resist non-Gaussian noises, we consider a new performance index called maximum correntropy criterion(MCC) which describes the similarity between two stochastic ...     

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。     

Event Counting of Partially-Observed Discrete-Event Systems with Uniformly and Nonuniformly Bounded Diagnosis Delays

We present an approach dealing with repeated fault events in the framework of model-based monitoring of discrete-event systems (DES). Various notions of diagnosability reported in the literature deal with uniformly bounded finite detection of counting delays over all faulty behaviors (uniform delays for brevity). The situation where the diagnosability notion of interest fails to hold under a given observation configuration leads typically to the deployment of more observational devices (e.g., sensors), which may be costly or infeasible. As an alternative to the additional deployment of observational devices, one might want to relax the uniformity of delays, while delays remain finite. To this end, we introduce a notion of diagnosability characterized with nonuniformly bounded finite counting delays (nonuniform counting delays for brevity), where finite delay bounds can vary on faulty behaviors. To evaluate the introduced notion of diagnosability with nonuniform counting delays, a polynomial-time verification algorithm is developed. Notably, the developed verification technique can readily be modified to construct a computationally superior verification algorithm for diagnosability under uniformly bounded finite counting delays (uniform counting delays for brevity) as compared to an algorithm previously reported in the literature. We also develop a novel on-line event counting algorithm that improves the time and space complexities of the currently available algorithms for the counting of special events.

控制系统可诊断性的内涵与研究综述

作为表征控制系统故障诊断能力的属性, 故障可诊断性揭示了故障诊断深层次的内涵.将可诊断性分析纳入控制系统与诊断方案的设计环节, 可以从根本上提高系统对故障的诊断能力, 为研究故障诊断提供新的思路.本文分别从可诊断性的内涵、研究现状以及潜在发展趋势三个角度系统地对可诊断性进行分析.首先, 从定义、影响因素、与已有概念的关系以及应用四个方面剖析了控制系统可诊断性的内涵和研究意义.其次, 分别从可诊断性评价与设计两个方面对可诊断性的研究现状进行分析.最后, 通过对可诊断性已有成果进行总结归纳, 探讨了可诊断性研究存在的不足以及未来发展的趋势.     

Towards a Formal Semantics for UML/MARTE State Machines Based on Hierarchical Timed Automata

UML is a widely-used,general purpose modeling language.But its lack of a rigorous semantics forbids the thorough analysis of designed solution,and thus precludes the discovery of significant problems at design time.To bridge the gap,the paper investigates the underlying semantics of UML state machine diagrams,along with the time-related modeling elements of MARTE,the profile for modeling and analysis of real-time embedded systems,and proposes a formal operational semantics based on extended hierarchical timed automata.The approach is exemplified on a simple example taken from the automotive domain.Verification is accomplished by translating designed models into the input language of the UPPAAL model checker.