赋时离散事件系统的安全诊断

针对一类计时或非计时自动机模型,研究其赋时离散事件系统(TDES)故障诊断的安全性问题.首先对TDES的安全可诊断性进行形式化;然后通过构造一个非法语言识别器对被禁止危险操作序列进行识别,在此基础上构建一个安全诊断器,提出一种基于安全诊断器的安全诊断方法,并得到一个关于TDES安全可诊断性的充分必要条件,从而实现TDES的安全故障诊断.     

观测永久丢失下随机离散事件系统故障预测的验证算法

针对随机离散事件系统在故障预测时可能出现系统观测永久丢失,导致预测不准确的问题,提出一种观测永久丢失下故障预测验证的算法。首先对观测永久丢失的随机离散事件系统的U-可预测性进行了形式化。其次使用随机预测器构造了一个随机离散事件系统的U-预测器,实现了系统的故障预测。基于U-预测器,提出了随机离散事件系统U-可预测性的充分必要条件及验证算法,并且引入成对的方式,明显地改进了该验证算法的复杂度。仿真结果表明,该验证算法使得观测永久丢失下系统故障预测准确。最后,实例说明观测永久丢失下故障预测验证算法的应用。结果表明,该验证算法相比现有同类验证算法应用范围更广,验证结果更精确。     

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。     

离散事件动态系统理论的发展动向

离散事件动态系统(DEDS)是这样一类人造系统,它在每一时间点上的变化发展依赖于许多不同事件的复杂交互作用,其状态仅在离散的时间点上发生变化。这样的系统很多,如制造系统、计算机系统、城市交通系统、排队服务系统、复杂的多模式过程控制系统等。离散事件动态系统理论自1980年由美国哈佛大学著名教授何毓琦(Y.C.Ho)倡导研究以来,已取得了不小进展。十多年后的今天,问题和成果并存,我们对建立这一理论的困难也有了进一步的认识。本文从探索的角度向读者简介一下此理论的发展情况,与计算机科学的联系,潜在的应用问题,及讨论一下此理论目前存在的问题和未来发展方向。本文不     

离散事件动态系统稳定性分析方法

本文提出一种受控时序PETRI网络方法以建立离散事件动态系统状态空间模型,并以此为基础给出一类离散事件系统的稳定性定义及一种新的稳定性分析方法.     

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.     

“线性”离散事件动态系统的特征值

本文讨论无环向图和皮特利网描述的一类离散事件动态系统的特征值的求解方法，对事件延迟系统，给出了频域的特征矩阵M（z)的特征值求法，并由之提出一个新的求取闭环无环向图描述的离散系统特征值的方法。     

Detectability in stochastic discrete event systems

A discrete event system possesses the property of detectability if it allows an observer to perfectly estimate the current state of the system after a finite number of observed symbols, i.e., detectability captures the ability of an observer to eventually perfectly estimate the system state. In this paper we analyze detectability in stochastic discrete event systems (SDES) that can be modeled as probabilistic finite automata. More specifically, we define the notion of A-detectability, which characterizes our ability to estimate the current state of a given SDES with increasing certainty as we observe more output symbols. The notion of A-detectability is differentiated from previous notions for detectability in SDES because it takes into account the probability of problematic observation sequences (that do not allow us to perfectly deduce the system state), whereas previous notions for detectability in SDES considered each observation sequence that can be generated by the underlying system. We discuss observer-based techniques that can be used to verify A-detectability, and provide associated necessary and sufficient conditions. We also prove that A-detectability is a PSPACE-hard problem.     

非线性DEDS的标准结构

非线性DEDS是指由极大极小函数描述的系统, 常见于计算机科学、控制论、运筹学等领域, 考虑非自治非线性DEDS的结构问题, 通过引入白色图和凝白色图, 得到了系统能达和能观的两个充要条件以及系统的标准结构, 同时还给出了它们的矩阵表示.     

Diagnosability of discrete event systems and its applications

As man-made systems become more and more complex, diagnostics of component failures is no longer an easy task that can be performed based on experience and intuition. Therefore, it is important to develop a systematic approach to diagnostic problems. Diagnostics can be done either on-line or off-line. By on-line diagnostics, we mean diagnostics performed while the system to be diagnosed is in normal operation. On the other hand, in off-line diagnostics, the system is not in normal operation. We will study both on-line and off-line diagnostics in this paper and identify main features and differences of these two types of diagnostics. We will also introduce the concept of diagnosability and study its properties, all in the framework of discrete event systems. This study is motivated by diagnostic problems in the automotive industry and we will emphasize its applications.     

Polynomial-time verification of diagnosability of fuzzy discrete event systems

A fuzzy approach to perform diagnosis of fuzzy discrete event systems(FDESs)is proposed by constructing diagnosers,which may more effectively cope with the problems of vagueness and fuzziness arising from failure diagnosis of fuzzy systems.However,the complexity of constructing this kind of diagnosers is exponential in the state space and the number of fuzzy events of the system.In this paper,we present an algorithm for verifying the diagnosability of FDESs based on the construction of a nondeterministic automaton called F-verifier instead of diagnosers.Both the construction of F-verifiers and the verification of diagnosability of FDESs can be realized with a polynomial-time complexity.     

Optimizing discrete event dynamic systems via the gradient surface method

In this paper we propose a gradient surface method (GSM) for the optimization of discrete event dynamic systems. GSM combines the advantages of response surface methodology (RSM) and efficient derivative estimation techniques like perturbation analysis (PA) or likelihood ratio method (LR). In GSM, the gradient estimation is obtained by PA (or LR), and the performance gradient surface is obtained from observations at various points in a fashion similar to the RSM. Zero points of the successive approximating gradient surface are then taken as the estimates of the optimal solution. GSM is characterized by several attractive features: it is a single-run method and more efficient than RSM; it uses at each iteration step the information from all data points rather than just the local gradient; it tries to capture the global features of the gradient surface and thereby quickly arrives at the vicinity of the optimal solution. A number of examples are exhibited to illustrate this method.This work was supported by the Office of Naval Research Grants Nos. N00014-90-K-1093 and N00014-89-J-1023, by National Science Foundation Grant No. ECS-85-15449 and by Army Grant No. DAAL-03-86-K-0171.     

Fault detection and isolation in manufacturing systems with an identified discrete event model

In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.     

模型未知的离散事件系统故障诊断方法

针对离散事件系统模型难以建立的大型实际系统,无法对其进行有效故障诊断的问题,提出一种基于主动学习的故障诊断方法。首先,为获取到的系统事件日志添加正常/故障标签,并将日志集划分为训练集和测试集,提出一种基于抽象技术的迭代算法提取训练集中日志的故障特征样本。然后,通过故障特征样本构造初始故障识别器,并利用测试集中的日志检验识别器的准确性。仿真结果表明,该故障诊断算法使得模型未知下诊断精度更高。最后,实例说明系统模型未知下故障诊断算法的应用。与现有研究相比,提出的方法可以在系统模型未知下进行故障诊断且算法复杂度为多项式,诊断精度更高,应用范围更加广泛。     

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.     

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