A General Algorithm for Pattern Diagnosability of Distributed Discrete Event Systems (original) (raw)

Diagnosability of Patterns in Distributed Discrete Event Systems

IFAC Proceedings Volumes, 2009

A pattern is a Finite State Machine that can describe rich faulty scenarios, such as the occurrence of single faults, multiple faults, multiple occurrences of a fault, or the repair of a system. In distributed systems, the events in the pattern, as well as in the system trajectories, are emitted from different components. Our approach is based on distributed simulation and communication to check the recognition of the pattern from the conclusion of local recognition of local patterns. The components communicate observable events and shared communication events, as well as their local recognition results during the checking process without sharing their local models in any way.

Global and local consistencies in distributed fault diagnosis for discrete-event systems

IEEE Transactions on Automatic Control, 2000

In this paper, we present a unified framework for distributed diagnosis. We first introduce the concepts of global and local consistency in terms of supremal global and local supports, then present two distributed diagnosis problems based on them. After that, we provide algorithms to achieve supremal global and local supports respectively, and discuss in detail the advantages and disadvantages of each. Finally, we present an industrial example to demonstrate our distributed diagnosis approach.

Diagnosability Analysis for Self-observed Distributed Discrete Event Systems

2013

Diagnosability is a crucial property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system and thus has a significant impact on the performance and reliability of complex systems. Most existing approaches assumed that observable events in the system are globally observed. But sometimes it is not possible to obtain global information. Thus a recent work has proposed a new framework to check diagnosability in a system where each component can only observe its own observable events to keep the internal structure private in terms of observations. However, the authors implicitly assume that local paths in components can be exhaustively enumerated, which is not true in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system, i.e., what we call joint diagnosability in a self-observed distributed system. First we prove the undecidability of joint diagnosability with un...

Undecidable Case and Decidable Case of Joint Diagnosability in Distributed Discrete Event Systems

2013

Diagnosability is an important property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. Most existing approaches assumed that each observable event in the system is globally observed. Considering the cases where there is no global information, one of our recent work proposed a new framework to check diagnosability in a system where each component can only observe its own observable events to keep the internal structure private in terms of observations. However, we assumed that the local paths in each component can be exhaustively enumerated, which is not suitable in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system in a general case, i.e., what we call joint diagnosability in a self-observed distributed system. First, we prove the undecidability of joint diagnosability with unobservable communication events by reducing the Post's Correspondence Problem to joint diagnosability problem. We also propose an algorithm to check a sufficient but not necessary condition of joint diagnosability, which is then adapted when the assumption of all communication events being unobservable is relaxed, i.e., communication events could be either observable or unobservable. Then, we discuss about the decidable case where communication events are all observable and develop a new efficient algorithm to test it. Finally, we also provide an important property of joint diagnosability after analyzing its relationship with classical diagnosability.

Hierarchical Fault Diagnosis for Discrete-Event Systems under Global Consistency

Discrete Event Dynamic Systems, 2006

In previous work the authors proposed a distributed diagnosis approach consisting of two phases-preliminary diagnosis in each local diagnoser and interdiagnoser communication. The objective of communication is to achieve either global or local consistency among local diagnoses, where global consistency is captured by the equilibrium concept of supremal global support. To achieve this equilibrium, an algorithm called Computational Procedure for Global Consistency (CPGC) was proposed. But it turns out that CPGC has high time complexity and weak scalability. To rectify these shortcomings, we propose a hierarchical computational procedure. A further advantage of this procedure is demonstrated, based on multiresolutional diagnosis. With the latter, fault detection is conducted at each hierarchical level, so that computation can be confined to those modules likely to possess faults, while fault-free modules are safely disregarded. A simplified industrial example is provided in illustration.

Diagnosis of Discrete Event Systems Using Decentralized Architectures

Discrete Event Dynamic Systems, 2007

Decentralized diagnosis of discrete event systems has received a lot of attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. This paper casts the problem of decentralized diagnosis in a new hierarchical framework. A key feature is the exploitation of different local decisions together with appropriate rules for their fusion. This includes local diagnosis decisions that can be interpreted as "conditional decisions". Under this new framework, a series of new decentralized architectures are defined and studied. The properties of their corresponding notions of decentralized diagnosability are characterized and their relationship with existing work described. Corresponding verification algorithms are also presented and on-line diagnosis strategies discussed. *

New results on decentralized diagnosis of discrete-event systems

2004

The past decade has witnessed the development of a body of theory, with associated applications, for fault diagnosis of dynamic systems that can be modeled in a discrete event systems framework. This paper presents several new notions of diagnosability, together with on-line diagnosis decision rules, in the context of a general decentralized architecture that allows for the use of "conditional decisions" by local diagnosers. The properties of these new notions of diagnosability are presented and their relationship with existing work discussed. Verification algorithms and local diagnoser synthesis methods are briefly outlined.

Diagnosability Analysis of Discrete Event Systems with Autonomous Components

European Conference on Artificial Intelligence, 2010

Diagnosability is the property of a given partially observable system model to always exhibit unambiguously a failure behavior from its only available observations in finite time after the fault occurrence, which is the basic question that underlies diagnosis taking into account its requirements at design stage. However, for the sake of simplicity, the previous works on diagnosability analysis of discrete event systems (DESs) have the same assumption that any observable event can be globally observed, which is at the price of privacy. In this paper, we first briefly describe cooperative diagnosis architecture for DESs with autonomous components, where any component can only observe its own observable events and thus keeps its internal structure private. And then a new definition of cooperative diagnosability is consequently proposed. At the same time, we present a formal framework for cooperative diagnosability checking, where global consistency of local diagnosability analysis can be achieved by analyzing communication compatibility between local twin plants without any synchronization. The formal algorithm with its discussion is provided as well. 2 PRELIMINARIES In this section, we first describe how to model DESs with autonomous components and then give some important concepts before proposing cooperative diagnosis architecture for such systems. 2.1 System model We consider a distributed DES composed of a set of autonomous components {G 1 , G 2 ,..., G n } that communicate with each other by communication events. Moreover, any component can only observe its own observable events and thus can keep its internal structure private. This kind of system is modeled by a set of FSMs with each one representing the local model of one component.

Decentralized Failure Diagnosis of Discrete Event Systems

IEEE Transactions on Systems, Man, and Cybernetics, 2006

By decentralized diagnosis we mean diagnosis using multiple diagnosers, each possessing its own set of sensors, without involving any communication among diagnosers or to any coordinators. The notion of decentralized diagnosis is formalized by introducing the notion of codiagnosability that requires that a failure be detected by one of the diagnosers within a bounded delay. Algorithms of complexity polynomial in the size of the system and the nonfault specification are provided for: 1) testing codiagnosability, 2) computing the bound in delay of diagnosis, 3) offline synthesis of individual diagnosers, and 4) online diagnosis using them. The notion of codiagnosability and the above algorithms are initially presented in a setting of a specification language (violation of which represents a fault) and are later specialized to the case where faults are modeled as the occurrences of certain events. The notion of strong codiagnosability is also introduced to capture the ability of being certain about both the failure as well as the nonfailure conditions in a system within a bounded delay.

Diagnosis of a class of distributed discrete-event systems

IEEE Transactions on Systems, Man, and Cybernetics, 2000

Discrete-event modeling can be applied to a large variety of physical systems, such as digital hardware, queuing networks, communication networks, and industrial protection systems, in order to support different tasks, including fault detection, monitoring, and diagnosis. This paper focuses on the model-based diagnosis of a class of distributed discrete-event systems, called active systems. An active system, which is designed to react to possibly harmful external events, is modeled as a network of communicating automata, where each automaton describes the behavior of a system component. Unlike other approaches based on the synchronous composition of automata and on the off-line creation of the model of the entire system, the proposed diagnostic technique deals with asynchronous events and does not need any global diagnoser to be built. Instead, the current approach features a problem-decomposition/solution-composition nature whose core is the on-line progressive reconstruction of the behavior of the active system, guided by the available observations. This incremental technique makes effective the diagnosis of large-scale active systems, for which the one-shot generation of the global model is almost invariably impossible in practice. The diagnostic method encompasses three steps: 1) reconstruction planning; 2) behavior reconstruction; and 3) diagnosis generation.