We address the problem of failure diagnosis in discrete event systems with decentralized information. We propose a coordinated decentralized architecture consisting of two local sites communicating with a coordinator that is responsible for diagnosing the failures occurring in the system. We extend the notion of diagnosability, originally introduced in [1] for centralized systems, to the proposed coordinated decentralized architecture. We specify one protocol that realizes the proposed architecture. We analyze the diagnostic properties of this protocol. The key feature of the proposed protocol is that it achieves the same diagnostic performance as the centralized diagnoser. 1 Introduction Failure detection and isolation is an important task in the automatic control of large complex systems, and consequently, the problem of failure diagnosis has received considerable attention in the literature. Many schemes ranging from fault-tree and analytical redundancy methods to discrete event s...

In this paper we formulate asynchronous diagnosis by means of hidden state history reconstruction, from alarm observations. We follow a so-called true concurrency approach, in which no global state and no global time is available. Instead, we use only local states in combination with a partial order model of time, in which local events are ordered if they are either generated on the same site, or related via some causality relation. Our basic mathematical tool is that of net unfoldings originating from the Petri net research area. This study was motivated by the problem of event correlation in telecommunications network management.

We address the problem of alarm correlation in large distributed systems. The key idea is to make use of the concurrence of events in order to separate and simplify the state estimation in a faulty network. Petri nets and their causality semantics are used to model concurrency. Special partially stochastic Petri nets are developed, that establish some kind of equivalence between concurrence and independence. The diagnosis problem is defined as the computation of the most likely history of the net given a sequence of observed alarms. Solutions are provided in four contexts, with a gradual complexity on the structure of observations.

) Vineet Gupta Radha Jagadeesan Prakash Panangaden y vgupta@mail.arc.nasa.gov radha@cs.luc.edu prakash@cs.mcgill.ca Caelum Research Corporation Dept. of Math. and Computer Sciences School of Computer Science NASA Ames Research Center Loyola University--Lake Shore Campus McGill University Moffett Field CA 94035, USA Chicago IL 60626, USA Montreal, Quebec, Canada Abstract This paper describes a stochastic concurrent constraint language for the description and programming of concurrent probabilistic systems. The language can be viewed both as a calculus for describing and reasoning about stochastic processes and as an executable language for simulating stochastic processes. In this language programs encode probability distributions over (potentially infinite) sets of objects. We illustrate the subtleties that arise from the interaction of constraints, random choice and recursion. We describe operational semantics of these programs (programs are run by sampling random choices), deno...

This paper discusses diagnosis problems in distributed systems within the context of a language-theoretic discrete event formalism. A distributed system is seen as a system with multiple spatially separated sites with each site having a diagnoser that observes some of the events generated by the system and diagnoses the faults associated with the site. We allow the diagnosers to share information by sending messages to each other. The existence and synthesis of diagnosers is investigated. The formulation and results are motivated by the diagnosis of failures in a wireless LAN. 1 Introduction We are interested in understanding the design of diagnostics for distributed systems. This theoretical work is motivated by our experience with the design of distributed diagnostics for coordinating vehicle systems [5, 10] and wireless local area networks [3, 6]. These systems are comprised of spatially separated sites (e.g., vehicles or radios) of semi-autonomous activity. Since these systems op...

We consider a discrete event dynamic system (DEDS) obtained by the parallel composition of several subsystems. Each subsystem can be seen as a standard stochastic DEDS. The composed system is provided with true concurrency semantics that emphasize concurrent behaviors of the subsystems. For these semantics, a trajectory appears as a partial order of events. For simplicity, we focus on the case of a global system composed of only two subsystems. We assume that firings in each subsystem are collected by a local sensor, which yields a sequence of transition labels (or events). The objective is to recover the most likely global trajectory of the system from the two (asynchronous) sequences of observations. This is an almost standard hidden state estimation problem, amenable to the clasical Viterbi algorithm. We propose a solution in which this global trajectory is built recursively by two asynchronously cooperating "players, " each one being in charge of one subsystem. These two players run local Viterbi algorithms based on local states of the subsystems, plus some coordination information. This supervising architecture is particularly suited to large modular systems and is currently being applied to the distributed monitoring (and fault diagnosis) of telecommunication networks.

Abstract—In this paper, we consider the diagnosis of asynchronous discrete event systems. We follow a so-called true concurrency approach, in which no global state and no global time is available. Instead, we use only local states in combination with a partial order model of time. Our basic mathematical tool is that of net unfoldings originating from the Petri net research area. This study was motivated by the problem of event correlation in telecommunications network management. Index Terms—Alarm correlation, asynchronous diagnosis, diagnosis, discrete event systems, Petri nets, unfoldings. I.

In this paper we formulate distributed diagnosis by means of hidden state history reconstruction, from alarm observations. We follow a so-called true concurrency approach, in which no global state and no global time is available. Instead, we use only local states in combination with a partial order model of time, in which local events are ordered if they are either generated on the same site, or related via some causality relation. Our basic mathematical tool is that of net unfoldings originating from the Petri net research area. This study was motivated by the problem of event correlation in telecommunications network management.

We address the problem of model-based fault diagnosis for hybrid numeric/symbolic dynamical systems. The objective is the systematic design of fault diagnosis algorithms, based on fault graphs provided by reliability analysis and on-board sensor data processing. We propose a new approach, based on hmm ideas and Viterbi algorithm. Experimental results of the proposed approach for a simulated mechanical engineering example are reported. Extensions for taking into account a discrete controller and fault concurrence are discussed.

Abstract- We infroduce a modular architecture ’ for diag-nosis in the framework of discrete evenf systems. The ar-chifecrure is best suited for systems wifh multiple inferacf-ing components. We suggest to use local diagnosis at each coniponenf and pmvide conditions on the system sfructure so that the global system behavior can be diagnosed using these local diagnoses. We also discuss a fesf to check the diugnosability of a given system in rhe modular architecture set up.