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.

For distributed systems, i.e. large networked complex systems, there is a drastic difference between a local view and knowledge of the system, and its global view. Distributed systems have local state and time, but do not possess global state and time in the usual sense. In this paper, motivated by the monitoring of distributed systems and in particular of telecommunications networks, we develop Markov nets as an extension of Markov chains and hidden Markov models (Hmm) for distributed and concurrent systems. By a concurrent system, we mean a system in which components may evolve independently, with sparse synchronizations. We follow a so-called true concurrency approach, in which neither global state nor global linear time are available. Instead, we use only local states in combination with a partial order model of time. Our basic mathematical tool is that of Petri net unfoldings. Keywords : discrete event systems, stochastic Petri nets, unfoldings. 1 Motivations Distributed network...

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. This paper reports on an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA’s Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion. 1

Archives des publications du CNRC (NPArC)

On-line reconfiguration is the ability to rearrange dynamically the elements of a system to accommodate failure events or new requirements. Due to the modular representation, decentralized discreteevent approach, recently proposed for the diagnosis of systems, is particularly well suited to the diagnosis of reconfigurable systems. The contribution of this article is to extend our decentralized approach to reconfigurable discrete-event systems. A first step in this direction is to extend the way a decentralized system is modelled. The idea consists in modelling separately the behavior of the components and the system topology. A second step is to formally define what is a reconfiguration. A property of reconfiguration, that we call safety, is identified to be important. When satisfied, we show that our decentralized diagnosis approach can easily be extended to reconfigurable systems.

Decentralized failure diagnosis problems arise in communication networks. Network operators require automated tools to isolate failures that need immediate attention. Failure notification is often accompanied with local timing information, although the presence of a global clock is not guaranteed. A timed discrete-event system for decentralized failure diagnosis is proposed. Additionally, the model allows asynchronous communication between decentralized diagnosers. The problem is treated whether an observor map of a diagnoser factors over a timed discrete-event system with a finite state set.

Abstract: this document presents a generic model capturing the essential structural and behavioral characteristics of network components in the light of fault management. The generic model is described by means of UML notations, namely class diagrams, sequence diagrams and instance diagrams. The model covers both circuit-based and packet-based networks. Technologyspecific models must be derived from this model in order to be used by a fault diagnosis module. We also present how we compiled the model to obtain rules for a Viterbi distributed diagnoser, which has been demonstrated on an SDH platform. 1

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.

The formalism of chronicles has been proposed to monitor and diagnose dynamic physical systems. Even if efficient chronicle recognition algorithms exist, it is now well-known that distributed approaches are better suited to monitor actual systems. In this article, we adapt the chronicle-based approach to a distributed context and illustrate this work on the monitoring of software components.