Modular diagnosability applied to on line Diagnosis of Digital Embedded System

Today, embedded systems are increasingly used to control complex systems. In this thesis, we are interested in critical embedded systems used for the control of transport systems such as railway systems. The aim of this work is to enable the design of fault-tolerant systems for the control of transport systems. We propose a new timed embedded systems modeling approach to diagnose their faults. It is based on decomposition of the system and structural extension of diagnosability context of modular timed systems. In DES, there are two basic approaches for diagnosis: diagnoser based approach and chronicles (Causal Temporal Signature (CTS)) based approach. The major limitation of diagnoser approaches rely in the management of the combinatorial explosion related to the formalism of automata. In this work, our main lock is to combat this limit. We propose new engineering models based method for the diagnosis of critical embedded systems. On the other hand, the major limitation of chronicles approach is first to be able to guaranty the consistency of a database. A second level of difficulty is in interpreting some sequences of events at the input of the diagnostic system under the hypothesis of multiple failures. In this work, we propose two different methods to verify the consistency of a set of CTS and we propose an interpretation algorithm based on a concept of worlds which guarantees the correct diagnosis

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Source https://theses.hal.science/tel-00861200
Author Saddem, Ramla
Maintainer CCSD
Last Updated May 9, 2026, 18:39 (UTC)
Created May 9, 2026, 18:39 (UTC)
Identifier NNT: 2012ECLI0031
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS) ; Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)
creator Saddem, Ramla
date 2012-12-10T00:00:00
harvest_object_id bdbf5b7d-43c0-44bf-9a51-2f0e8c72e874
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
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