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Vers l'intégration diagnostic/pronostic pour la maintenance des systèmes complexes

Abstract : The maintenance efficiency of industrial systems is an important economical and business issue. The main difficulties come from the choice of maintenance actions. A wrong choice can lead to maintenance costs that are not acceptable. A generic supervision architecture is proposed in this thesis and supports the decision of maintenance actions for a complex system. This architecture integrates some diagnostic and prognostic capabilities to determine the current and future state of the system. The diagnostic function aims at identifying faulty components that may cause system failures. The prognostic function estimates the remaining time until the next system failure. A formal generic modeling framework for a complex system is presented and encapsulates the knowledge that is used by the diagnostic and prognostic functions. An original coupling of diagnosis and prognosis is then characterised. A generic and adaptive prognostic function is defined with a Weibull model in order to evaluate the system remaining useful life by the use of probability. Performance criteria for the proposed supervision architecture are characterised. They rely on diagnostic and prognostic properties. A methodology that provides a feedback to the system design is proposed to improve diagnostic function efficiency by guaranteeing some diagnosability objectives. This work is partly supported by the ARCHISTIC project in collaboration with Airbus France and National Engineering School of Tarbes, France.
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Submitted on : Wednesday, January 27, 2010 - 2:04:53 PM
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  • HAL Id : tel-00450835, version 1


Pauline Ribot. Vers l'intégration diagnostic/pronostic pour la maintenance des systèmes complexes. Automatique / Robotique. Université Paul Sabatier - Toulouse III, 2009. Français. ⟨tel-00450835⟩



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