Contribution au diagnostic et pronostic des systèmes à évènements discrets temporisés par réseaux de Petri stochastiques

Abstract : Due to the increasing complexity of systems and to the limitation of sensors number, developing monitoring methods is a main issue. This PhD thesis deals with the fault diagnosis and prognosis of timed Discrete Event Systems (DES). For that purpose, partially observed stochastic Petri nets are used to model the system. The model represents both the nominal and faulty behaviors of the system and characterizes the uncertainty on the occurrence of events as random variables with exponential distributions. It also considers partial measurements of both markings and events to represent the sensors of the system. Our main contribution is to exploit the timed information, namely the dates of the measurements for the fault diagnosis and prognosis of DES. From the proposed model and collected measurements, the behaviors of the system that are consistent with those measurements are obtained. Based on the event dates, our approach consists in evaluating the probabilities of the consistent behaviors. The probability of faults occurrences is obtained as a consequence. When a fault is detected, a method to estimate its occurrence date is proposed. From the probability of the consistent trajectories, a state estimation is deduced. The future possible behaviors of the system, from the current state, are considered in order to achieve fault prediction. This prognosis result is extended to estimate the remaining useful life as a time interval. Finally, a case study representing a sorting system is proposed to show the applicability of the developed methods.
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Submitted on : Thursday, March 1, 2018 - 9:36:08 AM
Last modification on : Tuesday, February 5, 2019 - 11:41:18 AM
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  • HAL Id : tel-01720312, version 1


Rabah Ammour. Contribution au diagnostic et pronostic des systèmes à évènements discrets temporisés par réseaux de Petri stochastiques. Automatique. Normandie Université, 2017. Français. ⟨NNT : 2017NORMLH21⟩. ⟨tel-01720312⟩



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