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Contribution au diagnostic et a l'analyse de défauts d'une machine synchrone à aimants permanents.

Abstract : The advent of new magnetic materials and recent advances in power electronics have played a major role in the progress of hybrid electric vehicles. Nowadays, permanent magnet synchronous machines (PMSM) thanks to their performances, especially their energy efficiency, are considered as ideal candidates for the traction chains of hybrid and electric vehicles. However, due to material aging, manufacturing defects or severe operating conditions, different types of faults are capable to occur in the machine components, its control or measuring devices. In order to ensure safety, reliability and availability, the integration of a fault diagnosis and condition monitoring approach in the automotive electrical powertrain system is becoming more and more important. In this context, the aim of the thesis is to contribute to the diagnosis and characterization of faults in the PMSM based on a vibration analysis. First, analytical modeling approaches for the PMSM and inter-turn short-circuits, eccentricity and rotor demagnetization faults will be proposed. The major interest of such models, in a diagnosis context, is to study the behavior of the machine in the presence of studied faults in order to deduce the most suitable detection methods. In addition, numerical models will be developed in order to validate the analytical magnetic and mechanical parts of the machine as well as the demagnetization fault. In the phase of fault impact analysis, we will focus on the cases of rotor eccentricity and demagnetization. The fault indicators will be extracted from the vibratory signal representations in time and space domains and their Fourier transforms, in the cases of single faults and the cases of two combined faults. For single fault cases, two diagnosis approaches will be proposed: the first uses the principle of statistical tests and fault signature tables, inspired by model-based diagnosis methods, while the second relies on a set of three neural networks, such as each one is with a single input and a single output and dedicated to isolate one type of fault. Finally, the performance of these two approaches, in terms of robustness and adaptability, will be compared for the same training and test sets.
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Submitted on : Monday, February 5, 2018 - 2:06:07 PM
Last modification on : Thursday, November 28, 2019 - 3:35:31 AM
Long-term archiving on: : Saturday, May 5, 2018 - 8:31:08 AM


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  • HAL Id : tel-01700940, version 1


Kawthar Alameh. Contribution au diagnostic et a l'analyse de défauts d'une machine synchrone à aimants permanents.. Automatique. Normandie Université, 2017. Français. ⟨NNT : 2017NORMR072⟩. ⟨tel-01700940⟩



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