International audienceThe goal of this paper is to identify the parameter set of a given electrical machine. The identification method is based on data assimilation coupled with FEM model. Data assimilation method is an optimization approach that limits the space of candidate parameter sets by centering it around those of the ideal machine. An application to an electrical machine is presented based on the analysis of flux sensor signals. The methodology is validated by using twin experiments that consider simulated data and tested considering measurements extracted from the ”real” alternator
International audienceThe goal of this paper is to identify rotor inter-turn short-circuits of an alternator. The detection method is based on the analysis of a flux probe signal located in the air gap of the machine. Previous works have shown that pattern recognition can be applied to detect such a fault by using the experimental data as prototypes. A new method is developed here by considering a learning step based on simulation. Therefore the machine is modeled and validated in that purpose. A feature selection is made by considering feature correlation and disparity. Finally, k nearest neighbors is used to classified experimental test data
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