Selection of Measurement Modality for Magnetic Material Characterization of an Electromagnetic Device Using Stochastic Uncertainty Analysis

IEEE Transactions on Magnetics (Impact Factor: 1.39). 12/2011; 47(11):4564 - 4573. DOI: 10.1109/TMAG.2011.2151870
Source: IEEE Xplore


Magnetic material properties of an electromagnetic device (EMD) can be estimated by solving an inverse problem where electromagnetic or mechanical measurements are adequately interpreted by a numerical forward model. Due to measurement noise and uncertainties in the forward model, errors are made in the reconstruction of the material properties. This paper describes the formulation and implementation of a time-efficient numerical error estimation procedure for predicting the optimal measurement modality that leads to minimal error resolution in magnetic material characterization. We extended the traditional Cramér-Rao bound technique for error estimation due to measurement noise only, with stochastic uncertain geometrical model parameters. Moreover, we applied the method onto the magnetic material characterization of a Switched Reluctance Motor starting from different measurement modalities: mechanical; local and global magnetic measurements. The numerical results show that the local magnetic measurement modality needs to be selected for this test case. Moreover, the proposed methodology is validated numerically by Monte Carlo simulations, and experimentally by solving multiple inverse problems starting from real measurements. The presented numerical procedure is able to determine a priori error estimation, without performing the very time consuming Monte Carlo simulations.

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Available from: Ahmed Abdallh, Apr 22, 2014
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    • "Based on the recovered B–H curve at each inverse problem compared to the original B–H curve, one may calculate the recovery error (RE). Many formulas can be used; however, we calculate RE based on the ratio between the area under the recovered and the original B–H curves, respectively[7]: "
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    ABSTRACT: Magnetic material properties of an electromagnetic device can be recovered by solving an inverse problem where measurements are adequately interpreted by a mathematical forward model. The accuracy of these forward models dramatically affects the accuracy of the material properties recovered by the inverse problem. The more accurate the forward model is, the more accurate recovered data are. However, the more accurate 'fine' models demand a high computational time and memory storage. Alternatively, less accurate 'coarse' models can be used with a demerit of the high expected recovery errors. This paper uses the Bayesian approximation error approach for improving the inverse problem results when coarse models are utilized. The proposed approach adapts the objective function to be minimized with the a priori misfit between fine and coarse forward model responses. In this paper, two different electromagnetic devices, namely a switched reluctance motor and an EI core inductor, are used as case studies. The proposed methodology is validated on both purely numerical and real experimental results. The results show a significant reduction in the recovery error within an acceptable computational time.
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