Calibration of a Finite Element Forward Model in Eddy Current Inspection

We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative...

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Veröffentlicht in:IEEE sensors journal 2022-06, Vol.22 (11), p.10699-10707
Hauptverfasser: Hampton, Joel, Tesfalem, Henok, Dorn, Oliver, Fletcher, Adam, Peyton, Anthony, Brown, Matthew
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container_end_page 10707
container_issue 11
container_start_page 10699
container_title IEEE sensors journal
container_volume 22
creator Hampton, Joel
Tesfalem, Henok
Dorn, Oliver
Fletcher, Adam
Peyton, Anthony
Brown, Matthew
description We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative solver. However, the subject of calibration of the model has not received much attention in the literature to date. We consider a multi-frequency, eddy current depth profiling application, which is important for non-destructive testing in the nuclear industry. In the optimisation algorithm, we use a Levenberg-Marquardt algorithm and a bisection search to ensure constraints are satisfied, coupled with a truncated Gradient and Hessian method. We calibrate two types of finite element models, one using a filament representation for the coils and the other using a full 3D approach. The results show the feasibility of using the constrained non-linear optimisation algorithm for tuning the finite element model parameters. The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy.
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The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. 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The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2022.3167253</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-5740-348X</orcidid><orcidid>https://orcid.org/0000-0001-8748-8253</orcidid><orcidid>https://orcid.org/0000-0002-2285-2090</orcidid><orcidid>https://orcid.org/0000-0003-4741-238X</orcidid><orcidid>https://orcid.org/0000-0002-1529-7295</orcidid><oa>free_for_read</oa></addata></record>
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subjects Algorithms
Calibration
Coils
Conductivity
constrained optimization
Constraints
Depth profiling
Eddy current testing
Eddy currents
Finite element analysis
Finite element method
finite element model
Inductance
inductance spectroscopy
inverse problem
Iterative methods
Mathematical models
Neural networks
Nondestructive testing
Optimization
Parameters
Signal to noise ratio
Three dimensional models
Tuning
title Calibration of a Finite Element Forward Model in Eddy Current Inspection
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