Direct computation of static difference magnetic field in nonlinear magnetic materials and application to shape reconstruction of damaged areas in aging materials

Author

Cranganu-Cretu, Bogdan ; Hantila, Florea Ioan ; Preda, Gabriel ; Chen, Zhenmao ; Miya, Kenzo

Author_Institution

Int. Inst. of Universality, Tokyo, Japan

Volume

38

Issue

2

fYear

2002

fDate

3/1/2002 12:00:00 AM

Firstpage

1073

Lastpage

1076

Abstract

In order to accurately compute the magnetic field variation due to changes in material properties (e.g. material aging), we propose a technique that computes the difference magnetic field directly. Nonlinear materials are analyzed by means of the polarization technique. The linear field problem is solved using a fast Green function approach. A two-dimensional formulation is validated upon comparison with measurement data. Then, it is used as fast forward solver for a neural network approach to the inverse problem of reconstructing the shape of the aged material area in a plate. Finally, results of reconstruction, based on 500 case database simulations of a yoke and plate geometry, are presented, indicating the good quality of the reconstruction

Keywords

Green´s function methods; ageing; eddy current testing; electromagnetic wave polarisation; magnetic fields; magnetic materials; magnetostatics; neural nets; aged material area; aging materials; damaged areas; database simulations; difference magnetic field; fast Green function approach; fast forward solver; ferromagnetic materials; inverse problem; linear field problem; magnetic field variation; magnetic flux leakage testing; material aging; material properties; measurement data; neural network approach; nondestructive testing; nonlinear magnetic materials; nonlinear magnetics; nonlinear materials; polarization technique; reconstruction quality; shape reconstruction; static difference magnetic field; yoke-and-plate geometry; Aging; Green function; Inverse problems; Magnetic analysis; Magnetic field measurement; Magnetic fields; Magnetic materials; Material properties; Neural networks; Polarization;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.996275

Filename

996275