Simulation and Analysis of 3-D Magnetic Flux Leakage

Dutta, Sushant M.; Ghorbel, Fathi H.; Stanley, Roderic K.

doi:10.1109/tmag.2008.2011896

Cited by 86 publications

(33 citation statements)

References 9 publications

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“…The model was able to predict all the orthogonal components of 3D-MFL fields of a surfacebreaking defect [9]. They also proposed that the use of the tangential (circumferential) component of MFL signal would be useful for determination of location of defects with respect to the sensor [10]. Magnetic leakage profiles of the interacting and non-interacting defects are analyzed and we conclude that the center to center distance between two axially aligned defects is greater than the four times of the defect radius is considered as non-interacting defect.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Model for Prediction of Magnetic Flux Leakage from Surface Defects in Ferromagnetic Tubes

Suresh

Abudhahir

2016

Measurement Science Review

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In this paper, an analytical model is proposed to predict magnetic flux leakage (MFL) signals from the surface defects in ferromagnetic tubes. The analytical expression consists of elliptic integrals of first kind based on the magnetic dipole model. The radial (B z ) component of leakage fields is computed from the cylindrical holes in ferromagnetic tubes. The effectiveness of the model has been studied by analyzing MFL signals as a function of the defect parameters and lift-off. The model predicted results are verified with experimental results and a good agreement is observed between the analytical and the experimental results. This analytical expression could be used for quick prediction of MFL signals and also input data for defect reconstructions in inverse MFL problem.

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Section: Introductionmentioning

confidence: 99%

An Analytical Model for Prediction of Magnetic Flux Leakage from Surface Defects in Ferromagnetic Tubes

Suresh

Abudhahir

2016

Measurement Science Review

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“…Two methods of the MFL testing are practiced vis-à-vis forward and inverse problem method. Calculation of the defects from the known defect profile is referred as forward problem [1]. Analytical and finite model based approaches afford solutions to the forward problem.…”

Section: Introductionmentioning

confidence: 99%

Dipole Model to Predict the Rectangular Defect on Ferromagnetic Pipe

Suresh¹,

Abudhair²

2016

Journal of Magnetics

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Dipole model based analytical expression is proposed to estimate the length and depth of the rectangular defect on ferromagnetic pipe. Among the three leakage profiles of Magnetic Flux Leakage (MFL), radial and axial leakage profiles are considered in this work. Permeability variation of the specimen is ignored by considering the flux density as close to saturation level of the inspected specimen. Comparing the profile of both the components, radial leakage profile furnishes the better estimation of defect parameter. This is evident from the results of error percentage of length and depth of the defect. Normalized pattern of the proposed analytical model radial leakage profile is good agreement with the experimentally obtained profile support the performance of proposed expression.

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“…A case in point is the solution of Maxwell's equations in magnetic flux leakage (MFL) which includes the forward model and inverse problem [1]. The approaches for solving the forward model involve dipole models [2]- [4] and numerical methods [5]- [9]. The analytical solution method is applied in certain simple regularized defects and its validity rests with corresponding assumptions.…”

Section: Introductionmentioning

confidence: 99%

Finite-Element Neural Network-Based Solving 3-D Differential Equations in MFL

Wang

et al. 2012

IEEE Trans. Magn.

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The solution of a differential equation contains the forward model and the inverse problem. The finite element method (FEM) and the iterative approach based on FEM are extensively used to solve varied differential equations. Although FEM could obtain an accurate solution, the shortcoming of the approach is the high computational costs. This paper proposes an improved finite-element neural network (FENN) embedding a FEM in a neural network structure for solving the forward model while a conjugate gradient (CG) method is employed as the learning algorithm. Taking the 3-D magnetic field analysis in magnetic flux leakage (MFL) testing as an example, the comparison between CG algorithm and the gradient descent (GD) algorithm is presented. The vector plot of magnetic field intensity is obtained, and the vertical components of magnetic flux density are respectively analyzed. The iterative approach based on FENN and parallel radial wavelet basis function neural network is also adopted to solve the inverse problem. This approach iteratively adjusts weights of the inverse network to minimize the error between the measured and predicted values of MFL signals. The forward and inverse results indicate that FENN and the iterative approach are feasible methods with rapidness, accuracy and stability associated with 3-D different equations in MFL testing.Index Terms-Conjugate gradient (CG) algorithm, finite-element method (FEM), finite-element neural network (FENN), forward model, inverse problem, magnetic flux leakage (MFL) testing.

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Simulation and Analysis of 3-D Magnetic Flux Leakage

Cited by 86 publications

References 9 publications

An Analytical Model for Prediction of Magnetic Flux Leakage from Surface Defects in Ferromagnetic Tubes

An Analytical Model for Prediction of Magnetic Flux Leakage from Surface Defects in Ferromagnetic Tubes

Dipole Model to Predict the Rectangular Defect on Ferromagnetic Pipe

Finite-Element Neural Network-Based Solving 3-D Differential Equations in MFL

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