Local area magnetization and inspection method for aerial pipelines

Cheng, Shun‐Feng; Wu, Xiaowen; Kang, Yihua

doi:10.1016/j.ndteint.2004.12.008

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…Each output neuron is also a summation unit followed by a linear activation function, and the output of each neuron is equal to (9) Finally, Dirichlet boundary conditions are applied. The corresponding variables in FENN are modified as (7). The FENN does not require any training, since most of its weights can be computed in advance and stored.…”

Section: B Fenn Model For 3-d Equations In Mflmentioning

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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Section: B Fenn Model For 3-d Equations In Mflmentioning

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 magnetic flux leakage (MFL) technique is one of the nondestructive test (NDT) methods employed for detection of defects in ferromagnetic structures like pipelines, rail tracks, and bridges [1][2][3][4]. We restrict our studies to the signals that are obtained from an intelligent pipeline inspection gauge used for inspection of gas and oil pipelines.…”

Section: Introductionmentioning

confidence: 99%

Extracting the region of interest from MFL signals

Sadr¹,

Okhovat²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we study the magnetic flux leakage (MFL) signals for detection of defects in ferromagnetic materials. MFL signals consist of a background that is not constant and is combined with noise. Since there are slight variations because of noise, any large distortion shows a defect. Here the estimation of the background and then the determination of a threshold to distinguish defects from noise have been used for locating defects. In this method, precise evaluation of these two parameters has a vital role on the defect detection. The concept of histograms has been employed for eliminating the effect of defects in computing background signal. Results show that this algorithm is fast enough and yields detection of more defects that have lower amplitude. In the next step, an appropriate value for the threshold is determined by considering a trade-off between defect detection rate and noise separation rate.

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“…It can only be applied in certain simple regularised defects and its validity rests with corresponding assumptions. The second group is based on numerical calculations [4][5][6][7] . The finite element method (FEM) is commonly used in the forward numerical analysis.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of 3D defect profiles from the MFLT signals by using a radial wavelet basis function neural network iterative model

Xu¹,

Wang²,

Zuo³

et al. 2012

insight

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To achieve multi-resolution approximation of 3D defect profile reconstruction from magnetic flux leakage (MFL) signals, a radial wavelet basis function neural network iterative model, which contains a forward model and an inverse model based on a parallel radial wavelet basis function neural network (PRWBFNN), is proposed. The forward model in the loop is to determine the MFL signals for a given set of flaw parameters, and the inverse model is used to predict the profile given the measured value of the MFL signals and acts to constrain the solution space. This approach iteratively adjusts the weights of the inverse network to minimise the error between the measured and predicted values of the MFL signals. The reconstruction results of different defects indicate that significant advantages over other neural network-based defect characterisation schemes could be obtained.

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Local area magnetization and inspection method for aerial pipelines

Cited by 16 publications

References 2 publications

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

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

Extracting the region of interest from MFL signals

Reconstruction of 3D defect profiles from the MFLT signals by using a radial wavelet basis function neural network iterative model

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