2D defect reconstruction from MFL signals by a genetic optimization algorithm

Han, Wenhua; Que, Peiwen

doi:10.1007/s11181-006-0037-0

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…... (8) where each element represents a pixel in the function matrix and the element value represents the defect depth. The measured MFL is the normal component of B.…”

Section: The Expression For 3d Defect Profile and Mfl Signalsmentioning

confidence: 99%

“…These models can provide accurate results, but the related computational cost is high. The third group of forward MFL models is based on artificial neural networks [8][9] . The inverse problems are defect profile reconstruction, ie the determination of flaw parameters such as the flaw length, depth and shape (profile) from the measured values of the MFL signal [10] .…”

Section: Introductionmentioning

confidence: 99%

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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

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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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“…... (8) where each element represents a pixel in the function matrix and the element value represents the defect depth. The measured MFL is the normal component of B.…”

Section: The Expression For 3d Defect Profile and Mfl Signalsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…These models can provide accurate results, but the related computational cost is high [10] . The third group of forward MFL models is based on artificial neural networks [11][12][13] . Neural networks are learning machines that can learn any arbitrary mapping between input and output.…”

Section: Introductionmentioning

confidence: 99%

A magnetic flux leakage analysis model based on finite element neural network

Yuan¹,

Wang²,

Ji³

et al. 2011

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MFLThe major drawback of the finite element method (FEM), which is commonly used in magnetic leakage field testing, is the high cost of calculation. In this paper, a finite element neural network (FENN), which embeds a finite element model in a neural network structure, is adopted that enables a faster and more accurate solution for the forward model compared with FEM. The second-order Newton method is introduced as a learning algorithm. The FENN model for magnetic flux leakage (MFL) testing is established and comparisons between the gradient decent algorithm and the second-order Newton method, under the circumstances of various defects, are presented. The vector plot of magnetic field intensity and the vertical components of magnetic flux density are analysed. The relevant results indicate that the second-order Newton method-based FENN can resolve the MFL forward model in parallel, which has the advantages of rapidness and stability, and it is a valid and feasible calculation method. Keywords: magnetic flux leakage, electromagnetic calculation, finite element method, finite element neural network.

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