@inproceedings{1fd6b27abb2e4f61b6f797a20e5e580f,
title = "MFL signal inversion using the finite element network",
abstract = "Iterative algorithms that incorporate a numerical forward model have been typically used to solve the problem of flaw profile estimation from measured magnetic flux leakage (MFL) data. These approaches use the forward model to determine the measurement signal for a given defect profile, and obtain the desired profile by iteratively minimizing a cost function. The use of numerical models is computationally expensive, and alternative forward models are needed. This paper presents a finite element neural network (FEN) obtained by embedding a finite element model in a parallel neural network architecture that enables fast and accurate solution of the forward problem. Previous results have indicated that the FEN performance as a forward model is comparable to that of the conventional finite element method. In this paper, we investigate the applicability of the FEN to determining flaw profiles from MFL data in pipeline inspection and present results on synthetic MFL data.",
keywords = "Finite element models, Inverse problems, MFL, Neural networks",
author = "Pradeep Ramuhalli",
year = "2008",
doi = "10.1063/1.2902722",
language = "English",
isbn = "9780735404946",
series = "AIP Conference Proceedings",
pages = "633--640",
booktitle = "Review of Progress in QuantitativeNondestructive Evaluation",
note = "34th Annual Review of Progress in Quantitative Nondestructive Evaluation ; Conference date: 22-07-2007 Through 27-07-2007",
}