Hardware and software design for an electromagnetic induction tomography (EMT) system for high contrast metal process applications

Ma, Xiandong; Peyton, A. J.; Higson, S. R.; Lyons, Anthony R. A.; Dickinson, S. J.

doi:10.1088/0957-0233/17/1/018

Cited by 92 publications

(65 citation statements)

References 14 publications

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“…Owing to the fact that electrical tomography techniques generally have a low spatial resolution compared with hard-field tomography techniques, there has been much research on new reconstruction algorithm developments [62][63][64][65]. The common goal of these new algorithms is to improve electrical tomography's ill-posed nature, aiming to achieve a better spatial resolution and image accuracy.…”

Section: Reconstruction Algorithmsmentioning

confidence: 99%

Super-sensing technology: industrial applications and future challenges of electrical tomography

Wei

Qiu

Primrose

2016

Phil. Trans. R. Soc. A.

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One contribution of 10 to a theme issue 'Super-sensing through industrial process tomography' . Electrical tomography is a relatively new imaging technique that can image the distribution of the passive electrical properties of an object. Since electrical tomography technology was proposed in the 1980s, the technique has evolved rapidly because of its low cost, easy scale-up and non-invasive features. The technique itself can be sensitive to all passive electrical properties, such as conductivity, permittivity and permeability. Hence, it has a huge potential to be applied in many applications. Owing to its illposed nature and low image resolution, electrical tomography attracts more attention in industrial fields than biomedical fields. In the past decades, there have been many research developments and industrial implementations of electrical tomography; nevertheless, the awareness of this technology in industrial sectors is still one of the biggest limitations for technology implementation. In this paper, the authors have summarized several representative applications that use electrical tomography. Some of the current tomography research activities will also be discussed.This article is part of the themed issue 'Supersensing through industrial process tomography'.

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Section: Reconstruction Algorithmsmentioning

confidence: 99%

Super-sensing technology: industrial applications and future challenges of electrical tomography

Wei

Qiu

Primrose

2016

Phil. Trans. R. Soc. A.

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“…Because of its contactless feature, the entire data collection process becomes non-invasive, which makes it suitable in many applications in both industrial fields (non-destructive pipeline imaging, metal production monitoring and material inspection [2][3][4]) and biomedical fields [5,6]. For a typical MIT system, coils are used as the transmitters and receivers based on the mutual inductance theory.…”

Section: Magnetic Induction Tomography (Mit) Is An Electromagnetic Immentioning

confidence: 99%

“…In the literature, MIT have been extensively studied in 2D and most of the systems are still producing low resolution 2D imaging result [3,4,[7][8][9][10], which focuses on the cross sectional distribution and assumes there is no axial (z-axis) conductivity variation. For some applications where the axial conductivity change is significant, this assumption is unrealistic and the 2D imaging is no longer applicable.…”

Section: Magnetic Induction Tomography (Mit) Is An Electromagnetic Immentioning

confidence: 99%

Four Dimensional Reconstruction Using Magnetic Induction Tomography: Experimental Study

Wei¹,

Soleimani²

2012

PIER

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Abstract-Magnetic Induction Tomography (MIT) is a relatively new and emerging type of tomography techniques that is able to map the distribution of all three passive electrical properties (PEPs). Its non-invasive and contactless features make it an attractive technique for many applications compared to the traditional contact electrode based electrical impedance tomography. Recently, MIT has become a promising monitoring technique in industrial process tomography, and the area of the research interest has moved from 2D to 3D because of the volumetric nature of electromagnetic field. Three dimensional MIT images provide more information on the conductivity distribution, especially in the axial direction. However, it has been reported that the reconstructed 3D images can be distorted when the imaging object is located at a less sensitive region. Although this distortion can be compensated by adjusting the regularisation criteria, this is not practical in real life applications as the prior information about the object's location is often unavailable. This paper presents a memory efficient 4D MIT algorithm which can maintain the image quality under the same regularisation circumstances. Instead of solving each set of measurement individually, the 4D algorithm takes advantage of the correlations between the image and its neighboring data frames to reconstruct 4D of conductivity movements. The 4D algorithm improves the image qualities by increasing the temporal resolution. It also overcomes some sensitivity issues of 3D MIT algorithms and can provide a more stable result in terms of the size consistency of the reconstructed image. Several experimental results using real laboratory data are presented for validating the proposed algorithms.

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“…To date, the major progress reported in MIT image reconstruction mainly involved the application of linear algorithms including single step schemes such as linear back-projection [3,4], Tikhonov regularization [5][6][7][8][9][10], truncated singular value decomposition (TSVD) [9], and iterative schemes like simultaneous iterative reconstruction technique (SIRT) [9], Landweber method [11] and the conjugate gradients least squares (CGLS) method [12]. Single step reconstruction methods are fast but they can only produce qualitative images.…”

Section: Introductionmentioning

confidence: 99%

Absolute Imaging of Low Conductivity Material Distributions Using Nonlinear Reconstruction Methods in Magnetic Induction Tomography

Dekdouk¹,

Ktistis²,

Armitage³

et al. 2016

PIER

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Abstract-Magnetic Induction Tomography (MIT) is a newly developing technique of electrical tomography that in principle is able to map the electrical conductivity distribution in the volume of objects. The image reconstruction problem in MIT is similar to electrical impedance tomography (EIT) in the sense that both seek to recover the conductivity map, but differ remarkably in the fact that data being inverted in MIT is derived from induction theory and related sources of noise are different. Progress in MIT image reconstruction is still limited, and so far mainly linear algorithms have been implemented. In difference imaging, step inversion was demonstrated for recovering perturbations within conductive media, but at the cost of producing qualitative images, whilst in absolute imaging, linear iterative algorithms have mostly been employed but mainly offering encouraging results for imaging isolated high conductive targets.In this paper, we investigate the possibility of absolute imaging in 3D MIT of a target within conductive medium for low conductivity application (σ < 5 Sm −1 ). For this class of problems, the MIT image reconstruction exhibits non-linearity and ill-posedness that cannot be treated with linear algorithms. We propose to implement for the first time in MIT two effective inversion methods known in non-linear optimization as Levenberg Marquardt (LMM) and Powell's Dog Leg (PDLM) methods. These methods employ damping and trust region techniques for controlling convergence and improving minimization of the objective function. An adaptive version of Gauss Newton is also presented (AGNM), which implements a damping mechanism to the regularization parameter. Here, the level of penalty is varied during the iterative process. As a comparison between the methods, different criteria are examined from image reconstructions using the LMM, PDLM and AGNM. For test examples, volumetric image reconstruction of a perturbation within homogeneous cylindrical background is considered. For inversion, an independent finite element FEM software package Maxwell by Ansys is employed to generate simulated data using a model of a 16 channel MIT system. Numerical results are employed to show different performance characteristics between the methods based on convergence, stability and sensitivity to the choice of the regularization parameter. To demonstrate the effect of scalability of absolute imaging in MIT for more realistic problems, a human head model with an internal anomaly is used to produce reconstructions for different finer resolutions. AGNM is adopted here and employs the Krylov subspace method to replace the computationally demanding direct inversion of the regularized Hessian.

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Hardware and software design for an electromagnetic induction tomography (EMT) system for high contrast metal process applications

Cited by 92 publications

References 14 publications

Super-sensing technology: industrial applications and future challenges of electrical tomography

Super-sensing technology: industrial applications and future challenges of electrical tomography

Four Dimensional Reconstruction Using Magnetic Induction Tomography: Experimental Study

Absolute Imaging of Low Conductivity Material Distributions Using Nonlinear Reconstruction Methods in Magnetic Induction Tomography

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