Near-field linear sampling method for axisymmetric eddy current tomography

Haddar, Houssem; Riahi, Mohamed Kamel

doi:10.1088/1361-6420/ac1c50

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…Despite the promising initial results, there is still room for optimization. The linear sampling method proved to be suitable, for example, for solving inverse problems in related research fields [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Magnetic Induction Tomography: Separation of the Ill-Posed and Non-Linear Inverse Problem into a Series of Isolated and Less Demanding Subproblems

Schledewitz

Klein

Rueter

2023

Sensors

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Magnetic induction tomography (MIT) is based on remotely excited eddy currents inside a measurement object. The conductivity distribution shapes the eddies, and their secondary fields are detected and used to reconstruct the conductivities. While the forward problem from given conductivities to detected signals can be unambiguously simulated, the inverse problem from received signals back to searched conductivities is a non-linear ill-posed problem that compromises MIT and results in rather blurry imaging. An MIT inversion is commonly applied over the entire process (i.e., localized conductivities are directly determined from specific signal features), but this involves considerable computation. The present more theoretical work treats the inverse problem as a non-retroactive series of four individual subproblems, each one less difficult by itself. The decoupled tasks yield better insights and control and promote more efficient computation. The overall problem is divided into an ill-posed but linear problem for reconstructing eddy currents from given signals and a nonlinear but benign problem for reconstructing conductivities from given eddies. The separated approach is unsuitable for common and circular MIT designs, as it merely fits the data structure of a recently presented and planar 3D MIT realization for large biomedical phantoms. For this MIT scanner, in discretization, the number of unknown and independent eddy current elements reflects the number of ultimately searched conductivities. For clarity and better representation, representative 2D bodies are used here and measured at the depth of the 3D scanner. The overall difficulty is not substantially smaller or different than for 3D bodies. In summary, the linear problem from signals to eddies dominates the overall MIT performance.

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Section: Discussionmentioning

confidence: 99%

Magnetic Induction Tomography: Separation of the Ill-Posed and Non-Linear Inverse Problem into a Series of Isolated and Less Demanding Subproblems

Schledewitz

Klein

Rueter

2023

Sensors

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“…Numerous techniques have been developed to address the issue of damage detection in FGM structures. One approach is to use non-destructive testing (NDT) methods, such as ultrasonic testing [8][9][10], eddy current tomography [11][12][13][14][15], and X-ray computed tomography (CT) [16]. These methods can detect damage by analyzing changes in the properties of the material, such as changes in acoustic properties or changes in magnetic fields.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach-Based Sparsity for Damage Localization in Functionally Graded Material

et al. 2023

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Model-based approaches have been widely employed in damage detection and localization studies. However, alternative techniques, such as built-in online detection methods, hold promise for future advancements in structural health monitoring technologies. In this research paper, we present a dynamic algorithm specifically designed for accurate damage localization in functionally graded plates. The suggested method involves the creation of a grid matrix that captures the dynamic response of the structure over time. Subsequently, an optimization process is performed using a linear equation that incorporates the information contained within the grid, enabling the precise localization of damage. To address the inherent sparsity of the localization nature, we utilize the FISTA (fast iterative shrinkage-thresholding algorithm) as a problem solver. The effectiveness of our approach is evaluated through experimental tests on a functionally graded plate with clamped free boundary conditions. Multiple damage scenarios are investigated, including cases with damage signals on and off-the-grid. The results demonstrate that our proposed approach is capable of accurately predicting the position of damage, indicating its suitability for application in low-size data systems.

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“…At present, the most common solution to achieve a wide range of high-precision inspections is to work with a mechanical device to perform pointby-point sweeps. Haddar uses eddy current technology to sweep for cracks [4] and deposits [5] in pipelines through a linear sampling method. Gong et al designed an on-board image acquisition system that utilizes multiple line-scan cameras to capture full cross-section images of the tunnel surface for detecting tunnel surface cracks.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Tree–Complex Wavelet Transform-Based Infrared and Visible Image Fusion Technique and Its Application in Tunnel Crack Detection

Wang,

Chen

2023

Applied Sciences

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Computer vision methods have been widely used in recent years for the detection of structural cracks. To address the issues of poor image quality and the inadequate performance of semantic segmentation networks under low-light conditions in tunnels, in this paper, infrared images are used, and a preprocessing method based on image fusion technology is developed. First, the DAISY descriptor and the perspective transform are applied for image alignment. Then, the source image is decomposed into high- and low-frequency components of different scales and directions using DT-CWT, and high- and low-frequency subband fusion rules are designed according to the characteristics of infrared and visible images. Finally, a fused image is reconstructed from the processed coefficients, and the fusion results are evaluated using the improved semantic segmentation network. The results show that using the proposed fusion method to preprocess images leads to a low false alarm rate and low missed detection rate in comparison to those using the source image directly or using the classical fusion algorithm.

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Near-field linear sampling method for axisymmetric eddy current tomography

Cited by 8 publications

References 21 publications

Magnetic Induction Tomography: Separation of the Ill-Posed and Non-Linear Inverse Problem into a Series of Isolated and Less Demanding Subproblems

Magnetic Induction Tomography: Separation of the Ill-Posed and Non-Linear Inverse Problem into a Series of Isolated and Less Demanding Subproblems

Novel Approach-Based Sparsity for Damage Localization in Functionally Graded Material

A Dual-Tree–Complex Wavelet Transform-Based Infrared and Visible Image Fusion Technique and Its Application in Tunnel Crack Detection

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