Identification of Multiple Cracks from Eddy-Current Testing Signals With Noise Sources by Image Processing and Inverse Analysis

Nagaya, Yoshiaki; Takagi, Toshiyuki; Uchimoto, Tetsuya; Huang, Haoyu

doi:10.1109/tmag.2004.825322

Cited by 22 publications

(4 citation statements)

References 3 publications

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“…Additionally, multiple pancake probes can be fabricated to compose an array with a flexible arrangement of coils and different operation modes [7][8][9]. Furthermore, cracks can be quantitatively evaluated using the arrayed probe [10].…”

Section: Introductionmentioning

confidence: 99%

Mechanism study for directivity of TR probe when applying Eddy current testing to ferro-magnetic structural materials

Zhang

Uchimoto

Takagi

et al. 2021

NDT & E International

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Section: Introductionmentioning

confidence: 99%

Mechanism study for directivity of TR probe when applying Eddy current testing to ferro-magnetic structural materials

Zhang

Uchimoto

Takagi

et al. 2021

NDT & E International

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“…In the NDT and evaluation community, several methods for solving the inverse problem by using the detection step have been applied. In Nagaya et al (2004), the authors use image processing and genetics algorithms for identifying the number and positions of cracks and reconstructing crack shapes. A support vector machine-based algorithm is used for defect detection, location and characterization (Bernieri et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Defect localization and characterization in eddy current nondestructive testing by change detection and global optimization

Kermadi

Moussaoui

Brahimi

et al. 2018

COMPEL

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PurposeThis paper aims to present a data-processing methodology combining kernel change detection (KCD) and efficient global optimization algorithms for solving inverse problem in eddy current non-destructive testing. The main purpose is to reduce the computation cost of eddy current data inversion, which is essentially because of the heavy forward modelling with finite element method and the non-linearity of the parameter estimation problem. Design/methodology/approachThe KCD algorithm is adapted and applied to detect damaged parts in an inspected conductive tube using probe impedance signal. The localization step allows in reducing the number of measurement data that will be processed for estimating the flaw characteristics using a global optimization algorithm (efficient global optimization). Actually, the minimized objective function is calculated from data related to defect detection indexes provided by KCD. FindingsSimulation results show the efficiency of the proposed methodology in terms of defect detection and localization; a significant reduction of computing time is obtained in the step of defect characterization. Originality/valueThis study is the first of its kind that combines a change detection method (KCD) with a global optimization algorithm (efficient global optimization) for defect detection and characterization. To show that such approach allows to reduce the numerical cost of ECT data inversion.

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“…The receiver coil is located between the two exciting coils. The purpose of this design is to reduce or eliminate background signals and increase the sensitivity to anomalies [2,18]. The system provides fast, reliable data transfer by taking advantage of the Ethernet communication.…”

Section: Methodsmentioning

confidence: 99%

A novel feature extraction method of eddy current testing for defect detection based on machine learning

Yin

Zhang

et al. 2019

NDT & E International

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Identification of Multiple Cracks from Eddy-Current Testing Signals With Noise Sources by Image Processing and Inverse Analysis

Cited by 22 publications

References 3 publications

Mechanism study for directivity of TR probe when applying Eddy current testing to ferro-magnetic structural materials

Mechanism study for directivity of TR probe when applying Eddy current testing to ferro-magnetic structural materials

Defect localization and characterization in eddy current nondestructive testing by change detection and global optimization

A novel feature extraction method of eddy current testing for defect detection based on machine learning

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