An Attempt at the Detection of a Flaw Using the Inverse Problem Solution of Impedance Imaging

Author(s):  
M. A. Hussain ◽  
M. McKee ◽  
J. Frankel

Abstract In this paper we present some preliminary numerical simulations which allow us to predict a single flaw in a simply connected body. The purpose of this investigation was to detect flaws and cracks of engineering components using the method of electrical current computed tomography (ECCT), which is used in non-destructive testing technology. As in the previous paper, we have utilized the network analogy to detect a single flaw anywhere in the object. For detection of multiple flaws, the analysis has to be refined to give consistent results.

2020 ◽  
Vol 165 ◽  
pp. 04014
Author(s):  
Liu Tao ◽  
Li Jia ◽  
Zheng Zhi-gang ◽  
Huang Zhi ◽  
Jiang Jian ◽  
...  

GPR is an effective non-destructive testing technology. This paper introduces its composition principle and operation method, explains the process of parameter setting and image optimization, obtains the dielectric constant of 10000 points, compares it with the density, and then obtains the uniformity distribution law of construction quality based on image. By calibrating the thickness of the road surface, the effective detection of road diseases can be realized, and the theoretical basis and practical application conditions of GPR technology can be clarified.


Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 612 ◽  
Author(s):  
Jue Hu ◽  
Weiping Xu ◽  
Bin Gao ◽  
Gui Tian ◽  
Yizhe Wang ◽  
...  

Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.


2019 ◽  
Vol 48 (2) ◽  
pp. 212002 ◽  
Author(s):  
张丹丹 ZHANG Dan-dan ◽  
任姣姣 REN Jiao-jiao ◽  
李丽娟 LI Li-juan ◽  
乔晓利 QIAO Xiao-li ◽  
顾健 GU Jian

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Liang Qi ◽  
Mao-cheng Zhao ◽  
Zhong Li ◽  
De-hong Shen ◽  
Jun Lu

2011 ◽  
Author(s):  
Shi-tu Luo ◽  
Xiang-lin Tan ◽  
Meng-chun Pan ◽  
Cheng-guang Fan

2014 ◽  
Vol 353 ◽  
pp. 23-27
Author(s):  
E. Barreira ◽  
S.S. de Freitas ◽  
V.P. de Freitas ◽  
João M.P.Q. Delgado

Infrared thermography is a non destructive testing technology that has been applied to detect buildings pathologies for some decades. The thermograms are affected by several parameters and it is crucial to fully understand them in order to correctly interpret the temperature readings. The infrared radiation is affected by the radiation emitted by the surface and the radiation reflected and emitted by the surroundings. Therefore there are two kinds of parameters that affect the infrared images: parameters connected to the properties of the material itself and parameters connected with the environmental conditions. In this paper we present a sensibility study of the main parameters involved with infrared thermography evaluations to detect building pathologies. To do so, some simple experiments were carried out at the Building Physics Laboratory (LFC) of the Engineering Faculty of Porto University (FEUP). The sensibility study was performed with LFC’s equipment to evaluate how measurements are influenced by emissivity, reflections, absorptance and the meteorological conditions.


2020 ◽  
Vol 20 (5) ◽  
pp. 632-638
Author(s):  
Lenka Kuchariková ◽  
Eva Tillová ◽  
Michaela Kritikos ◽  
Milan Uhríčik ◽  
Ivana Švecová

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