caa a22 4500 445381485 CHVBK 20180317143018.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10948-010-0860-3 doi (NATIONALLICENCE)springer-10.1007/s10948-010-0860-3 An Efficient SQUID NDE Defect Detection Approach byUsinganAdaptiveFinite-ElementModeling [Elektronische Daten] [Farrokh Sarreshtedari, Sasan Razmkhah, Nahid Hosseini, Jurgen Schubert, Marko Banzet, Mehdi Fardmanesh] Incorporating the finite-element method for the modeling of the SQUID NDE response to a predefined defect pattern, an adaptive algorithm has been developed for the reconstruction of unknown defects using an optimization algorithm for updating of the forward problem. The defect reconstruction algorithm starts with an initial estimation for the defect pattern. Then the forward problem is solved and the obtained field pattern is compared with the measured signal from the SQUID NDE system. The result is used by an optimization algorithm to update the defect structure to be incorporated in the FEM forward problem for the next iteration. Since the mentioned model based inverse algorithm normally consumes a lot of computational resources, the number of iterations plays an important role in the determination of the total response convergence time. Consequently, different optimization algorithms have been applied and their performances are compared. In this work by incorporating an efficient forward model and using the stochastic and deterministic optimization algorithms for defect updating we have investigated their performance on the inversion of the SQUID NDE signal and also their ability to defect reconstruction in the noisy environment. Springer Science+Business Media, LLC, 2010 SQUID NDE nationallicence Defect detection algorithm nationallicence Finite-element modeling nationallicence Sarreshtedari Farrokh Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut Razmkhah Sasan Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut Hosseini Nahid Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut Schubert Jurgen Forschungszentrum Jülich, Institute of Bio and Nano-systems, 52425, Juelich, Germany aut Banzet Marko Forschungszentrum Jülich, Institute of Bio and Nano-systems, 52425, Juelich, Germany aut Fardmanesh Mehdi Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut Journal of Superconductivity and Novel Magnetism Springer US; http://www.springer-ny.com 24/1-2(2011-01-01), 1077-1081 1557-1939 24:1-2<1077 2011 24 10948 https://doi.org/10.1007/s10948-010-0860-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10948-010-0860-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sarreshtedari Farrokh Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Razmkhah Sasan Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Hosseini Nahid Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Schubert Jurgen Forschungszentrum Jülich, Institute of Bio and Nano-systems, 52425, Juelich, Germany aut NATIONALLICENCE 700 1- Banzet Marko Forschungszentrum Jülich, Institute of Bio and Nano-systems, 52425, Juelich, Germany aut NATIONALLICENCE 700 1- Fardmanesh Mehdi Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 773 0- Journal of Superconductivity and Novel Magnetism Springer US; http://www.springer-ny.com 24/1-2(2011-01-01), 1077-1081 1557-1939 24:1-2<1077 2011 24 10948 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer