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Metody rozwiązywania zagadnień odwrotnych w defektoskopii wiroprądowej

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
EN
Methods of solving inverse problems in eddy current defectoscopy
Języki publikacji
PL
Abstrakty
PL
W artykule dokonano przeglądu metod stosowanych przy rozwiązywaniu zagadnień odwrotnych w metodzie jedno i wieloczęstotliwościowej prądów wirowych. Celem pracy było zebranie i usystematyzowanie dotychczasowego stanu wiedzy dotyczącego tego tematu.
EN
This paper contains a review of methods used in solving inverse problems for eddy current method. The main purpose of this paper is to collect and systematize the actual scientific knowledge and the main research trends in eddy current data inversion.
Rocznik
Strony
7--11
Opis fizyczny
Bibliogr. 42 poz., rys.
Twórcy
autor
  • Politechnika Szczecińska, Katedra Elektrotechniki Teoretycznej i Informatyki
Bibliografia
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  • [4] Kojima F., Computational Method for Crack Shape Reconstruction of Steam Generator Tubes Using Method of Mapping, Electomagnetic Nondestructive Evaluation IV, IOS Press (2000), 181-188
  • [5] Sikora R., Komorowski M., Chady T., A Neural Network Model of Eddy Current Probe, Electromagnetic Nondestructive Evaluation, IOS Press (1997), 231-238
  • [6] Elshafiey I., Udpa L., Udpa S. S., Application of Neural Networks to Inverse Problems in Electromagnetics, IEEE, Trans. Magn., 30 (1994), n.5, 3629-3632
  • [7] Burrascano P., Cardelli E., Faba A., Fiori S., Massinelli A., A Multilayer Perceptron Approach to a Non-Destructive Test Problem, Electromagnetic Nondestructive Evaluation V, IOS Press (2001), 75-81
  • [8] Reaknos I.T., Theodoulidis T. P., Panas S. M., Tsiboukis T. D., Impedance inversion in eddy current testing of layered planar structures via neural networks, NDT&E International, 30 (1997), n.2, 69-74
  • [9] Ramuhalli P., Udpa L., Udpa S., Neural Network Algorithm for Electromagnetic NDE Signal Inversion, Electromagnetic Nondestructive Evaluation V, IOS Press (2001), 121-130
  • [10] Morabito F. C., Versaci M., A Fuzzy Neural Approach to Localizing Holes in Conducting Plates, IEEE, Trans. Magn., 37 (2001), n.5, 3534-3537
  • [11] Chady T., Enokizono M., Sikora R., Todaka T., Tsuchida Y., Natural Crack Recognition Using Inverse Neural Model and Multi-Frequency Eddy Current Method, IEEE, Trans. Magn., 37 (2001), n.4, 2797-2799
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  • [13] Tsuchida Y., Takahata R., Chady T., Enokizono M., Numerical analysis of multi-frequency excitation spectrogram method for eddy current testing, Review of Progress Quantitative Nondestructive Evaluation 20, (2001), 627-632
  • [14] Chady T., Enokizono M., Sikora R., Neural Network Models of Eddy Current Multi-Frequency System for Nondestructive Testing, IEEE, Trans. Magn., 36 (2000), n.4, 1725-1727
  • [15] Yusa N., Cheng W., Miya K., Inverse Problems in Eddy Current Testing Using Neural Network, Review in Progress in Quantitative Nondestructive Evaluation 19, (2000), 549-556
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  • [17] Song S. J., Shin Y. K., Eddy current flaw characterization in tubes by neural networks and finite element modeling, NDT&E International, 33 (2000), 233-243
  • [18] Kojima F., Kubotab N., Hashimoto S., Identification of crack profiles using genetic programming and fuzzy inference, Journal of Materials Processing Technology, 108 (2001), 263-267
  • [19] Duca A., Ioan D., A Hybrid Transform-Neural Network Approach for the Inverse Problem in NDET, Non-Linear Electromagnetic Systems, IOS Press (2000), 269-272
  • [20] Dogandzić A., Xiang P., A Statistical Model For Eddy-Current Signals from Steam Generator Tubes, Quantitative Nondestructive Evaluation 23A, (2003)
  • [21] Khandetsky V., Antonyuk I., Signal processing in defect detection using back-propagation neural networks, NDT&E International, 35 (2002), 483-488
  • [22] Bouden D., Lemahieu I., Basart J., Inversion of Eddy Current Impedance Change Data for Single Cracks and Separate Cracks, Electromagnetic Nondestructive Evaluation V, IOS Press (2001), 105-112
  • [23] Pierri R., Rubinacci G., Tamburrino A., A Quadratic Approach for the Reconstruction of Conductivity Profiles Using Eddy Currents, IEEE, Trans. Magn., 32 (1996), n.3, 1310-1312
  • [24] Brusquet L. L., Davoust M. E., Fleury G., Groove Sizing Using a Robust Neural Network Approach, Review of Quantitative Evaluation 22, (2003), 711-718
  • [25] Pavo J., Mija K., Reconstruction of Crack Shape by Optimization Using Eddy Current Field Measurement, IEEE, Trans. Magn., 30 (1994), n.5, 3407-3410
  • [26] Sikora R., Baniukiewicz P., Reconstruction of cracks from eddy current signals using genetic algorithms and fuzzy logic, Review of Progress in Quantitative Evaluation, (2005)
  • [27] Michelsson O., Uhlmann F. H., A Boundary-Integral Based Forward Solution for Eddy Current Nondestructive Testing, IEEE, Trans. Magn., 36 (2000), n.4
  • [28] Albanese R., Numerical Techniques for ECT Crack Retrieval Non-Linear Electromagnetic Systems, IOS Press (2000), 281-284
  • [29] Huang H., Takagi T., Fast signal prediction of noised signals in eddy current testing, IEEE, Trans. Magn., 36 (2000), n.4, 1719-1723
  • [30] Chen Z., Miya K., ECT inversion using a knowledge-based forward solver, Journal of Nondestructive Evaluation, 17 (1998), n.3, 167-175
  • [31] Huang H., Takagi T., Inverse Analyses for Natural and Multicracks Using Signals From a Differential Transmit-Receive ECT Probe, IEEE, Trans. Magn., 38 (2002), n.2, 1009-1012
  • [32] Simkin J., Trowbridge C.W., Optimization problems in electromagnetics, IEEE, Trans. Magn., 27 (1991), n.5, 4016-4019
  • [33] Magele C. A., Pries K., Renhart W., Dyczij-Edlinger R., Richter K. R., High Order Evolution Strategies for the Global Optimization of Electromagnetic Devices, IEEE, Trans. Magn., 29 (1993), n.2, 1775-1778
  • [34] Arkadan A. A., Sareen T., Subramaniam S., Genetic algorithms for Nondestructive Testing in Crack Identification, IEEE, Trans. Magn., 30 (1994), n.6, 4320-4322
  • [35] Badics Z., Matsumoto Y., Kojima S., Usui Y., Aoki K., Nakayasu F., Kurokawa A., Rapid Flaw Reconstruction Scheme for Three-dimensional Inverse Problem in eddy Current NDE, Electromagnetic Nondestructive Evaluation, IOS Press (1997), 303-309
  • [36] Rao P. B. C., Rao C. B., Jayakumar T., Raj B., Simulation of eddy current signals from multiple defects, NDT&E International, 29 (1996), n.5, 269-273
  • [37] Pavo J., Reconstruction of Group Cracks in Plate Specimens Using ECT Impedance Data, Electomagnetic Nondestructive Evaluation IV, IOS Press (2000), 204-211
  • [38] Kumano S., Kawata K., Enami K., Classification of ECT signals using Boundary Cluster Neural Networks, Electromagnetic Nondestructive Evaluation, IOS Press (1997), 239-245
  • [39] Rajesh S. N., Udpa L., Udpa S. S., Numerical Model Based Approach for Estimating Probability of detection in NDE Applications, IEEE, Trans. Magn., 29 (1993), n.2, 1857-1860
  • [40] Hoshikawa H., Koyama K., ECT Flaw Image Restoration by Deconvolution, Electromagnetic Nondestructive Evaluation, IOS Press (1997), 295-302
  • [41] Eua-anant N., Cai X., Udpa L., Chao J., Elshafiey I., Crack Detection in Eddy Current Images of Jet Engine Disks, Review of Progress in Quantitative Nondestructive Evaluation, (2000), 773-780
  • [42] Shyamsunder M. T., Raj B., Rao P. B. C., Jayakumar T., Kalyanasundaram P., Automated Quantitative Defect Characterization by Eddy Current Testing, Review of Progress in Quantitative Evaluation 20, (2001), 649-654
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0014-0022
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