DocumentCode
1532959
Title
Natural crack recognition using inverse neural model and multi-frequency eddy current method
Author
Chady, Tomasz ; Enokizono, Masato ; Sikora, Ryszard ; Todaka, Takashi ; Tsuchida, Yuji
Author_Institution
Oita Ind. Res. Inst., Japan
Volume
37
Issue
4
fYear
2001
fDate
7/1/2001 12:00:00 AM
Firstpage
2797
Lastpage
2799
Abstract
In this paper a Multi-Frequency Excitation and Spectrogram Eddy Current System and an inverse neural model were used to detect and identify natural flaws in steam generator tubes. It is shown that the applied dynamic neural model of the ECT sensor offers very high speed of operation and guarantees reliability of the recognition results
Keywords
crack detection; eddy current testing; inverse problems; neural nets; nuclear reactor steam generators; NDE; crack recognition; dynamic neural inverse model; eddy current testing; magnetic sensor; multi-frequency excitation and spectrogram method; natural flaw; steam generator tube; Eddy current testing; Eddy currents; Electrical capacitance tomography; Fatigue; Frequency; Helium; Inverse problems; Magnetic sensors; Nuclear power generation; Spectrogram;
fLanguage
English
Journal_Title
Magnetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9464
Type
jour
DOI
10.1109/20.951310
Filename
951310
Link To Document