MRPC eddy current flaw classification in tubes using deep neural networks
| Autor: | Se-Gon Kwon, Hak-Joon Kim, Nauman Munir, Jinhyun Park, Yun-Taek Yeom, Sung-Jin Song, Seong-Jin Han |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 020209 energy Pattern recognition 02 engineering and technology Key issues lcsh:TK9001-9401 030218 nuclear medicine & medical imaging law.invention 03 medical and health sciences 0302 clinical medicine Data acquisition Nuclear Energy and Engineering law Electromagnetic coil Nondestructive testing 0202 electrical engineering electronic engineering information engineering Eddy current Deep neural networks Preprocessor lcsh:Nuclear engineering. Atomic power Artificial intelligence business Training performance |
| Zdroj: | Nuclear Engineering and Technology, Vol 51, Iss 7, Pp 1784-1790 (2019) |
| ISSN: | 1738-5733 |
| Popis: | Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large. Keywords: Steam generator tube, ECT, MRPC, Automated classification, DNN |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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