MRPC eddy current flaw classification in tubes using deep neural networks

  • Park, Jinhyun (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Han, Seong-Jin (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Munir, Nauman (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Yeom, Yun-Taek (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Song, Sung-Jin (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Kim, Hak-Joon (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Kwon, Se-Gon (Korea Railroad Corp.)
  • Received : 2019.03.25
  • Accepted : 2019.05.13
  • Published : 2019.10.25


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.


Supported by : Korea Agency for Infrastructure Technology Advancement (KAIA)


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