• Journal of Magnetics
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• 제16권2호
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• pp.88-91
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• 2011

Steam generator tubes (SGTs) in nuclear power plants (NPPs) are a boundary between the primary side generating heat by nuclear fission and the secondary side generating electric power by a turbine. The water inside the SGT is high temperature and high pressure. Therefore, defects and magnetic phases (MPs) are partly produced in non-magnetic SGT by high stresses and temperatures. This causes trouble regarding the safety of SGTs but it is difficult to detect the MP using the conventional eddy current technique (ECT). In particular, a circumferential defect (CD) and circumferential magnetic phase (CMP) cannot detected by ECT. Consequently, a new method is needed to detect CDs and CMPs in SGT. A new U-type yoke with two types of coils was designed and the reactance signal by the CMPs and CDs in the SGT material was simulated.

• Journal of Magnetics
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• 제15권2호
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• pp.70-73
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• 2010

A magnetic phase is partly produced in a steam generator tube due to stress and heat, because steam generator tubes are exposed to high temperature, high pressure and radioactivity conditions. This adversely affects the safety of steam generator tubes. However, it is difficult to detect it using conventional eddy current methods. Therefore, a new type of probe is needed to separate the signals from the defects and magnetic phases. In this study, a new U-type yoke, which contained two types of coils, a magnetizing coil and detecting coil, was designed. In addition, the signal induced by the magnetic phase and defect in an Inconel 600 plate were simulated.

• Nuclear Engineering and Technology
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• 제51권7호
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• pp.1784-1790
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• 2019

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.