Detection and prediction of thimble tube defects using artificial neural networks

Issue title: 26th International Workshop on Electromagnetic Nondestructive Evaluation

Guest editors: Theodoros Theodoulidis, Christophe Reboud and Christos Antonopoulos

Article type: Research Article

Authors: Wu, Tong^a; | Wang, Yuanyuan^b | Li, Xiaoguang^c | Tao, Yu^a | Ye, Chaofeng^a;

Affiliations: [a] School of Information Science and Technology, ShanghaiTech University, Shanghai, China | [b] Yangjiang Nuclear Power Co., Ltd., Guangdong, China | [c] CGNPC Inspection Technology Co., Ltd., Suzhou, China

Correspondence: [*] Corresponding authors: Tong Wu, School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China. E-mail: wutong2@shanghaitech.edu.cn. Chaofeng Ye, School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China. E-mail: yechf@shanghaitech.edu.cn

Abstract: The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%.

Keywords: Thimble tube, eddy current testing, artificial neural network, quantification, prediction

DOI: 10.3233/JAE-230132

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 74, no. 4, pp. 327-334, 2024