Transactions of the Korean Society of Pressure Vessels and Piping (한국압력기기공학회 논문집)

Korean Society of Pressure Vessels and Piping (한국압력기기공학회)
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Development of the Modified Preprocessing Method for Pipe Wall Thinning Data in Nuclear Power Plants

원자력 발전소 배관 감육 측정데이터의 개선된 전처리 방법 개발

  • Seong-Bin Mun ;
  • Sang-Hoon Lee ;
  • Young-Jin Oh ;
  • Sung-Ryul Kim
  • 문성빈 (한국전력기술(주) 스마트융합연구소, 금오공과대학교 디지털융합공학과 ) ;
  • 이상훈 (한국전력기술(주) 스마트융합연구소) ;
  • 오영진 (한국전력기술(주) 스마트융합연구소) ;
  • 김성렬 (금오공과대학교 컴퓨터소프트웨어공학과)
  • Received : 2023.11.17
  • Accepted : 2023.12.28
  • Published : 2023.12.30
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Abstract

In nuclear power plants, ultrasonic test for pipe wall thickness measurement is used during periodic inspections to prevent pipe rupture due to pipe wall thinning. However, when measuring pipe wall thickness using ultrasonic test, a significant amount of measurement error occurs due to the on-site conditions of the nuclear power plant. If the maximum pipe wall thinning rate is decided by the measured pipe wall thickness containing a significant error, the pipe wall thinning rate data have significant uncertainty and systematic overestimation. This study proposes preprocessing of pipe wall thinning measurement data using support vector machine regression algorithm. By using support vector machine, pipe wall thinning measurement data can be smoothened and accordingly uncertainty and systematic overestimation of the estimated pipe wall thinning rate data can be reduced.

Keywords

Acknowledgement

본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제입니다(과제번호 : 20224B10100030).

References

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  2. Oh. Y.J., Jee K.K. and Park. H.B., "Machine Learning Approach for Thinned Pipe Classification using Thickness Measurement Data of Nuclear Secondary Piping Systems", Trans. of the Korean Nuclear Society Spring Meeting, Jeju, 2019.05.23. - 24. 
  3. Oh. Y.J., Yun. H., Moon. S.J., Han K.H. and Park. B.U., 2015, "Development of Numerical Algorithm of Total Point Method for Thinning Evaluation of Nuclear Secondary Pipes", Trans. of the KPVP, Vol.11, No.2, pp. 31-39. 
  4. Oh. Y.J., Yun. H. and Park. H.B., 2018, "Preprocessing Method of Measurement Data for Machine-Learning Application to Thinning Location Prediction", Proc. of 2018 KPVP, Gwangju, 2018.11.22 -23. 
  5. Yun. H., Moon. S.J, and Oh. Y.J., "Development of Wall-Thinning Evaluation Procedure for Nuclear Power Plant Piping Part 1: Quantification of Thickness Measurement Deviation", Nuclear Engineering and Technology, Vol.48, No.3, pp.820-830, 2016. doi:http://dx.doi.org/10.1016/j.net.2016.01.020 
  6. Aurelien Geron, 2020, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd, Hanbit Media Inc., pp.216-218. 
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