The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A study on early faults detection of pressurizer pressure control system using MTS

MTS를 이용한 가압기 압력 제어 계통의 조기 고장 감지에 대한 연구

  • 차재민 (고등기술연구원 플랜트 SE팀) ;
  • 김준영 (고등기술연구원 플랜트 SE팀) ;
  • 신중욱 (고등기술연구원 플랜트 SE팀) ;
  • 염충섭 (고등기술연구원 플랜트 SE팀) ;
  • 강성기 ((주)엠엔디)
  • Received : 2016.09.02
  • Accepted : 2016.10.30
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

A pressurizer is a major equipment system in a nuclear power plant (NPP) and controls the reactor cooling system pressure within the allowable range. Faults in the pressurizer can be critical to the NPP; therefore, early fault detection in the pressurizer is significant for NPP safety. This study applies Mahalanobis Taguchi system (MTS), which is one of the promising pattern classification methods, based on the Mahalanobis distance concept and Taguchi quality engineering theory to the early fault detection problem of the pressurizer pressure control system. We conducted experiments using data from full scope NPP simulator based on a pressurizer pressure transmitter faults scenario to validate the faults detection performance of MTS. As a result, MTS can rapidly detect the faults compared to conventional faults detection based on single sensor monitoring.

원자력 발전소의 가압기는 1차 계통의 냉각재가 고온에서도 기화되지 않도록 압력을 가해주는 장치이다. 즉, 가압기의 고장은 원자력 발전소에 큰 영향을 미칠 수 있으며, 따라서, 가압기의 조기 고장 감지는 원자력 발전소의 안전에 매우 중요하다. 이를 위해, 본 연구에서는 마할라노비스 거리 개념과 다구찌 품질 공학 이론에 기반한 패턴 분류 인식 알고리즘 중 하나인 마할라노비스 다구찌 시스템(MTS)을 가압기 압력 제어 계통의 조기 고장 감지에 적용하였다. MTS의 고장 감지 성능을 검증하기 위해, 실제 원자력 발전소에서 발생하고 있는 가압기 압력전송기 고장 시나리오를 대상으로 하여, Full Scope 시뮬레이터를 통해 모사된 데이터에 적용하였다. 실험 결과, MTS는 단일 센서모니터링 기반의 전통적인 고장 감지에 비하여 매우 빠르게 고장을 감지할 수 있었다.

Keywords

References

  1. Choo, K.-K., Oh, S.-D., and Kim, Y.-J. (2012). Faults diagnosis of wind turbine using MTS techniques, Proceedings of the Korean Institute of the Industrial Engineers/The Korean Operations Research and Management Science Society, 2406-2416.
  2. Chun, H.-Y., Park, G.-T., Park, S.-Y., and Kim, In.-S. (1987). A study of instrument failure detection in PWR pressurizer, The Transaction of the Korean Institute of Electrical Engineers, 36, 70-76.
  3. Hong, J.-E. (2009). Analysis of multivariate system using Mahalanobis Taguchi system, Journal of the Society of Korea Industrial and Systems Engineering, 32, 20-25.
  4. Jin, X. and Chow, T. W. S. (2013). Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis-Taguchi system, Expert Systems and Applications, 40, 5787-5795. https://doi.org/10.1016/j.eswa.2013.04.024
  5. Lee, C. Y. (1992). Fault diagnosis of a PWR pressurizer using an artificial neural network, Proceedings of The Institute of Electronics Engineers of Korea, 210-219.
  6. Oh, S.-H., Kim, D.-I., Zhu, O.-P., and Kim, K.-J. (1996). A study on the failure detection and validation of pressurizer level sensor signal in nuclear power plant, The Transaction of the Korean Institute of Electrical Engineers, 45, 1460-1466.
  7. Park, J. H., Lee, D. H., and Lee, S. (2002). Failure diagnosis of pressurizer in PWR, Proceedings of Korean Society of Precision Engineering, 474-477.
  8. Park, S. G., Park, W. S., Lee, Y. Y., Kim, D. S., and Oh, J. E. (2008). A fault diagnosis on the rotating machinery using MTS, Transactions of the Korean Society for Noise and Vibration Engineering, 18, 619-623. https://doi.org/10.5050/KSNVN.2008.18.6.619
  9. Soylemezoglu, A., Jagannathan, S., and Saygin, C. (2010). Mahalanobis Taguchi system (MTS) as a prognostics tool for rolling element bearing failures, Journal of Manufacturing Science and Engineering, 132.
  10. Taguchi, G. and Jugulum, R. (2002). The Mahalanobis-Taguchi Strategy: A Pattern Technology System, John Wiley & Sons, New York.
  11. Tylee, J. L. (1982). A generalized likelihood ratio approach to detecting and identifying failures in pressurizer instrumentation, Nuclear Technology, 56, 484-492. https://doi.org/10.13182/NT82-A32907
  12. Wang, Z., Wang, Z., Tao, L., and Ma, J. (2012). Fault diagnosis for bearing based on Mahalanobis-Taguchi system, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), 1-5.
  13. Willsky, A. S. and Jones, H. L. (1976). A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems, IEEE Transactions on Automatic Control, 21, 108-112. https://doi.org/10.1109/TAC.1976.1101146