Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Application of particle filtering for prognostics with measurement uncertainty in nuclear power plants

  • Kim, Gibeom (Department of Nuclear Engineering, Kyung Hee University) ;
  • Kim, Hyeonmin (Nuclear ICT Research Division, Korea Atomic Energy Research Institute) ;
  • Zio, Enrico (Department of Nuclear Engineering, Kyung Hee University) ;
  • Heo, Gyunyoung (Department of Nuclear Engineering, Kyung Hee University)
  • Received : 2018.04.23
  • Accepted : 2018.08.02
  • Published : 2018.12.25
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Abstract

For nuclear power plants (NPPs) to have long lifetimes, ageing is a major issue. Currently, ageing management for NPP systems is based on correlations built from generic experimental data. However, each system has its own characteristics, operational history, and environment. To account for this, it is possible to resort to prognostics that predicts the future state and time to failure (TTF) of the target system by updating the generic correlation with specific information of the target system. In this paper, we present an application of particle filtering for the prediction of degradation in steam generator tubes. With a case study, we also show how the prediction results vary depending on the uncertainty of the measurement data.

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References

  1. Nuclear Safety and Security Commission, Design Life Time of Nuclear Power Plants, http://nsic.nssc.go.kr/intrst/view.do?ppleIntrstInfoSn-22&ppleIntrst CtgrySn-44. (Accessed 4 July 2018).
  2. H. Cothron, Steam Generator Management Program: Steam Generator Integrity Assessment Guideline, 1019038, EPRI, 2009.
  3. E. Zio, Reliability engineering: old problems and new challenges, Reliab. Eng. Syst. Saf. 94 (2) (2009) 125-141. https://doi.org/10.1016/j.ress.2008.06.002
  4. N.H. Kim, D. An, J.H. Choi, Prognostics and Health Management of Engineering Systems, Springer International Publishing, Switzerland, 2017.
  5. J.B. Coble, J.W. Hines, Prognostic algorithm categorization with PHM challenge application, in: Prognostics and Health Management, 2008. PHM 2008. International Conference on, IEEE, 2008, October, pp. 1-11.
  6. J. Coble, J.W. Hines, Applying the general path model to estimation of remaining useful life, Int. J. Prognostics Health Manag. 2 (1) (2011) 71-82.
  7. H. Kim, J.T. Kim, G. Heo, Prognostics for integrity of steam generator tubes using the general path model, Nucl. Eng. Technol. 50 (1) (2017) 88-96. https://doi.org/10.1016/j.net.2017.10.006
  8. D. An, J.H. Choi, N.H. Kim, Prognostics 101: a tutorial for particle filteringbased prognostics algorithm using Matlab, Reliab. Eng. Syst. Saf. 115 (2013) 161-169. https://doi.org/10.1016/j.ress.2013.02.019
  9. E. Zio, G. Peloni, Particle filtering prognostic estimation of the remaining useful life of nonlinear components, Reliab. Eng. Syst. Saf. 96 (3) (2011) 403-409. https://doi.org/10.1016/j.ress.2010.08.009
  10. E. Zio, The Monte Carlo Simulation Method for System Reliability and Risk Analysis, vol. 39, Springer, London, 2013.
  11. J. Kim, Nuclear Power Plants System - System and Design, Korea Atomic Industrial Forum, 2011.
  12. J. Hong, Nuclear Materials, Hanshouse, 2012.
  13. H. Kim, H. Shim, C. Oh, S. Jung, Y. Chang, H. Kim, Development of probabilistic program for structural integrity assessment of steam generator tubes, in: The Korean Society of Mechanical Engineers Fall Annual Conference, 2012, pp. 477-481.
  14. Nuclear Regulatory Commission, Bases for Plugging Degraded PWR Steam Generator Tubes (No. REG/G-1.121 (8-76)), Nuclear Regulatory Commission, 1976.
  15. P. Paris, F. Erdogan, A critical analysis of crack propagation laws, J. Basic Eng. 85 (4) (1963) 528-533. https://doi.org/10.1115/1.3656900
  16. H. ALFREDO, W.H. TANG, Probability concepts in engineering, Planning 1 (4) (2007) 1-3.
  17. R. Gnanadesikan, Methods for Statistical Data Analysis of Multivariate Observations, vol. 321, John Wiley & Sons, 2011.
  18. H. Kim, S.H. Lee, J.S. Park, H. Kim, Y.S. Chang, G. Heo, Reliability data update using condition monitoring and prognostics in probabilistic safety assessment, Nucl. Eng. Technol. 47 (2) (2015) 204-211. https://doi.org/10.1016/j.net.2014.12.008
  19. G. Kim, G. Heo, H. Kim, Prognostics for steam generator tube rupture using Markov chain model, in: Korean Nuclear Society Spring Meeting 2016, 2016.
  20. G. Kim, H. Kim, Y. Chang, S. Jung, G. Heo, Data-Driven prognostics for major piping in nuclear power plants, in: Asia Pacific Conference of the Prognostics and Health Management Society 2017, 2017.
  21. G. Kim, G. Heo, Comparison of model-based and data-driven prognostics: case study for steam generator tube rupture, in: Korean Nuclear Society Spring Meeting 2018, 2018.
  22. G. Kim, H. Kim, G. Heo, Prognostics Using Particle Filter for Steam Generator Tube Rupture in Nuclear Power Plants. Probabilistic Safety Assessment and Management, 2018, p. 14.

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