Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Prognostics for integrity of steam generator tubes using the general path model

  • Kim, Hyeonmin (Nuclear ICT Research Division, Korea Atomic Energy Research Institute) ;
  • Kim, Jung Taek (Nuclear ICT Research Division, Korea Atomic Energy Research Institute) ;
  • Heo, Gyunyoung (Department of Nuclear Engineering, Kyung Hee University)
  • Received : 2017.03.02
  • Accepted : 2017.10.16
  • Published : 2018.02.25
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Abstract

Concerns over reliability assessments of the main components in nuclear power plants (NPPs) related to aging and continuous operation have increased. The conventional reliability assessment for main components uses experimental correlations under general conditions. Most NPPs have been operating in Korea for a long time, and it is predictable that NPPs operating for the same number of years would show varying extent of aging and degradation. The conventional reliability assessment does not adequately reflect the characteristics of an individual plant. Therefore, the reliability of individual components and an individual plant was estimated according to operating data and conditions. It is essential to reflect aging as a characteristic of individual NPPs, and this is performed through prognostics. To handle this difficulty, in this paper, the general path model/Bayes, a data-based prognostic method, was used to update the reliability estimated from the generic database. As a case study, the authors consider the aging for steam generator tubes in NPPs and demonstrate the suggested methodology with data obtained from the probabilistic algorithm for the steam generator tube assessment program.

Keywords

References

  1. E. Zio, Reliability engineering: old problems and new challenges, Reliab. Eng. Syst. Saf. 94 (2009) 125-141. https://doi.org/10.1016/j.ress.2008.06.002
  2. G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess, B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley & Sons, Inc, 2006.
  3. J.K. Kim, Nuclear Power Plants System - System and Design, Korea Atomic Industrial Forum, Inc, 2011, pp. 13-15.
  4. P. Varde, M.G. Pecht, Role of prognostics in support of integrated risk-based engineering in nuclear power plant safety, Int. J. Progn. Health Manag. 3 (2012) 59.
  5. P.Ramuhalli,B. Ivans Jr.,E.Hirt, D.Wootan,G.Coles,M. Mitchell, et al.,AnUpdated Methodology for Enhancing RiskMonitors with Integrated Equipment Condition Assessment, Pacific Northwest National Laboratory, PNNL-23478, 2014.
  6. S. Poghosyan, A. Amirjanyan, Risk-Informed Prioritization of Modernization Activities Using Ageing PSA Model, Probabilistic Safety Assessment and Management, Honolulu, US, 2014, p. 27.
  7. H. Kim, G. Heo, Survey on prognostics techniques for updating initiating event frequency in PSA, in: Korean Nuclear Society Spring Meeting, Jeju, Korea, 2015.
  8. 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 (2015) 204-211. https://doi.org/10.1016/j.net.2014.12.008
  9. J. Coble, J.W. Hines, Applying the general path model to estimation of remaining useful life, Int. J. Progn. Health Manag. 2 (2011) 71.
  10. C.J. Lu, W.O. Meeker, Using degradation measures to estimate a time-tofailure distribution, Technometrics 35 (1993) 161-174. https://doi.org/10.1080/00401706.1993.10485038
  11. R.B. Chinnam, On-line reliability estimation of individual components, using degradation signals, IEEE Trans. Reliab. 48 (1999) 403-412. https://doi.org/10.1109/24.814523
  12. T. Grish, S. Lam, J. Jayaram, Reliability Prediction Using Degradation Data-A Preliminary Study Using Neural Network-Based Approach, European Safety and Reliability 2003, Maastricht, Netherlands, 2003, pp. 15-18.
  13. H. Kim, Development of Prognostics Methodologies for Ageing-Dependent PSA, Kyung Hee University, 2016.
  14. W.M. Bolstad, Introduction to Bayesian Statistics, John Wiley & Sons, 2013.
  15. K.-R. Koch, Introduction to Bayesian Statistics, Springer Science & Business Media, 2007.
  16. P.M. Lee, Bayesian Statistics: An Introduction, John Wiley & Sons, 2012.
  17. M. Modarres, M. Kaminskiy, V. Krisvtsov, Reliability Engineering and Risk Analysis e A Practical Guide, second ed., CRC Press, 2010, pp. 135-137.
  18. J. Coble, J.W. HINES, Incorporating prior belief in the general path model: a comparison of information sources, Nucl. Eng. Technol. 46 (2014) 773-782. https://doi.org/10.5516/NET.04.2014.722
  19. E. Zio, The Monte Carlo Simulation Method for System Reliability and Risk Analysis, Springer, 2013.
  20. E. Zio, M. Marseguerra, Basics of the Monte Carlo Method with Application to System Reliability, Hagen, LiLoLe, 2002.
  21. E. Zio, G. Peloni, Particle filtering prognostic estimation of the remaining useful life of nonlinear components, Reliab. Eng. Syst. Saf. 96 (2011) 403-409.
  22. Korea Atomic Energy Research Institute, The type of degradation mechanisms for steam generator, http://mdportal.kaeri.re.kr/materials-safety/degradationlwr/degradation-lwr-sg/139-d20120065. (Accessed 14 February 2017).
  23. H. Kim, H. Shim, C. Oh, S. Jung, Y. Chang, H. Kim, et al., Development of probabilistic program for structural integrity assessment of steam generator tubes, in: Korean Society of Mechanical Engineers Fall Annual Conference, Changwon, Korea, 2012, pp. 477-481.
  24. E. Fuller, Steam generator integrity assessment guidelines revision 2-Non- Proprietary Version1012987 (Final Report), Electric Power Research Institute, TR-1012987, 2006.

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