Data-Driven Prognostics for Major Piping in Nuclear Power Plants



Gibeom Kim Hyeonmin Kim Yoon-Suk Chang Seungho Jung Gyunyoung Heo


As operation period of Nuclear Power Plants (NPPs) is getting longer, necessity of reflecting ageing effect is increasing. Especially, when it comes to the piping in NPPs such as reactor coolant system piping or steam generator
tubes, it is vulnerable to stress corrosion crack (SCC) or wear due to the fluid with high temperature, high pressure and radiation. Accidents related to such cases have been reported. Since ruptures of the piping can result in severe accidents, it is important to predict and prevent them in advance. Current NPPs ageing management is performed with the physical model based on generic experimental data, which cannot properly consider each NPPs’ different operation environment or history. Prognostics using plant specific data can compensate this limit of ageing management using the physical model. Recently, as usable data of NPPs is increasing with the development of instrumentation technology, applicability of prognostics for NPPs has been increased. Therefore, this paper suggests some prognostics methods such as GPM (General Path Model), MCMC (Markov Chain Monte Carlo) and Particle filter that can consider ageing degradation for the major piping in NPPs. It is expected that prognostics results can be used in Probabilistic Safety Assessment (PSA) considering current or future ageing degradation.

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Borgonovo, E., Marseguerra, M., & Zio, E. (2000). A Monte Carlo methodological approach to plant availability modeling with maintenance, aging and obsolescence. Reliability Engineering & System Safety, 67(1), 61-73.
Coble, J. B., & Hines, J. W. (2008). Prognostic algorithm categorization with PHM challenge application. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-11). IEEE.
Gilks, W. R. (2005). Markov chain monte carlo. John Wiley & Sons, Ltd.
Gordon, N., Ristic, B., & Arulampalam, S. (2004). Beyond the kalman filter: Particle filters for tracking applications. Artech House, London, 830.
Kim, H., & Heo, G. (2015). Survey on Prognostics Techniques for Updating Initiating Event Frequency in PSA. Korean Nuclear Society, 1.
Kim, H., Lee, S. H., Park, J. S., Kim, H., Chang, Y. S., & Heo, G. (2015). Reliability data update using condition monitoring and prognostics in probabilistic safety assessment. Nuclear Engineering and Technology, 47(2), 204-211. Korean Nuclear Society, 2.
Kim, H., Shim, H., Oh, C., Jung, S., Chang, Y., & Kim, H. (2012). Development of probabilistic program for structural integrity assessment of steam generator tubes. The Korean Society of Mechanical Engineers Fall Annual Conference, 477-481.
Kim, H., Lee, S. H., Park, J. S., Kim, H., Chang, Y. S., & Heo, G. (2014). Study of Updating Initiating Event Frequency using Prognostics.
Kim, G., Kim, H., & Heo, G. (2016). Prognostics for Steam Generator Tube Rupture using Markov Chain model. Korean Nuclear Society, 2
Lu, C. J., & Meeker, W. O. (1993). Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161-174.
Varde, P. V., & Pecht, M. G. (2012). Role of prognostics in support of integrated risk-based engineering in nuclear power plant safety. International Journal of Prognostics and Health Management Volume 3 (color), 59.
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