Improved Heat Exchanger Lifecycle Prognostic Methods for Enhanced Light Water Reactor Sustainability
As the licenses of many nuclear power plants in the US and abroad are being extended, accurate knowledge of system and component condition is becoming increasingly important. The US Department of Energy (DOE) has funded a project with the primary goal of developing lifecycle prognostic methods to generate accurate and continuous Remaining Useful Life (RUL) estimates as components transition through unique stages of the component lifecycle. Specific emphasis has been placed on creating and transitioning between three distinct stages of operational availability. These stages correspond to Beginning Of Life (BOL) where little or no operational information is available, early onset operations at various expected and observed stress levels where there is the onset of detectable degradation, and degradation towards the eventual End Of Life (EOL). This paper provides an application overview of a developed lifecycle prognostic approach and applies it to a heat exchanger fouling test bed under accelerated degradation conditions resulting in an increased understanding of system degradation. Bayesian and Bootstrap Aggregation methods are applied to show improvements in RUL predictions over traditional methods that do not utilize these methods, thereby improving the
lifecycle prognostic model for the component. The analyses of results from applying these lifecycle prognostic algorithms to a heat exchanger fouling experiment are detailed.
Condition Monitoring, Prognostics, Heat Exchanger Fouling, Bagging, Bayesian Updating, Empirical Methods
Baraldi, P., Gola, G., Zio, E., Roverso, D., & Hoffman, M. (2011). A Randomized Model Ensemble Approach for Reconstructing Signals from Faulty Sensors, Expert Systems with Applications, vol. 38, Iss. 8, pp. 9211-9224.
Baraldi, P., Roozbeh, R.F., & Zio, E. (2010). Classifierensemble incremental-learning procedure for nuclear transient identification at different operational conditions, Reliability Engineering and System Safety, Elsevier, 2011, 96 (4), pp. 480-488.
Breiman, L. (1996). Bagging Predictors, Machine Learning, vol. 24, pp. 123-140.
Bryll, R., Gutierrez-Osuna, R., & Quek, F. (2003). Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets, Pattern Recognition, vol. 36, Elsevier Ltd., pp. 1291-1302.
Büchlmann, P. (2003). Bagging, subagging and bragging for improving some prediction algorithms, Seminar für Statistik, ETH Zürich, Switzerland.
Büchlmann, P., & Yu, B. (2002). Analyzing Bagging, Published within The Annals of Statistics, vol. 30, no. 4, pp. 927-961, Institute of Mathematical Statistics, JSTOR.
Buecker, B. (2009). Save big bucks with proper condenser performance monitoring, Energy Tech Magazine, April Issue.
Coble, J.B., & Hines, J.W. (2011). Applying the General Path Model to Estimation of Remaining Useful Life, International Journal of Prognostics and Health Management vol. 2, pp. 71-82.
Coble, J.B., Ramuhalli, P., Bond, L.J., Hines, J.W., & Upadhyaya, B.R. (2012). Prognostics and health management in nuclear power plants: A review of technologies and applications, Pacific Northwest National Laboratory Report, PNNL-21515.
Fayard, E.C. (2011). Improving condenser reliability and availability through effective offline cleaning and nondestructive testing, Proceedings of Electric Power Research Condenser Technology Conference, August 3-4, Chicago, IL.
Garvey, D., & Hines, J.W. (2006). Development and Application of Fault Detectability Performance Metrics for Instrument Calibration Verification and Anomaly Detection, Journal of Pattern Recognition Research (JPRR), vol. 1, pp. 2-15.
Gelman, A., Carlin J., Stern, H., & Rubin, D. (2004). Bayesian Data Analysis. vol. 2, Chapman & Hall/CRC, Boca Raton, USA.
Ghosh, J.K., Delampady, M., & Samanta, T. (2006), An Introduction to Bayesian Analysis: Theory and Methods 1st ed. Springer Science & Business Media, New York, USA, pp. 29-59.
Georgiadis, M. C., & Macchietto, S. (2000). Dynamic Modelling and Simulation of Plate Heat Exchangers Under Milk Fouling, Chemical Engineering Science, vol. 55, no. 9, pp. 1605-1619.
Gut, J., & Pinto, J. (2003). Modeling of Plate Heat Exchangers with Generalized Configurations, International Journal of Heat and Mass Transfer, vol. 46, Iss. 14, pp. 2571-2585.
Hines, J.W., & Garvey, D. (2006). Traditional and robust vector selection methods for use with similarity based models, 5th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT), November 12-16, Albuquerque, NM.
Hines, J.W., Garvey, D., Preston, J., & Usynin, A. (2007). Empirical methods for process and equipment prognostics, Tutorial presented at the IEEE Reliability and Maintainability Symposium (RAMS).
Hines, J.W., & Garvey, D. (2006). Process and equipment monitoring toolbox tutorial, Nuclear Engineering Department, University of Tennessee.
Ibrahim, S., & Attia, S. (2015). The Influence of Condenser Cooling Seawater Fouling on the Thermal Performance of a Nuclear Power Plant, Annals of Nuclear Energy, vol. 76, pp. 421-430.
Jonsonn, G.R., Lalot, S., Palsson, O.P., & Desmet, B. (2007). Use of Extended Kalman Filtering in Detecting Fouling in Heat Exchangers, In the International Journal of Heat and Mass Transfer, vol. 50, iss. 13–14, pp. 2643–2655.
Lu, C.J., & Meeker, W.Q. (1993). Using degradation measures to estimate a time-to-failure distribution, Technometrics, vol. 35, no. 2, pp. 161-174.
Meyer, R.M., Bond, L.J., & Ramuhalli, P. (2012). Online Condition Monitoring to Enable Extended Operation of Nuclear Power Plants, International Journal of Nuclear Safety and Simulation, vol. 3, no. 1, pp. 31-50.
Nam, A., Sharp, M., Hines, J.W., & Upadhyaya, B. (2013). Lifecycle Prognostic Algorithm Development and Application to Test Beds, Chemical Engineering Transactions, vol. 33, pp. 901-906.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline Evaluation of Prognostics Performance, International Journal of Prognostics and Health Management, vol. 1, pp. 4.
Sharp, M. (2013). Simple metrics for evaluating and conveying prognostic model performance to users with varied backgrounds, Annual Conference of the Prognostics and Health Management Society. October 14-17, New Orleans, LA.
Schmidt, F.W., Henderson, R.E., & Wolgemuth, C.H. (1993). Introduction to Thermal Sciences: Thermodynamics, Fluid Dynamics, Heat Transfer. Canada: John Wiley & Sons, Inc.
Upadhyaya, B.R., Naghedolfeizi, M., & Raychaudhuri, B. (1994). Residual Life Estimation of Plant Components, P/PM Technology, vol. 7, no. 3, pp. 22-29.
Upadhyaya, B.R., Hines, J.W. (2004). On-line monitoring and diagnostics of the integrity of nuclear plant steam generators and heat exchangers, Final Report: vol. 1, Experimental and Hybrid Modeling Approach for Monitoring Heat Exchanger System Performance, prepared for the DOE-NEER Program by the University of Tennessee, Knoxville, Report No. DEFG07-01ID14114/UTNE-07.
Wand, W.P., & Jones, M.C. (1995). Kernel smoothing, London: Chapman & Hall.