Prognostics for Light Water Reactor Sustainability: Empirical Methods for Heat Exchanger Prognostic Lifetime Predictions

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Zachary Welz Alan Nam Michael Sharp J. Wesley Hines Belle R. Upadhyaya

Abstract

As the licenses of many nuclear power plants in the US and abroad are being extended, the accurate knowledge of system and component condition is becoming more important. The US Department of Energy (DOE) has funded a project with the primary goal of developing lifecycle prognostic methods that generate accurate and continuous remaining useful life (RUL) estimates as components transition through each stage of the component lifecycle. These stages correspond to beginning of life, operations at various expected and observed stress levels, the onset of detectable degradation, and degradation towards the eventual end of life. This paper provides an overview and application of a developed lifecycle prognostic approach and applies it to a heat exchanger fouling test bed under accelerated degradation conditions. The results of applying the lifecycle prognostic algorithms to the heat exchanger fouling experiment are given, followed by a discussion of the strengths and shortcomings of the developed techniques for this application.

How to Cite

Welz, Z., Nam, A., Sharp, M., Hines, J. W., & Upadhyaya, B. R. (2014). Prognostics for Light Water Reactor Sustainability: Empirical Methods for Heat Exchanger Prognostic Lifetime Predictions. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1494
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Keywords

condition monitoring, prognostics, heat exchanger fouling

References
Ardsomang, T., Hines, J.W., & Upadhyaya, B.R. (2013). Heat Exchanger Fouling and Estimation of Remaining Useful Life, Annual Conference of Prognostics and Health Management Society.
Buecker, B. (2009). Save Big Bucks with Proper Condenser Performance Monitoring, Energy Tech Magazine, April 2009.
Coble, J.B., & Hines, J.W. (2011:2). Applying the General Path Model to Estimation of Remaining Useful Life, International Journal of Prognostics and Health Management.
Fayard, E.C. (2011). Improving Condenser Reliability and Availability Through Effective Offline Cleaning and Nondestructive Testing, Proceedings of Electric Power Research Condenser Technology Conference, July 2011.
Garvey, D., & Hines, J.W. (2006). Traditional and Robust Vector Selection Methods for use with Similarity Based Models, by 5th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT '05), Albuquerque, NM.
Gelman, A., Carlin J., Stern, H., & Rubin, D. (2004). Bayesian Data Analysis 2nd ed. Boca Raton: Chapman and Hall/CRC, Boca Raton, USA.
Georgiadis, M. C., & Macchietto, S. (2000). Dynamic modelling and simulation of plate heat exchangers under milk fouling, Chemical Engineering Science, 55(9).
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.
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 (IJHMT).
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.
Nam, A., Sharp, M., Hines, J.W., & Upadhyaya, B. (2013). Lifecycle Prognostic Algorithm Development and Application to Test Beds, Chemical Engineering Transactions, Vol. 33.
Saxena, A., et al. (2010). Metrics for Offline Evaluation of Prognostics Performance,, In the Internation Journal of Prognostics and Health Management (IJPHM), vol. 1(1) 001, pp. 20.
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. 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 7 (3), 22 – 29.
Upadhyaya, B.R., Hines, J.W., et al. (2004). On-Line Monitoring and Diagnostics of the Integrity of Nuclear Plant Steam Generators and Heat Exchangers, Final Report: Volume 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. DE-FG07- 01ID14114/UTNE-07.
Wand, W.P., & Jones, M.C. (1995). Kernel Smoothing, London: Chapman & Hall.
Yang, D., Usynin, A., & Hines, J.W. (2006). Anomalybased intrusion detection for SCADA systems, 5th Intl. Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies (NPIC&HMIT 05).
Section
Technical Papers