Role of Prognostics in Support of Integrated Risk-based Engineering in Nuclear Power Plant Safety
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Published
Oct 18, 2020
P.V. Varde
Michael G. Pecht
Abstract
There is a growing trend in applying a prognostics and health management approach to engineering systems in general and space and aviation systems in particular. This paper reviews the role of prognostics and health management approach in support of integrated risk-based applications to nuclear power plants, like risk-based in-service inspection, technical specification optimization, maintenance optimization, etc. The review involves a survey of the state-of-art technologies in prognostics and health management and an exploration of its role in support of integrated risk-based engineering and how the technology can be adopted to realize enhanced safety and operational performance. An integrated risk-based engineering framework for nuclear power plants has been proposed, where probabilistic risk assessment plays the role of identification, prioritization and optimization of systems, structures, and components, while deterministic assessment is performed using a prognostics and health management approach. Keeping in view the requirements of structural reliability assessment, the paper also proposes essential features of a ‘Mechanics-of-Failure’ approach in support of integrated risk-based engineering. The performance criteria used in prognostics and health management has been adopted to meet requirements of risk-based applications.
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Keywords
condition monitoring, prognostics and health management, Risk-based engineering, Nuclear plants
References
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Wheeler K.R., Kurtpglu T., and Poll S.D., (2010). A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics. International Journal of Prognostics and Health Management”, 003, (ISSN 2153-2648).
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Bhambra J.K., Nayagam S., Jennions I., (2011). Electronic prognostics and health management of aircraft avionics using digital power convertors. 2011 Annual Conference of the Prognostics and Health Management Society.
Bhattacharya, D., Kim T., and Pal S., (2010). A comparative study of wireless sensor networks and their routing protocols. Sensors, 10, 10506-10523; doi:10.3390/s101210506, ISSN 1424-8220.
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Bond L.J., Taylor, T.T., Doctor S.R., Hull Amy B., and Malik S. N., (2008b). Proactive management of materials degradation for nuclear power plant systems. 2008 International Conference on Prognostics and Health Management.
Chatterjee, K, and Nodarres, M., (2012). A probabilistic physics-of-failure approach to prediction of steam generator tube rupture frequency. Nuclear Engineering and Science, Vol. 170, Number 2, pp. 136-150.
Chen Chaochao, Brown D., Sconyers Chris, Zhang Bin, Vachtsevanos George, Orchard M.E. (2012). An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems. Expert Systems with Applications 39 9031–9040.
Chen Chaochao, Pech Michael, (2012). Prognostics of lithium-ion batteries using model-based and data-driven methods. Prognostics and System Health Management Conference (PHM-2012 Beijing).
Chen Chaochao, Vachtsevanos George (2012). Bearing condition prediction considering uncertainty: an interval type-2 fuzzy neural network approach. Robotics and Computer-Integrated Manufacturing 28 509–516.
Chen Chaochao, Vachtsevanos George, and Orchard Marcos E. (2010). Machine remaining useful life prediction based on adaptive neuro-fuzzy and high-order particle filtering. Annual Conference of the Prognostics and Health Management Society.
Chen Chaochao, Zhang Bin, Vachtsevanos George, and Orchard Marcos (2011). “Machine condition prediction based on adaptive neuro–fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, Vol. 58, No. 9, 4353.
Coble J.B. and Hines J.H., (2008). Prognostic algorithm categorization with PHM challenge application. 2008 Int. Conf. on Prognostics and Health Management.
Coble, J., and J.W. Hines, (2010). Application of failure prognostics to the IRIS plant., Seventh American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies NPIC and HMIT 2010, Las Vegas, NV, Nov. 7-11.
Coble, Jamie, Humberstone Matt, and Hines J. Wes, (2010). Adaptive Monitoring, Fault Detection and Diagnostics, and Prognostics System for the IRIS Nuclear Plant. Annual Conference of the Prognostics and Health Management Society, Portland, Oregon, USA.
Coble, JB, Ramuhalli, P, Bond, LJ, Hines, JW, and Upadhyay, BR, (July 2012). Prognostics and health management in nuclear power plants: a review of technologies and applications. PNNL-21515, Contract DE-AC05-76RL01830, Pacific Northwest National laboratory, Richland, Washington.
Coppe A., Haftka R.T., Kim Nam-Ho, and Bes C. (2008). “A statistical model for estimating probability of crack detection. 2008 Int. Conf. on Prognostics and Health Management.
Dasgupta Abhijit, Doraiswami Ravi, Azarian Michael, Michael Osterman, Sony Mathew, and Michael Pecht, (2010). The use of “canaries” for adaptive health management of electronic systems. ADAPTIVE 2010 : The Second International Conference on Adaptive and Self-Adaptive Systems and Applications, IARIA, 2010 ISBN: 978-1-61208-109-0.
Dharmaraju, N. and Rama Rao, A. (2008). Review article: Dynamic Analysis of Coolant Channel and Its Internals of Indian 540MWe PHWR Reactor. Hindawi Publishing Corporation - Science and Technology of Nuclear Installations, Volume 2008, Article ID 764301, 7 pages, doi:10.1155/2008/764301.
Gregor, F and Chokie, Alan (2006). Ageing management and life extension in the US nuclear power industry. CGI Report 06:23, Prepared for the Petroleum Safety Authority Norway, Chokie Group International Inc, USA.
Feldman Alexander, et. al., (2010). Empirical Evaluation of Diagnostic Algorithm Performance Using Generic Framework. International Journal of Prognostics and Health Management”, 002, (ISSN 2153-2648).
Gu Jie, Vichare Nikhil, Tracy T. and Pecht, M. Prognostics implementation methods for electronics. http://www.prognostics.umd.edu/calcepapers/07_Jie_PrognosticsImplemntationmethodElectronics_RAMS.pdf.
Guan X., Liu Y., Jha R., Saxena A., Celaya J, Geobel K., (2011). Comparison of two probabilistic fatigue damage assessment approaches using prognostic performance metrics. International Journal of Prognostics and Health Management, 005, (ISSN 2153-2648).
Hashemian, H.M. On-line Monitoring and Calibration Techniques in Nuclear Power Plants. IAEA-CN-164-7S05, IAEA, Vienna. http://www-pub.iaea.org/MTCD/publications/PDF/P1500_CD_Web/htm/pdf/topic7/7S05_H.Hashemian.pdf.
Heimes F.O., (2008). Recurrent neural networks for remaining useful life estimation. 2008 Int. Conf. on Prognostics and Health Management.
Heng Aiwina, Zhang Sheng, Tan, Andi CC, and Mathew Joseph, (2009). Review – Rotating machinery prognostics: state of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, 724-739. Doi:10.1016/j.ymssp.2008.06.09.
http: //standards.ieee.org/develop/wg/PHM.html.
Hyers, R.W., McGowan, J.G., Sullivan, K.L, Manwell, J.F., and Syrett, B.C., (2006). Condition monitoring and prognosis of utility scale wind turbines, Energy Materials, Vol. 1, No. 3, 187.
IEEE Standard Association, Draft of “P-1856 – Standard framework for prognostics and health management of electronic systems, IEEE Reliability Society,
International Atomic Energy Agency (1993). Risk based optimization of technical specifications for operation of nuclear power plant. IAEA-TECDOC-729, IAEA, Vienna.
International Atomic Energy Agency (1995). Management of research reactor ageing . IAEA-TECDOC-792, IAEA, Vienna.
International Atomic Energy Agency (1999). Assessment and management of ageing of major nuclear power plant components important to safety : CANDU pressure tubes. IAEA-TECDOC-1037, IAEA, Vienna.
International Atomic Energy Agency (1999). Living probabilistic safety assessment (LPSA). IAEA-TECDOC-1106, IAEA, Vienna.
International Atomic Energy Agency (2009a). Protecting against common cause failures in digital I&C systems of nuclear power plants, IAEA-Nuclear Energy Series No NP-T-1.5, IAEA, Vienna.
International Atomic Energy Agency (2009b). Proactive management of ageing for nuclear power plants, IAEA-Safety Report Series No. 62, IAEA, Vienna.
International Atomic Energy Agency, (1998). “Assessment and management of ageing of major nuclear power plant components important to safety: CANDU pressure tubes. IAEA-TECDOC-1037, IAEA, Vienna.
International Atomic Energy Agency (2010). Risk informed in-service inspection of piping systems of nuclear power plants: process, status, issues and development. IAEA nuclear energy series no. NP-T- IAEA, Vienna.
Kadak, Andrew C., Matsuo,Toshihiro, (2007). “The nuclear industry’s transition to risk-informed regulation and operation in the United States. Reliability Engineering and System Safety 92 609–618.
Kalgren P.W, et. al., (2010). Application of prognostic health management in digital electronic systems. International Journal of Prognostics and Health Management, 002, (ISSN 2153-2648). 1-4222-0525-4/07 2007, IEEE Paper 1326 Version 3.
Klein R., Rudyk E., Masad E., and Issacharoff M., (2011). Model based approach for identification of gears and bearings failure modes. International Journal of Prognostics and Health Management”, 009, (ISSN 2153-2648).
Lee Min-Rae, Lee Joon-Hyun and Kim Jung-Teak, (2005). Condition monitoring of a nuclear power plant check valve based on acoustic emission and a neural network. Jr. of Pressure vessel Tech., issue 3, Volume 127.
Lee, WW, Nguyen, LT, and Selvaduray, (2000). Solder joint fatigue models: review and applicability to chip scale packages. Microelectronics reliability, 40, 231-244.
Lin R., Wang Z., and Sun Y., (2004). “Wireless sensor network solutions for real-time monitoring of nuclear power plants., Proc. 5th World Congress on Intelligent Controls and Automation, Vol 4, pp. 3663 – 3667.
Mathew Sony, Das Diganta, Osterman Michael, Michael Pech, (2006). Prognostics assessment of aluminum support structure on a printed circuit board. ASME Journal of Electronic Packaging, , Vol. 128 / 339.
Meyer, Ryan, Ramuhalli Pradeep, Bond L.J., (2011). Developing effective online monitoring technologies to manager service degradation of nuclear power plants. IEEE PHM Conference, Denver, Colorado, USA, 20-23, , Denver.
Mishra S., Ganesan S., Pecht, M, and Xie Jingsong,(2004). Life consumption monitoring for electronics prognostics. 2004 IEEE Aerospace Conference Proceedings.
Modarres M., Kaminskiy M. and Valiliy Kristov, (2010) “Reliability engineering and risk analysis – a practical guide” (Second Edition), CRC Press.
Nuclear Energy Agency, (2005). CSNI Technical Opinion paper No 7 on Living PSA and its Use in the Nuclear Safety Decision-making Process /No 8 on Development and Use of Risk Monitors at Nuclear Power Plants. NEA 4411, Organization for Economic Co-Operation and Development, Paris.
Patil Nishad, Das Diganta, and Pecht Michael (2012). A prognostic approach for non-punch through and field stop igbts. Microelectronics Reliability.
Patil Nishad, Das, Diganta and Pecht Michael, Jose Celaya and Goebel Kai, (2009). Precursor parameter identification for insulated gate bipolar transistor (IGBT) prognostics. IEEE Transactions on Reliability, Vol. 58, No. 2, pp. 271-276.
Pecht, M.G., (2008) “Prognostics and health management of electronics”, Wiley and Sons.
Pecht, Michael and Dasgupta, Abhijit, (1996). Physics-of-failure: an approach to reliable product development, IEEE - 95 IRW FINAL REPORT.
Pecht, M. and Gu, Jie, (2009). Physics-of-failure-based prognostics for electronic products. Transaction of the Institute of Measurements and Control, 31, 309-322.
Pecht, Michael, (2009). Prognostics and Health Management. Encyclopedia of Structural Health Monitoring, Ed. Boller Christian, Chang Fu-Kuo and Fujino Y.; John Wiley and Sons, Ltd.
Pecht, Michael G. (2010) “Prognostics and health monitoring in complex engineering systems: methods and applications”, IEICE Fundamental Reviews Vol. 3 No. 4.
Puccinelli D., Haenggi M., (2005). Wireless sensor networks : application and challenges of ubiquitous sensing. Feature Article, IEEE Circuits and Systems, Third Edition.
Samul, MK, Dutta, BK, and Kushwaha, (2010). A probabilistic approach to evaluate creep and fatigue damage in critical components. Transaction of Indian Institute of Metals, Vol. 63, Issues 2-3, pp. 595-600.
Saxena A., et. al. (2010). Metrics for Offline Evaluation of Prognostic Performance. International Journal of Prognostics and Health Management”, Vol 1(1) 001, (ISSN 2153-2648).
Saxena A., Simon D., (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. 2008 Int. Conf. on Prognostics and Health Management.
Saxena, A., “Prognostics Performance Evaluation”, National Aeronautics and Space Administration web-site: http://ti.arc.nasa.gov/tech/dash/pcoe/prognostics-performance-evaluation/metrics/algorithm-performance/.
Sinha, R.K., Sinha, S.K., Madhusoodhan, K., (2008). Fitness for service assessment of coolant channels of Indian PHWRs. Journal of Nuclear Materials, Vol. 383, Issue 1-2, pages 14-21.
Tantawy A., Koutsoukos X., and Biswas G., (2008). Aircraft ac generators: hybrid system modeling and simulation. 2008 Int. Conf. on Prognostics and Health Management.
Tsu-Mu Kao, (2007) “Risk-Informed Regulation and Applications in Taiwan” International Journal of Performability Engineering Volume 3, Number 1, - Paper 5 - pp. 47 – 59.
U.S. Nuclear Regulatory Commission, (2002). Regulatory guide 1.182 - assessing and managing risk before maintenance activities at nuclear power plants. Office of nuclear regulatory research, Washington.
Uhrig, R.E., (1994). Application of artificial neural network in industrial technology. Proc. of the 1994 IEEE International Industrial Technology Conference.
Varde, P.V. Sankar S. and Verma, A.K., (1998). An operator support system for research reactor operations and fault diagnosis through a connectionist framework and PSA based knowledge based systems. Reliability Engineering and System Safety, 60, 53 – 69.
Varghese, Joy P., Verma, V.S., Rajput, C.D., Ramamurthy, K., (2010). “En-Masse Coolant Channel Replacement in Indian PHWR. Energy Procedia 00 000-000.
Vichare, N.M. and Pech M.G.,(2006). Prognostics and Health Management of Electronics. IEEE transactions on components and packaging technologies, V:29, 1.
Wang, W., Luo S., and Pecht, M.G. Economic design of the mean prognostic distance for canary-equipped electronic systems. Microelectronics Reliability, 52 (2012) 1086-1091.
Wheeler K.R., Kurtpglu T., and Poll S.D., (2010). A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics. International Journal of Prognostics and Health Management”, 003, (ISSN 2153-2648).
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Section
Technical Papers