Nuclear Engineering and Technology

  • 제40권1호
  • /
  • Pages.49-68
  • /
  • 2008
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  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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CONDITION MONITORING USING EMPIRICAL MODELS: TECHNICAL REVIEW AND PROSPECTS FOR NUCLEAR APPLICATIONS

  • Heo, Gyun-Young (Department of Nuclear Engineering, Kyung Hee University)
  • 발행 : 2008.02.29
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초록

The purpose of this paper is to extensively review the condition monitoring (CM) techniques using empirical models in an effort to reduce or eliminate unexpected downtimes in general industry, and to illustrate the feasibility of applying them to the nuclear industry. CM provides on-time warnings of system states to enable the optimal scheduling of maintenance and, ultimately, plant uptime is maximized. Currently, most maintenance processes tend to be either reactive, or part of scheduled, or preventive maintenance. Such maintenance is being increasingly reported as a poor practice for two reasons: first, the component does not necessarily require maintenance, thus the maintenance cost is wasted, and secondly, failure catalysts are introduced into properly working components, which is worse. This paper first summarizes the technical aspects of CM including state estimation and state monitoring. The mathematical background of CM is mature enough even for commercial use in the nuclear industry. Considering the current computational capabilities of CM, its application is not limited by technical difficulties, but by a lack of desire on the part of industry to implement it. For practical applications in the nuclear industry, it may be more important to clarify and quantify the negative impact of unexpected outcomes or failures in CM than it is to investigate its advantages. In other words, while issues regarding accuracy have been targeted to date, the concerns regarding robustness should now be concentrated on. Standardizing the anticipated failures and the possibly harsh operating conditions, and then evaluating the impact of the proposed CM under those conditions may be necessary. In order to make the CM techniques practical for the nuclear industry in the future, it is recommended that a prototype CM system be applied to a secondary system in which most of the components are non-safety grade. Recently, many activities to enhance the safety and efficiency of the secondary system have been encouraged. With the application of CM to nuclear power plants, it is expected to increase profit while addressing safety and economic issues.

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참고문헌

  1. SmartSignal Corporation, The Value of Predictive Analysis for Fossil-Fuel Power Plants, Product Brochure (2006)
  2. J. R. Koelsch, 'In Condition Monitoring, Early is Better,' On-line Publication at http://www.automationworld.com (2006)
  3. J. R. Koelsch, 'Profit from Condition Monitoring,' Online Publication at http://www.automationworld.com (2006)
  4. L. G. Paul, 'A New Schedule for Asset Monitoring,' Online Publication at http://managingautomation.com (2007)
  5. AID Corporation, On-line Early Detection, Product Brochure (2007)
  6. H. M. Hashemian, 'On-Line Testing of Calibration of Process Instrumentation Channels in Nuclear Power Plants,' NUREG/CR-6343, US Nuclear Regulatory Commission (1995)
  7. J. W. Hines and R. Seibert, 'Technical Review of On-Line Monitoring Techniques for Performance Assessment,' NUREG/CR-6895, US Nuclear Regulatory Commission (2006)
  8. J. W. Hines and E. Davis, 'Lessons Learned From the US Nuclear Power Plant On-Line Monitoring Programs,' Prog. Nucl. Ener., 46, 176 (2005) https://doi.org/10.1016/j.pnucene.2005.03.003
  9. R. Uhrig and J. W. HINES, 'Computational Intelligence in Nuclear Engineering,' Nucl. Eng. Tech., 37. 127 (2005)
  10. J. Mott, 'Pattern recognition software for plant surveillance,' Performance Software User's Group Meeting, King of Prussia, USA, June 1988
  11. SmartSignal Corporation, EPI*$Center^{TM}$, Product Brochure (2007)
  12. G. Welch and G. Bishop, 'Technical Course 8: An Introduction to the Kalman Filter,' The Association for Computing Machinery SIGGRAPH 2001, Los Angeles, USA, Aug. 12-17, 2001
  13. D. Simon, Optimal State Estimation: Kalman, H infinity, and Nonlinear Approaches, John Wiley & Sons, New York, (2006)
  14. J. Wakabayashi, A. Fukumoto, S. Tashima, and I. Kawahara, 'Application of Adaptive Kalman Filtering Technique for the Diagnostic System of Nuclear Power Plants,' IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, Albuquerque, USA Dec. 10-12, 1980
  15. A. Racz, 'Detection of Small Leakages by a Combination of Dedicated Kalman Filters and an Extended Version of the Binary Sequential Probability Ratio Test,' Nuc. Tech., 104, 128 (1993) https://doi.org/10.13182/NT93-A34875
  16. M. V. Kothare, B. Mettler, M. Morari, P. Bendotti, and C. Falinower, 'Level Control in the Steam Generator of a Nuclear Power Plant,' IEEE Trans. Cont. Syst. Tech., 8, 55 (2000) https://doi.org/10.1109/87.817692
  17. N. R. Draper and H. Smith, Applied Regression Analysis, 3rd, Wiley, New York (1998)
  18. G. P. McCabe and D.S. Moore, Introduction to the Practice of Statistics, 5th, W.H. Freeman & Company, New York (2005)
  19. H. Wolfgang, M. Mariene and S. Stefa, Nonparametric and Semiparametric Models, Springer Verlag, Berlin (2004)
  20. K. Teknomo, 'Kernel Regression,' On-line Publication at http://people.revoledu.com/kardi/tutorial/Regression/Kern elRegression/index.html
  21. V. Cherkassky and F. Mulier, Learning from Data, Concept, Theory and Methods, 2nd, John Wiley and Sons, Hoboken (2007)
  22. R. Koenker, Econometic Theory, Lecture Note at University of Illinois (2007)
  23. P. F. Doruska, Methods for Quantitatively Describing Tree Crown Profiles of Loblolly Pine, Ph.D. Dissertation at Virginia Polytechnic Institute and State University (1998)
  24. K. C. Gross, R. M. Singer, S. W. Wegerich, J. Mott, and E. Hansen, 'Multivariate State Estimation Technique (MSET) Based Surveillance System,' U.S. Patent 5,764,509 (1998)
  25. N. Zavaljevski and K. C. Gross, 'Sensor Fault Detection in Nuclear Power Plants Using Multivariate Estimation Technique and Support Vector Machines,' Int. Conf. Yugoslav Nuclear Society, Belgrade, Yugoslavia, Oct. 2-5, 2000
  26. H. Bozdogan, Statistical Data Mining and Knowledge Discovery, p. 217, Chapman and Hall/CRC Press, Boca Raton, (2004)
  27. J. W. Hines and A. Usynin, 'MSET Performance Optimization Through Regularization,' Nucl. Eng. Tech., 37, 177 (2005)
  28. J. M. Zurada, Introduction to Artificial Neural Systems, West Publishing Company, St. Paul (1992)
  29. S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, Englewood Cliffs (1994)
  30. B. R. Upadhyaya and E. Eryurek, 'Application of Neural Networks for Sensor Validation and Plant Monitoring,' Nucl. Tech., 97, 170 (1992) https://doi.org/10.13182/NT92-A34613
  31. S. W. Cheon and S. H. Chang, 'Application of Neural Networks to a Connectionist Expert System for Transient Identification in Nuclear Power Plants,' Nucl. Tech., 102, 177 (1993) https://doi.org/10.13182/NT93-A34815
  32. Y. Bartal, J. Lin, and R. E. Uhrig, 'Nuclear Power Plant Transient Diagnostics Using Artificial Neural Networks that Allow 'Don't-know' Classifications,' Nucl. Tech., 110, 436 (1995) https://doi.org/10.13182/NT95-A35112
  33. G. Guglielmi, T. Parisini, and G. Rossi, 'Fault Diagnosis and Neural Networks: a Power Plant Application,' Cont. Eng. Prac., 3, 601 (1995) https://doi.org/10.1016/0967-0661(95)00037-U
  34. P. F. Fantoni and A. Mazzola, 'A Pattern Recognition: Artificial Neural Network Based Model for Signal Validation in Nuclear Power Plants,' Annals of Nuclear Energy, 23, 1069 (1996) https://doi.org/10.1016/0306-4549(96)84661-5
  35. P. F. Fantoni and A. Mazzola, 'Multiple-Failure Signal Validation in Nuclear Power Plants using Artificial Neural Networks,' Nucl. Tech., 113, 368 (1996) https://doi.org/10.13182/NT96-A35216
  36. M. G. Na, S. H. Shin, D. W. Jung, S. P. Kim, J. H. Jeong, and B. C. Lee, 'Estimation of Break Location and Size for Loss of Coolant Accidents Using Neural Networks,' Nucl. Eng. Des, 232, 289 (2004) https://doi.org/10.1016/j.nucengdes.2004.06.007
  37. M. A. Kramer, 'Nonlinear Principal Component Analysis using Auto Associative Neural Networks,' AIChE Jour., 37, 233 (1991) https://doi.org/10.1002/aic.690370209
  38. Y. G. Du, D. Hodouin, and J. Thibault, 'Use of a Novel Autoassociative Neural Network for Nonlinear Steady- State Data Reconciliation,' AIChE Jour., 43, 1785 (1997) https://doi.org/10.1002/aic.690430714
  39. G. Heo, 'Thermal Power Estimation Based on a Neural Network and Principal Component Analysis in Nuclear Power Plants,' Master Thesis at KAIST (1998)
  40. B. Rasmussen and J. W. Hines, 'Uncertainty Estimation for Empirical Signal Validation Modeling,' International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology, Columbus, USA, Sep. 12-22, 2004
  41. P. F. Fantoni, S. Figedy, and A. Racz, 'PEANO, A Toolbox for Real-time Process Signal Validation and Estimation,' OECD Halden Reactor Project HWR-515, Feb. 1998
  42. C. Vitanza, 'Overview of the OECD-Halden Reactor Project,' Nucl. Eng. Des., 207, 207, (2001) https://doi.org/10.1016/S0029-5493(00)00384-8
  43. P. F. Fantoni, 'Experiences and Applications of PEANO for Online Monitoring in Power Plants,' Prog. Nucl. Ener. 46, 206, (2005) https://doi.org/10.1016/j.pnucene.2005.03.005
  44. A. Wald, Sequential Analysis, Dover Publications, Mineola (1975)
  45. S. R. Kim, Sequential Probability Ratio Test, Arche Publishing House, Seoul (1999)
  46. G. Heo, 'Development of a Fouling Monitoring for Safety-Related Heat Exchanger,' Nucl. Eng. Des., Pending Review
  47. I. Parzit, 'Sequential Tests for Failure Detection,' Ann. Nucl. Ener., 17, 347 (1990) https://doi.org/10.1016/0306-4549(90)90072-L
  48. K. C. Gross and K. E. Humenik, 'Sequential Probability Ratio Test for Nuclear Plant Component Surveillance,' Nucl. Tech., 93, 131 (1991) https://doi.org/10.13182/NT91-A34499
  49. M. G. Na, 'A Neuro-Fuzzy Inference System for Sensor Failure Detection Using Wavelet Denosing, PCA and SPRT,' Jour. Korean Nucl. Soc., 33, 483 (2001)
  50. R. L. Bickford, Surveillance System and Method Having an Adaptive Sequential Probability Fault Detection Test, US 7,082,379, (2006)
  51. D. S. Bai, Statistical Quality Management, Youngchi, Seoul (1992)
  52. P. C. Badavas, Real-Time Statistical Process Control, Prentice Hall PTR, Upper Saddle River (1992)
  53. T. Kourti and J. F. MacGregor, 'Multivariate SPC Methods for Process and Product Monitoring,' Jour. Qual. Tech., 28, 409, (1996) https://doi.org/10.1080/00224065.1996.11979699
  54. A. Raich and A. Cinar, 'Statistical Process Monitoring and Disturbance Diagnosis in Multivariate Continuous Processes,' AIChE Jour., 42, 995 (1996) https://doi.org/10.1002/aic.690420412
  55. J. W. Hines and D. Garvey, 'Process and Equipment Monitoring Methodologies Applied to Sensor Calibration Monitoring,' Qual. Reli. Eng. Int., 23, 123, (2007) https://doi.org/10.1002/qre.818
  56. Heat Exchange Institute (HEI), Standards for Power Plant Heat Exchangers, HEI (1998)
  57. Electric Power Research Institute (EPRI), Heat Exchanger Performance Monitoring Guideline, NP-7552, EPRI (1991)
  58. SmartSignal Corporation, Early Warning of Reactor Coolant Pump Seal Degradation, Product Brochure (2003)
  59. S. H. An, Y. H. Jeong, and S. H. Chang, 'Development of Nuclear Power Plant Online Monitoring System using Statistical Quality Control,' Proc. American Nucl. Soc., Boston, USA, June 24-28, 2007
  60. W. A. Campbell, Performance Test Codes 6 on Steam Turbine, American Society of Mechanical Engineers (ASME), New York (1996)
  61. G. Heo, S. J. Lee, S. H. Chang, and S. S. Choi, 'Thermal Performance Test through On-line Turbine Cycle Performance Monitoring in Nuclear Power Plants,' Proc. Korean Nucl. Soc., Seoul, Korea, Oct. 1999
  62. G. C. Alder, B. Blakeley, D. R. Fleming, W. C. Kettenacker, and G. L. Minner, Manuals for $PEPSE{\circledR}$ and $PEPSE{\circledR}$-GT, ScienTech, Idaho Falls (1999)
  63. D. J. Kukulka, 'An Evaluation of Heat Transfer Surface Materials Used in Fouling Applications,' Heat Tran. Eng., 26, 42 (2005)
  64. M. Khadem, A. Ipakchi, F. J. Alexandro, and R.W. Colley, 'Application of Neural Networks to Validation of Feedwater Flowrate in a Nuclear Power Plant,' Proc. American Power Conf., Chicago, USA, Apr. 13-15, 1993
  65. K. Kavaklioglu and B. R. Upadhyaya, 'Monitoring Feedwater Flow Rate and Component Thermal Performance of Pressurized Water Reactors by Means of Artificial Neural Networks,' Nucl. Tech., 107, 112 (1994) https://doi.org/10.13182/NT94-A35003
  66. G. Heo, S. S. Choi, and S. H. Chang, 'Thermal Power Estimation by Fouling Phenomena Compensation Using Wavelet and Principal Component Analysis,' Nucl. Eng. Des., 199, 31 (2000) https://doi.org/10.1016/S0029-5493(00)00249-1

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