Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Modified parity space averaging approaches for online cross-calibration of redundant sensors in nuclear reactors

  • Kassim, Moath (Department of Nuclear Engineering, Kyung Hee Univ.) ;
  • Heo, Gyunyoung (Department of Nuclear Engineering, Kyung Hee Univ.)
  • Received : 2018.02.01
  • Accepted : 2018.03.13
  • Published : 2018.05.25
Download PDF
⟨ Previous Next ⟩

Abstract

To maintain safety and reliability of reactors, redundant sensors are usually used to measure critical variables and estimate their averaged time-dependency. Nonhealthy sensors can badly influence the estimation result of the process variable. Since online condition monitoring was introduced, the online cross-calibration method has been widely used to detect any anomaly of sensor readings among the redundant group. The cross-calibration method has four main averaging techniques: simple averaging, band averaging, weighted averaging, and parity space averaging (PSA). PSA is used to weigh redundant signals based on their error bounds and their band consistency. Using the consistency weighting factor (C), PSA assigns more weight to consistent signals that have shared bands, based on how many bands they share, and gives inconsistent signals of very low weight. In this article, three approaches are introduced for improving the PSA technique: the first is to add another consistency factor, so called trend consistency (TC), to include a consideration of the preserving of any characteristic edge that reflects the behavior of equipment/component measured by the process parameter; the second approach proposes replacing the error bound/accuracy based weighting factor ($W^a$) with a weighting factor based on the Euclidean distance ($W^d$), and the third approach proposes applying $W^d$, TC, and C, all together. Cold neutron source data sets of four redundant hydrogen pressure transmitters from a research reactor were used to perform the validation and verification. Results showed that the second and third modified approaches lead to reasonable improvement of the PSA technique. All approaches implemented in this study were similar in that they have the capability to (1) identify and isolate a drifted sensor that should undergo calibration, (2) identify a faulty sensor/s due to long and continuous missing data range, and (3) identify a healthy sensor.

Keywords

References

  1. X. Jin, A. Ray, R.M. Edwards, Redundant Sensor Calibration and Estimation for Monitoring and Control of Nuclear Power Plants, Tran. of ANS 2009 Winter Meeting, Washington, DC, 2009.
  2. H.M. Hashemian, On-line Monitoring and Calibration Techniques in Nuclear Power Plants, Opportunities and Challenges for Water Cooled Reactors in the 21st Century, 2009. Vienna, Austria.
  3. H.M. Hashemian, D.W. Mitchell, M.S. Corporation, On-line Testing of Calibration of Process Instrumentation Channels in Nuclear Power Plants, International Topical Meeting on Nuclear Thermal Hydraulics, Operations and Safety, Taipei, Taiwan, 1994.
  4. R. Shankar, On Line Monitoring of Instrument Channel Performance - Volume 3: Applications to Nuclear Power Plant Technical Specification Instrumentation, Electric Power Research Institute, 2004, 3(3).
  5. B.Wooten, R. Colley, Instrument Calibration andMonitoring Program: Volume1, Basic for the Method, Electric Power Research Institute (TR-103436-V1), 1993.
  6. J.W. Hines, J. Garvey, D.R. Garvey, R. Seibert, Technical Review of On-Line Monitoring Techniques for Performance Assessment Volume 3: Limiting Case Studies, U.S. Nuclear Regulatory Commission (NUREG/CR-6895), 2007.
  7. H.M. Hashemian, Maintenance of Process Instrumentation in Nuclear Power Plants, Springer, 2006.
  8. H.M. Hashemian, Sensor Performance and Reliability, ISA - The Instrumentation, Systems, and Automation Society, 2005.
  9. J. Eiler, O. Glockler, On-line Monitoring for Improving Performance of Nuclear Power Plants. Part 1, Instrument Channel Monitoring, International Atomic Energy Agency, 2008.
  10. M. Kassim, G. Heo, Online cross calibration technique considering missing data for redundant sensors in research reactor, Proceedings of the IEEK, 2016, pp. 71-74.
  11. A. Ray, M. Desai, A redundancy management procedure for fault detection and isolation, ASME J. Dyn. Syst. Meas. Control 108 (3) (1986) 248-254. https://doi.org/10.1115/1.3143774
  12. E. Trucco, A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall, 1998.
  13. M. Kassim, G. Heo, Development of Cross Moving Median Filter for Denoising and Data Reconstruction in Nuclear Reactor, Transactions of the Korean Nuclear Society Autumn Meeting, Gyeongju Korea, 2017.
  14. J.W. Hines, R. Seibert, Technical Review of On-Line Monitoring Techniques for Performance Assessment. Volume 1. State-of-the-Art, U.S.Nuclear Regulatory Commission (NUREG/CR-6895), 2006.
  15. J.G. Ramirez, Statistical Intervals: Confidence, Prediction, Enclosure, Statistical Analysis System Institute, 2009.
  16. D.S. Moore, W. Notz, M.A. Fligner, The Basic Practice of Statistics, W.H. Freeman and Co, 2013.
  17. Pbarrett.net, Euclidean Distance Whitepaper, 2005. The Technical Whitepaper Series 6.
  18. R. Hammack, Book of Proof, Virginia Commonwealth University, 2013.
  19. J.W. Hines, P. Rebstock, On-Line Monitoring for Calibration Extension: An Overview and Introduction (See NUREG/CR-6895 for Details), University of Tennessee, 2009.
  20. J.W. Hines, B. Rasmussen, Online sensor calibration monitoring uncertainty estimation, Nucl. Technol. 151 (3) (2005) 281-288. https://doi.org/10.13182/NT05-A3650
  21. P. Ramuhalli, B.G. Braatz, G. Lin, J.B. Coble, S.L. Crawford, B.D. Shumaker, Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants, U.S.Department of Energy, 2014.
  22. G. Keller, Managerial Statistics, South-Western Cengage Learning, 2012.
  23. R. Seibert, D.R. Gravey, J.W. Hines, Prediction Intervals Versus Confidence Intervals for On-line Sensor Monitoring. The University of Tennessee, Personal Contact.
  24. A.C. Tamhane, D.D. Dunlop, Statistics and Data Analysis from Elementary to Intermediate, Prentice Hall, 2000.
  25. C.C. Aggarwal, Outlier Analysis, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA, 2016, pp. 1-28.
  26. S.I. Wu, Y.K. Kim, K.H. Lee, H.R. Kim, Design characteristics of the cold neutron source in HANARO process system and architecture design, International Conference on Research Reactor, Sydney, Australia, 2007.
  27. Sung Ho Ahn, Seong Ho Ahn, Jung Hwan Sung, Young Gi Kim, Fundamental Design of Systems and Facilities for Cold Neutron Source in the HANARO, Korea Atomic Energy Research Institute (KAERI/TR-3126), 2006.
  28. E. Gassmann, Pressure-sensor fundamentals: interpreting accuracy and error, Chem. Eng. Prog. 110 (6) (2014) 37-45.