Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Unsupervised learning algorithm for signal validation in emergency situations at nuclear power plants

  • Choi, Younhee (Department of Nuclear Engineering, Chosun University) ;
  • Yoon, Gyeongmin (Department of Nuclear Engineering, Chosun University) ;
  • Kim, Jonghyun (Department of Nuclear Engineering, Chosun University)
  • Received : 2021.03.11
  • Accepted : 2021.10.07
  • Published : 2022.04.25
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing. The algorithm aims to determine the stuck failures of signals in real time based on a variational auto-encoder (VAE), which employs unsupervised learning, and long short-term memory (LSTM). The application of unsupervised learning enables the algorithm to detect a wide range of stuck failures, even those that are not trained. First, this paper discusses the potential failure modes of signals in NPPs and reviews previous studies conducted on signal validation. Then, an algorithm for detecting signal failures is proposed by applying LSTM and VAE. To overcome the typical problems of unsupervised learning processes, such as trainability and performance issues, several optimizations are carried out to select the inputs, determine the hyper-parameters of the network, and establish the thresholds to identify signal failures. Finally, the proposed algorithm is validated and demonstrated using a compact nuclear simulator.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (Ministry of Science and ICT) (No. 2018M2B2B1065651 and NRF-2016R1A5A1013919).

References

  1. H. Basher, J. Neal, L. Ut-Battelle, Autonomous Control of Nuclear Power Plants, United States. Department of Energy, 2003.
  2. P. Le Bot, Human reliability data, human error and accident models-illustration through the Three Mile Island accident analysis, Reliab. Eng. Syst. Saf. 83 (2) (2004) 153-167. https://doi.org/10.1016/j.ress.2003.09.007
  3. B. Upadhyaya, T.W. Kerlin, P. Gaudio Jr., Development and Testing of an Integrated Signal Validation System for Nuclear Power Plants, Tennessee Univ., Knoxville, 1989. Combustion Engineering, Inc., Stamford, CT (USA).
  4. M. Weightman, The Great East Japan Earthquake Expert Mission, IAEA International Fact Finding Expert Mission of the Fukushima Dai-Ichi NPP Accident Following the Great East Japan Earthquake and Tsunami, Mission Report IAEA, 2011.
  5. F. Daiichi, Ans Committee Report, A Report by The American Nuclear Society Special Committee on Fukushima, 2012.
  6. J.-E. Yang, Fukushima Dai-Ichi accident: lessons learned and future actions from the risk perspectives, Nuclear Engineering and Technology 46 (1) (2014) 27-38. https://doi.org/10.5516/NET.03.2014.702
  7. J. Gertler, Fault detection and isolation using parity relations, Control Eng. Pract. 5 (1997) 653-661, 5. https://doi.org/10.1016/S0967-0661(97)00047-6
  8. P. Fantoni, A. Mazzola, A pattern recognition-artificial neural networks based model for signal validation in nuclear power plants, Ann. Nucl. Energy 23 (13) (1996) 1069-1076. https://doi.org/10.1016/0306-4549(96)84661-5
  9. J. Choi, S.J. Lee, Consistency index-based sensor fault detection system for nuclear power plant emergency situations using an LSTM network, Sensors 20 (2020) 1651, 6. https://doi.org/10.3390/s20061651
  10. X. Xu, J.W. Hines, R.E. Uhrig, Sensor validation and fault detection using neural networks, in: Proc. Maintenance and Reliability Conference (MARCON 99), of Conference, 1999.
  11. J.W. Hines, R.E. Uhrig, D.J. Wrest, Use of autoassociative neural networks for signal validation, J. Intell. Rob. Syst. 21 (2) (1998) 143-154. https://doi.org/10.1023/A:1007981322574
  12. S.G. Kim, Y.H. Chae, P.H. Seong, Signal fault identification IN nuclear power plants based ON DEEP neural networks, Annals of DAAAM & Proceedings (2019) 846-853.
  13. F. Di Maio, et al., Fault detection in nuclear power plants components by a combination of statistical methods, IEEE Trans. Reliab. 62 (2013) 833-845, 4. https://doi.org/10.1109/TR.2013.2285033
  14. C. Yoo, et al., Sensor validation and reconciliation for a partial nitrification process, Water Sci. Technol. 53 (4-5) (2006) 513-521. https://doi.org/10.2166/wst.2006.155
  15. W. Li, et al., Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method, Ann. Nucl. Energy 113 (2018) 105-117. https://doi.org/10.1016/j.anucene.2017.11.009
  16. N. Kaistha, B.R. Upadhyaya], Incipient fault detection and isolation of field devices in nuclear power systems using principal component analysis, Nucl. Technol. 136 (2) (2001) 221-230. https://doi.org/10.13182/nt01-a3240
  17. P. Baraldi, et al., An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control, Ann. Nucl. Energy 37 (6) (2010) 778-790. https://doi.org/10.1016/j.anucene.2010.03.002
  18. H. Albazzaz, X.Z. Wang, Statistical process control charts for batch operations based on independent component analysis, Ind. Eng. Chem. Res. 43 (21) (2004) 6731-6741. https://doi.org/10.1021/ie049582+
  19. N. Zavaljevski, K.C. Gross, Sensor Fault Detection in Nuclear Power Plants Using Multivariate State Estimation Technique and Support Vector Machines, Argonne National Lab., Argonne, IL (US), 2000.
  20. E. Zio, F. Di Maio, A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system, Reliab. Eng. Syst. Saf. 95 (1) (2010) 49-57. https://doi.org/10.1016/j.ress.2009.08.001
  21. P. Baraldi, et al., Comparison of data-driven reconstruction methods for fault detection, IEEE Trans. Reliab. 64 (3) (2015) 852-860. https://doi.org/10.1109/TR.2015.2436384
  22. I. Hwang, et al., A survey of fault detection, isolation, and reconfiguration methods, IEEE Trans. Control Syst. Technol. 18 (3) (2009) 636-653. https://doi.org/10.1109/TCST.2009.2026285
  23. A.A. Zuniga, et al., Classical failure modes and effects analysis in the context of smart grid cyber-physical systems, Energies 13 (5) (2020) 1215. https://doi.org/10.3390/en13051215
  24. J. An, S. Cho, Variational autoencoder based anomaly detection using reconstruction probability, Special Lecture on IE 2 (1) (2015) 1-18.
  25. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8) (1997) 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  26. J. Yang, J. Kim, Accident Diagnosis Algorithm with Untrained Accident Identification during Power-Increasing Operation, vol. 202, Reliability Engineering & system safety, 2020, p. 107032.
  27. J. Yang, J. Kim, An accident diagnosis algorithm using long short-term memory, Nuclear Engineering and Technology 50 (4) (2018) 582-588. https://doi.org/10.1016/j.net.2018.03.010
  28. F.A. Gers, J. Schmidhuber, F. Cummins, Learning to forget: continual prediction with LSTM, Neural Comput. 12 (10) (2000) 2451-2471. https://doi.org/10.1162/089976600300015015
  29. D. Lee, A.M. Arigi, J. Kim, Algorithm for autonomous power-increase operation using deep reinforcement learning and a rule-based system, IEEE Access 8 (2020) 196727-196746. https://doi.org/10.1109/access.2020.3034218
  30. H. Kim, A.M. Arigi, J. Kim, Development of a diagnostic algorithm for abnormal situations using long short-term memory and variational autoencoder, Ann. Nucl. Energy 153 (2021) 108077. https://doi.org/10.1016/j.anucene.2020.108077
  31. KAERI, Advanced Compact Nuclear Simulator Textbook, Nucl. Training Center Korea At. Energy Res. Inst., Daejeon, South Korea, 1990.
  32. H. Xu, Y. Deng, Dependent evidence combination based on shearman coefficient and pearson coefficient, IEEE Access 6 (2017) 11634-11640. https://doi.org/10.1109/access.2017.2783320
  33. H.Z. Nazir, et al., Quality quandaries: a stepwise approach for setting up a robust Shewhart location control chart, Qual. Eng. 26 (2) (2014) 246-252. https://doi.org/10.1080/08982112.2013.874562