DOI QR코드

DOI QR Code

Abnormality diagnosis model for nuclear power plants using two-stage gated recurrent units

  • Kim, Jae Min (Ulsan National Institute of Science and Technology) ;
  • Lee, Gyumin (Ulsan National Institute of Science and Technology) ;
  • Lee, Changyong (Ulsan National Institute of Science and Technology) ;
  • Lee, Seung Jun (Ulsan National Institute of Science and Technology)
  • Received : 2019.11.27
  • Accepted : 2020.02.06
  • Published : 2020.09.25

Abstract

A nuclear power plant is a large complex system with tens of thousands of components. To ensure plant safety, the early and accurate diagnosis of abnormal situations is an important factor. To prevent misdiagnosis, operating procedures provide the anticipated symptoms of abnormal situations. While the more severe emergency situations total less than ten cases and can be diagnosed by dozens of key plant parameters, abnormal situations on the other hand include hundreds of cases and a multitude of parameters that should be considered for diagnosis. The tasks required of operators to select the appropriate operating procedure by monitoring large amounts of information within a limited amount of time can burden operators. This paper aims to develop a system that can, in a short time and with high accuracy, select the appropriate operating procedure and sub-procedure in an abnormal situation. Correspondingly, the proposed model has two levels of prediction to determine the procedure level and the detailed cause of an event. Simulations were conducted to evaluate the developed model, with results demonstrating high levels of performance. The model is expected to reduce the workload of operators in abnormal situations by providing the appropriate procedure to ultimately improve plant safety.

Keywords

References

  1. S. Lee, P. Seong, Development of an integrated decision support system to aid cognitive process of operators, Nucl. Eng. Technol. 39 (No. 6) (2007) 703-717. https://doi.org/10.5516/NET.2007.39.6.703
  2. B. Bartlett, Eric, E. Uhrig, Robert, Nuclear power plant status diagnostics using an artificial neural network, Nucl. Technol. 97 (1992) 272-281. https://doi.org/10.13182/NT92-A34635
  3. K.M. Bakhshayesh, M.B. Ghofrani, Transient identification in nuclear power plants: a review, Prog. Nucl. Energy 67 (2013) 23-32. https://doi.org/10.1016/j.pnucene.2013.03.017
  4. D.W. Miller, J.W. Hines, B.K. Hajek, L. Khartabill, C.R. Hardy, M.A. Haas, L. Robbins, Experience with the hierarchical method for diagnosis of faults in nuclear power plant systems, Reliab. Eng. Syst. Saf. 44 (3) (1994) 297-311. https://doi.org/10.1016/0951-8320(94)90020-5
  5. M. Horiguchi, N. Fukawa, K. Nishimura, Development of nuclear power plant diagnosis technique using neural networks,, in: Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, 1991, pp. 279-282. Seattle, WA, USA.
  6. Santosh G. Vinod, A.K. Babar, H.S. Kushwaha, V Venkat Raj, Symptom based diagnostic system for nuclear power plant operations using artificial neural networks, Reliab. Eng. Syst. Saf. 82 (1) (2003) 33-40. https://doi.org/10.1016/S0951-8320(03)00120-0
  7. T.V. Santosh, Gopika Vinod, R.K. Saraf, A. Ghosh, H.s Kushwaha, Application of artificial neural networks to nuclear power plant transient diagnosis, Reliab. Eng. Syst. Saf. 92 (2007) 1468-1472. https://doi.org/10.1016/j.ress.2006.10.009
  8. C.M. Rocco S, E. Zio, A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems, Reliab. Eng. Syst. Saf. 92 (Issue 5) (2007) 593-600. https://doi.org/10.1016/j.ress.2006.02.003
  9. S. Seker, E. Ayaz, E. Turkcan, Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery, Eng. Appl. Artif. Intell. 16 (7-8) (2003).
  10. R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen, J. Wang, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Trans. Ind. Electron. 65 (2) (2018) 1539-1548. https://doi.org/10.1109/tie.2017.2733438
  11. S. Lee, P. Seong, A dynamic neural network based accident diagnosis advisory system for nuclear power plants, Prog. Nucl. Energy 46 (3-4) (2005) 268-281. https://doi.org/10.1016/j.pnucene.2005.03.009
  12. K. Mo, S. Lee, P. Seong, A dynamic neural network aggregation model for transient diagnosis in nuclear power plants, Prog. Nucl. Energy 49 (3) (2007) 262-272. https://doi.org/10.1016/j.pnucene.2007.01.002
  13. M.J. Embrechts, S. Benedck, Hybrid identification of nuclear power plant transients with artificial neural networks, IEEE Trans. Ind. Electron. 51 (3) (2004) 686-693. https://doi.org/10.1109/TIE.2004.824874
  14. R.G. da Costa, A.C. de Abreu Mol, P.V.R. de Carvalho, C.M. Franklin Lapa, An efficient Neuro-Fuzzy approach to nuclear power plant transient identification, Ann. Nucl. Energy 38 (6) (2011) 1418-1426. https://doi.org/10.1016/j.anucene.2011.01.027
  15. A. Ayodeji, Y. Liu, H. Xia, Knowledge base operator support system for nuclear power plant fault diagnosis, Prog. Nucl. Energy 105 (2018) 42-50. https://doi.org/10.1016/j.pnucene.2017.12.013
  16. J. Kim, S. Lee, P. Seong, Investigation on applicability of information theory to prediction of operator performance in diagnosis tasks at nuclear power plants, IEEE Trans. Nucl. Sci. 50 (4) (2003) 1238-1252. https://doi.org/10.1109/TNS.2003.814939
  17. S. Lee, P. Seong, Development of automated operating procedure system using fuzzy colored petri nets for nuclear power plants, Ann. Nucl. Energy 31 (8) (2004) 849-869. https://doi.org/10.1016/j.anucene.2003.12.002
  18. Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Network. 5 (2) (1994) 157-166. https://doi.org/10.1109/72.279181
  19. K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning Phrase Representations Using RNN EncodereDecoder for Statistical Machine Translation, 2014 arXiv preprint arXiv:1406.1078.
  20. J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, 2014 arXiv preprint arXiv: 1412.3555.
  21. I. Jolliffe, Principal Component Analysis, second ed., Springer, 2002, pp. 1-59.
  22. 3KEYMASTER Simulator, Western Service Corporation, Frederick, MD, USA, 2013.
  23. S. Godbole, S. Sarawagi, Discriminative methods for multi-labeled classification., in: H. Dai, R. Srikant, C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2004, Lecture Notes in Computer Science, vol. 3056, Springer, Berlin, Heidelberg, 2004.

Cited by

  1. Biosignal-Based Attention Monitoring to Support Nuclear Operator Safety-Relevant Tasks vol.14, 2020, https://doi.org/10.3389/fncom.2020.596531
  2. A Sensor Fault-Tolerant Accident Diagnosis System vol.20, pp.20, 2020, https://doi.org/10.3390/s20205839
  3. Real-time prediction of nuclear power plant parameter trends following operator actions vol.186, 2020, https://doi.org/10.1016/j.eswa.2021.115848