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Development of Nuclear Power Plant Instrumentation Signal Faults Identification Algorithm

원전 계측 신호 오류 식별 알고리즘 개발

  • 김승근 (한국원자력연구원 미래전략본부 지능형컴퓨팅연구실)
  • Received : 2020.09.28
  • Accepted : 2020.12.04
  • Published : 2020.12.31

Abstract

In this paper, the author proposed a nuclear power plant (NPP) instrumentation signal faults identification algorithm. A variational autoencoder (VAE)-based model is trained by using only normal dataset as same as existing anomaly detection method, and trained model predicts which signal within the entire signal set is anomalous. Classification of anomalous signals is performed based on the reconstruction error for each kind of signal and partial derivatives of reconstruction error with respect to the specific part of an input. Simulation was conducted to acquire the data for the experiments. Through the experiments, it was identified that the proposed signal fault identification method can specify the anomalous signals within acceptable range of error.

본 논문에서는 원전 비상 상황 발생 시 다수의 신호 오류가 발생했을 때 어떤 신호에 오류가 발생했는지를 추정하는 신호 오류 식별 (Fault identification) 방법론을 개발하였다. 변분 오토인 코더 (Variational autoencoder; VAE) 기반 모델은 기존의 이상 탐지 방법론과 같이 정상 신호 데이터만을 이용하여 훈련이 진행되며, 이후 각 신호에 대한 복원 오차 (Reconstruction error)와 복원 오차를 입력의 특정 부분으로 미분한 값을 이용하여 어떤 부분에 오류가 포함되어 있는지를 예측한다. 데이터 취득을 위하여 시뮬레이션을 수행하였으며, 일련의 실험으로부터 제시한 신호 오류 식별 방법이 적절한 오차 범위 내에서 오류가 발생한 신호를 특정할 수 있음을 확인하였다.

Keywords

Acknowledgement

본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제임(No. 20171510102040)

References

  1. An, J. W., and Cho, S. Z. (2015). Variational autoencoder based anomaly detection using reconstruction probability, Special Lecture on IE, SNU Data Mining Center, 2, pp. 1-18.
  2. Fantoni, P. F. (2000). A neuro-fuzzy model applied to full range signal validation of PWR nuclear power plant data, International J ournal of General Systems, 29(2), 305-320. https://doi.org/10.1080/03081070008960935
  3. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets, Advances in Neural Information rocessing Systems 27 (NIPS 2014), Dec. 8-13, Montreal, Canada.
  4. Hines, J. W., Uhrig, R. H., and Wrest, D. J. (1998). Use of autoassociative neural networks for signal validation, Journal of Intelligent and Robotic Systems, 21, 143-154. https://doi.org/10.1023/A:1007981322574
  5. Kim, S. G., Chae, Y. H., and Seong, P. H. (2020). Development of a generative - adversarial - network - based signal reconstruction method for nuclear power plants, Annals of Nuclear Energy, 142, Article 107410.
  6. Kim, S. G., No, Y. G., and Seong, P. H. (2015). Prediction of severe accident occurrence time using support vector machines, Nuclear Engineering and Technology, 47(1), 74-84. https://doi.org/10.1016/j.net.2014.10.001
  7. Kim, S. S., Kim, J. I., and Jung, K. C. (2019). Portfolio system using deep learning, Journal of the Korea Industrial Information Systems Research, 24(1), 23-30. https://doi.org/10.9723/JKSIIS.2019.24.1.023
  8. Kim, S. S., and Hong, K. J. (2017). Development and performance analysis of predictive model for KOSPI 200 index using recurrent neural networks, Journal of the Korea Industrial Information Systems Research, 22(6), 23-29. https://doi.org/10.9723/JKSIIS.2017.22.6.023
  9. Kingma, D. P., and Welling, M. (2014). Auto-encoding variational Bayes, arXiv: 1312.6114 [stat] https://arxiv.org/abs/1312.6114 (Accessed on Dec. 07th, 2020).
  10. Kingma, D. P., and Ba, J. L. (2014). Adam: a method for stochastic optimization, arXiv: 1412.6980v9 [cs.LG] https://arxiv.org/abs/1412.6980 (Accessed on Dec. 07th, 2020).
  11. Korea Atomic Energy Research Institute (1990). Advanced Compact Nuclear Simulator Textbook, Nuclear Training Center in Korea Atomic Energy Research Institute.
  12. Lim, D. H., Lee, S. H., and Na, M. G. (2010). Smart soft-sensing for the feedwater flowrate at PWRs using a GMDH algorithm, IEEE Transactions on Nuclear Science, 57(1), 340-347. https://doi.org/10.1109/TNS.2009.2035121
  13. Minar, R. M., Tuan, T. T., and Ahn, H. J. (2020). An Improved VTON (Virtual-try-on) algorithm using a [air of cloth and human image, Journal of the Korea Industrial Information Systems Research, 25(2), 11-18. https://doi.org/10.9723/JKSIIS.2020.25.2.011
  14. Na, M. G., Park, W. S., and Lim, D. H. (2008). Detection and diagnostics of loss of coolant accident using support vector machines, IEEE Transactions on Nuclear Science, 55(1), 628-636. https://doi.org/10.1109/TNS.2007.911136
  15. Nair, A. M., and Coble, J. (2017). Bayesian inference for high confidence signal validation and sensor calibration assessment, ANS 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, Jun. 11-15, San Francisco, CA, pp. 1688-1697.
  16. No, Y. G., Lee, C. Y., and Seong, P. H. (2018). Development of a prediction method for SAMG entry time in NPPs using the extended group method of data handling (GMDH) model, Annals of Nuclear Energy, 121, 552-556. https://doi.org/10.1016/j.anucene.2018.08.019
  17. No, Y. G., and Seong, P. H. (2016). Smart-sensing of the aux. feed-water pump performance in NPP severe accidents using advanced GMDH method, Proceedings of the KNS 2016 Spring Meeting, May 11-13. Jeju, Republic of Korea.
  18. Shaheryar, A., Yin, X., Hao, H., Mahmood, Z., and Abuassba, A. (2018). Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model, Artificial Intelligence, 50(3), pp. 341-382.
  19. Shaheryar, A., Yin, X., Hao, H., Ali, H., and Iqbal, K. (2016). A Denoising based autoassociative model for robust sensor monitoring in nuclear power plants, Science and Technology of Nuclear Installations, https://doi.org/10.1155/2016/9746948.
  20. Yang, J. E. (2014). Fukushima Dai-ichi accident: lessons learned and future actions from the risk perspectives, Nuclear Engineering and Technology, 46(1), 27-38. https://doi.org/10.5516/NET.03.2014.702