Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Retrieval methodology for similar NPP LCO cases based on domain specific NLP

  • No Kyu Seong (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Jae Hee Lee (KHNP Central Research Institute) ;
  • Jong Beom Lee (KHNP Central Research Institute) ;
  • Poong Hyun Seong (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2022.06.17
  • Accepted : 2022.09.26
  • Published : 2023.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP. There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.

Keywords

References

  1. https://www.nrc.gov/reading-rm/basic-ref/glossary/technical-specifications.html. 
  2. U.S., Nuclear regulatory commission (NRC), standard technical specifications combustion engineering plants revision 4.0 volume 1, specifications NUREG1432-U.S, Nucl. Regul. Commission (2012) 1. 
  3. http://opis.kins.re.kr/. 
  4. M. Ragheb, M. Abdelhai, Model-Based Analysis System for Tracking of Limiting Conditions for Operation and Surveillance Requirements in Power Plants. Applications of Artificial Intelligence V, International Society for Optics and Photonics, 1987, pp. 138-145. 
  5. M. Ragheb, M. Abdelhai, J. Cook, Producton-rule analysis system for nuclear plant technical specifications tracking, in: Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry, Springer, Boston, MA, 1988, pp. 673-680. 
  6. L.M. Lidsky, et al., The use of prolog for computerized technical specifications, in: Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry, Springer, Boston, MA, 1988, pp. 681-688. 
  7. W.H. Schlegelmilch, R.P. Jefferis, Interface of an expert system with on-line data for technical specification monitoring, in: Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry, Springer, Boston, MA, 1988, pp. 715-720. 
  8. U. Fiedler, S. Schalm, K. Pranckeviciute, Knowledge Based Management of Technical Specifications in the Nuclear Industry, 1992, p. 131. 
  9. G.V. Paiva, R. Schirru, Application of an Expert System for Real Time Diagnosis of the Limiting Conditions for Operation in Nuclear Power Plants, 2015. 
  10. A. D. Schechter, A.S. Nicolau and R. Schirru, Expert System Applied at the Angra-2 Nuclear Power Plant to Assist in the Resolution of Limiting Conditions for Operation. 
  11. A. Oluwasegun, J.C. Jung, The application of machine learning for the prognostics and health management of control element drive system, Nucl. Eng. Technol. 52 (10) (2020) 2262-2273.  https://doi.org/10.1016/j.net.2020.03.028
  12. C. Corrales, A. Fuentes, F. Ortega, A New APDS Approach-Alarm Processing Based on Alarm Prioritization-201, vol. 555, American Nuclear Society-ANS, La Grange Park, IL 60526 (United States), 2017. North Kensington Avenue,. 
  13. Y. Kim, J.J. Jeong, Development of a Technical Specification Monitoring System Using Real-Time Operating Parameters in OPR1000, Transactions of the Korean Nuclear Society Autumn Meeting, 2019. 
  14. S. Lee, J. Kim, Lee, Design of computerized operator support system for technical specification monitoring, Ann. Nucl. Energy 165 (2022), 108661. 
  15. S. Yu, N. Seong, Y. Kim, J. Lee, Conceptual Design of Operator Support System for Limiting Condition for Operation, Transactions of the Korean Nuclear Society Virtual Spring Meeting, 2020. 
  16. S. Jang, An Approach for Justifying Safety Margin in the Risk-Informed Integrated Decisionmaking, Transactions of the Korean Nuclear Society Spring Meeting, 2019. 
  17. N. Seong, J. Lee, J. Lee, Monitoring Methodology for Limiting Conditions for Operation of Technical Specification, Transactions of the Korean Nuclear Society Spring Meeting, 2022. 
  18. H. Lim, Natural Language Processing Bible, second ed., Human Science, 2020. 
  19. https://konlpy.org/en/latest/. 
  20. https://github.com/kakao/khaiii. 
  21. https://www.data.go.kr/. 
  22. Korea Hydro Nuclear Power, Standard Maintenance Procedure, 2022. 
  23. KEPCO Engineering & Construction, Human Factors Engineering Guideline, 9-750-J410-001, 2012. 
  24. C.D. Manning, P. Raghavan, H. Schutze, Introduction to information retrieval, Nat. Lang. Eng. 16 (1) (2010) 100-103.  https://doi.org/10.1017/S1351324909005129
  25. https://en.wikipedia.org/wiki/Vector_space_model. 
  26. G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval, Inf. Process. Manag. 24 (5) (1988) 513-523.  https://doi.org/10.1016/0306-4573(88)90021-0
  27. J. Ramos, Using tf-idf to determine word relevance in document queries, in: Proceedings of the First Instructional Conference on Machine Learning, 2003, pp. 29-48. 
  28. J. Son, S.B. Kim, H. Kim, S. Cho, Review and analysis of recommender systems, J. Kor. Instit. Indus. Eng. 41 (2) (2015) 185-208.  https://doi.org/10.7232/JKIIE.2015.41.2.185
  29. K. Lee, Sentence Korean Embeddings Using Korean Corpus, Acorn, 2020.