Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Enhanced reasoning with multilevel flow modeling based on time-to-detect and time-to-effect concepts

  • Kim, Seung Geun (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Seong, Poong Hyun (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2018.01.31
  • Accepted : 2018.03.13
  • Published : 2018.05.25
Download PDF
⟨ Previous Next ⟩

Abstract

To easily understand and systematically express the behaviors of the industrial systems, various system modeling techniques have been developed. Particularly, the importance of system modeling has been greatly emphasized in recent years since modern industrial systems have become larger and more complex. Multilevel flow modeling (MFM) is one of the qualitative modeling techniques, applied for the representation and reasoning of target system characteristics and phenomena. MFM can be applied to industrial systems without additional domain-specific assumptions or detailed knowledge, and qualitative reasoning regarding event causes and consequences can be conducted with high speed and fidelity. However, current MFM techniques have a limitation, i.e., the dynamic features of a target system are not considered because time-related concepts are not involved. The applicability of MFM has been restricted since time-related information is essential for the modeling of dynamic systems. Specifically, the results from the reasoning processes include relatively less information because they did not utilize time-related data. In this article, the concepts of time-to-detect and time-to-effect were adopted from the system failure model to incorporate time-related issues into MFM, and a methodology for enhancing MFM-based reasoning with time-series data was suggested.

Keywords

References

  1. M. Lind, An introduction to multilevel flow modeling, Nucl. Saf. Simulat. 2 (1) (2011) 1-11.
  2. M. Lind, H. Yoshikawa, S.B. Jorgensen, M. Yang, K. Tamayama, K. Okusa, et al., Multilevel flow modeling of Monju nuclear power plant, Nucl. Saf. Simulat. 2 (3) (2011) 274-284.
  3. B. Ohman, Failure mode analysis using multilevel flow models, in: 1999 European Control Conference, Karlsruhe, Germany, 31 Aug.-3 Sept., 1999.
  4. J. Wu, L. Zhang, W. Liang, J. Hu, et al., A novel failure mode analysis model for gathering system based on multilevel flow modeling and HAZOP, Process Safe. Environ. Protect. 91 (1-2) (2013) 54-60. https://doi.org/10.1016/j.psep.2012.02.002
  5. J. Ouyang, M. Yang, H. Yoshikawa, Z. Yangping, et al., Modeling of PWR plant by multilevel flow model and its application in fault diagnosis, J. Nucl. Sci. Technol. 42 (8) (2005) 695-705. https://doi.org/10.1080/18811248.2004.9726439
  6. A. Gofuku, Y. Tanaka, Application of a derivation technique of possible counter actions to an oil refinery plant, in: Proceedings of 4th IJCAI Workshop on Engineering Problems for Qualitative Reasoning, 1999, pp. 77-83.
  7. A. Gofuku, T. Inoue, T. Sugihara, et al., A technique to generate plausible counter-operation procedures for an emergency situation based on a model expressing functions of components, J. Nucl. Sci. Technol. 54 (5) (2017) 578-588. https://doi.org/10.1080/00223131.2017.1292966
  8. W. Qin, P.H. Seong, A validation method for emergency operating procedures of nuclear power plants based on dynamic multi-level flow modeling, Nucl. Eng. Technol. 37 (1) (2005) 118-126.
  9. M.M. Rene van Paassen, P.A. Wieringa, Reasoning with multilevel flow models, Reliab. Eng. Syst. Saf. 64 (1999) 151-165. https://doi.org/10.1016/S0951-8320(98)00060-X
  10. A. Gofuku, Y. Kondo, Quantitative effect indication of a counter action in an abnormal plant situation, Int. J. Nucl. Saf. Simulat. 2 (3) (2011) 255-264.
  11. A. Gofuku, Applications of MFM to intelligent systems for supporting plant operators and designers: function-based inference techniques, Int. J. Nucl. Saf. Simulat. 2 (3) (2011) 235-245.
  12. T. Kurtoglu, S.B. Johnson, E. Barszcz, J.R. Johnson, P.I. Robinson, et al., Integrating system health management into the early design of aerospace systems using functional fault analysis, in: 2008 International Conference on Prognostics and Health Management, Denver, CO, Oct. 6-9, 2008.
  13. J. Diebolt, C.P. Robert, Estimation of finite mixture distributions through Bayesian sampling, J. Roy. Stat. Soc. Ser. B (Methodological) (1994) 363-375.
  14. B.J. Stojkova, Bayesian Methods for Multi-modal Posterior Topologies, Ph.D. Dissertation, Department of Statistics and Actuarial Science, Simon Fraser University, 2017.
  15. N.E. Day, Estimating the components of a mixture of normal distributions, Biometrika 56 (1969) 463-474. https://doi.org/10.1093/biomet/56.3.463
  16. B.W. Silverman, Using kernel density estimates to investigate multimodality, J. Roy. Stat. Soc. Ser. B (Methodological) (1981) 97-99.
  17. J.E. Chacon, T. Duong, et al., Data-driven density derivative estimation with applications to nonparametric clustering and bump hunting, Electron. J. Stat. 7 (2013) 499-532. https://doi.org/10.1214/13-EJS781
  18. S. Mukhopadhyay, Large-scale mode identification and data-driven sciences, Electron. J. Stat. 11 (1) (2017) 215-240. https://doi.org/10.1214/17-EJS1229
  19. J.E. Kelley Jr., M.R. Walker, Critical-path planning and scheduling, in: 1959 Proceedings of the Eastern Joint Computer Conference, 1959, pp. 160-173.

Cited by

  1. Generation of Signed Directed Graphs Using Functional Models vol.52, pp.11, 2018, https://doi.org/10.1016/j.ifacol.2019.09.115
  2. System-Level Fault Prognosis for High-Speed Railway On-Board Systems vol.2673, pp.12, 2019, https://doi.org/10.1177/0361198119867673