Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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MFM-based alarm root-cause analysis and ranking for nuclear power plants

  • Mengchu Song (Department of Electrical and Photonics Engineering, Technical University of Denmark) ;
  • Christopher Reinartz (Department of Electrical and Photonics Engineering, Technical University of Denmark) ;
  • Xinxin Zhang (Department of Electrical and Photonics Engineering, Technical University of Denmark) ;
  • Harald P.-J. Thunem (Institute for Energy Technology) ;
  • Robert McDonald (Institute for Energy Technology)
  • Received : 2023.05.20
  • Accepted : 2023.07.24
  • Published : 2023.12.25
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Abstract

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

Keywords

Acknowledgement

This research was funded by the Danish Offshore Technology Centre, Denmark.

References

  1. EPRI, Alarm Processing Methods: Improving Alarm Management in Nuclear Power Plant Control Rooms, 1003662, Technical Report, Electric Power Research Institute, Palo Alto, CA, 2003.
  2. X. Wu, Z. Li, A review of alarm system design for advanced control rooms of nuclear power plants, Int. J. Hum.-Comput. Interaction 34 (6) (2018) 477-490, http://dx.doi.org/10.1080/10447318.2017.1371950.
  3. J.C. Laberge, P. Bullemer, M. Tolsma, D.V.C. Reising, Addressing alarm flood situations in the process industries through alarm summary display design and alarm response strategy, Int. J. Ind. Ergon. 44 (3) (2014) 395-406, http://dx.doi.org/10.1016/j.ergon.2013.11.008.
  4. J. Wang, F. Yang, T. Chen, S.L. Shah, An overview of industrial alarm systems: Main causes for alarm overloading, research status, and open problems, IEEE Trans. Autom. Sci. Eng. 13 (2) (2016) 1045-1061, http://dx.doi.org/10.1109/TASE.2015.2464234.
  5. J.E. Larsson, B. Ohman, A. Calzada, J. DeBor, New solutions for alarm problems, in: 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology, NPIC and HMIT 2006, 2006, pp. 922-929.
  6. W. Brown, J. O'Hara, J. Higgins, Advanced alarm systems: Revision of guidance and its technical basis, NUREG/CR-6684, Technical Report, Brookhaven National Laboratory, Upton, NY, 2000.
  7. B. Ohman, Alarm analysis on large systems using multilevel flow models, IFAC Proc. Vol. 33 (28) (2000) 95-100, http://dx.doi.org/10.1016/s1474-6670(17)36816-7.
  8. K. Parsa, S. Member, M. Hassall, M. Naderpour, Process alarm modeling using graph theory : Alarm design review and rationalization, IEEE Syst. J. 15 (2) (2021) 2257-2268.
  9. Z. Simeu-Abazi, A. Lefebvre, J.P. Derain, A methodology of alarm filtering using dynamic fault tree, Reliab. Eng. Syst. Saf. 96 (2) (2011) 257-266, http://dx.doi.org/10.1016/j.ress.2010.09.005.
  10. L. Abele, M. Anic, T. Gutmann, J. Folmer, M. Kleinsteuber, B. Vogel-Heuser, Combining knowledge modeling and machine learning for alarm root cause analysis, IFAC Proc. Vol. (IFAC-PapersOnline) 46 (9) (2013) 1843-1848, http://dx.doi.org/10.3182/20130619-3-RU-3018.00057.
  11. Z. Cai, L. Zhang, J. Hu, Y. Yi, Y. Wang, Comprehensive alarm information processing technology with application in petrochemical plant, J. Loss Prev. Process Ind. 38 (2015) 101-113, http://dx.doi.org/10.1016/j.jlp.2015.08.010.
  12. J. Hu, Y. Yi, A two-level intelligent alarm management framework for process safety, Saf. Sci. 82 (2016) 432-444, http://dx.doi.org/10.1016/j.ssci.2015.10.005.
  13. F. Wen, C. Chang, Tabu search approach to alarm processing in power systems, IEE Proc., Gener. Transm. Distrib. 144 (1) (1997) 31-38, http://dx.doi.org/10.1049/ip-gtd:19970716.
  14. S. Lai, T. Chen, Methodology and application of pattern mining in multiple alarm flood sequences, IFAC-PapersOnLine 28 (8) (2015) 657-662, http://dx.doi.org/10.1016/j.ifacol.2015.09.043.
  15. M. Schleburg, L. Christiansen, N.F. Thornhill, A. Fay, A combined analysis of plant connectivity and alarm logs to reduce the number of alerts in an automation system, J. Process Control 23 (6) (2013) 839-851, http://dx.doi.org/10.1016/j.jprocont.2013.03.010.
  16. S.Y. Yim, H.G. Ananthakumar, L. Benabbas, A. Horch, R. Drath, N.F. Thornhill, Using process topology in plant-wide control loop performance assessment, Comput. Chem. Eng. 31 (2) (2006) 86-99, http://dx.doi.org/10.1016/j.compchemeng.2006.05.004.
  17. H. Jiang, R. Patwardhan, S.L. Shah, Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix, J. Process Control 19 (8) (2009) 1347-1354, http://dx.doi.org/10.1016/j.jprocont.2009.04.013.
  18. F. Yang, P. Duan, S.L. Shah, T. Chen, Capturing Connectivity and Causality in Complex Industrial Processes, in: SpringerBriefs in Applied Sciences and Technology, Springer International Publishing, Cham, 2014, http://dx.doi.org/10.1007/978-3-319-05380-6.
  19. R. Parvez, W. Hu, T. Chen, Real-time pattern matching and ranking for early prediction of industrial, Control Eng. Pract. 120 (61903345) (2022) 105004, http://dx.doi.org/10.1016/j.conengprac.2021.105004.
  20. J. Folmer, D. Pantforder, B. Vogel-Heuser, An analytical alarm flood reduction to reduce operator's workload, in: Human-Computer Interaction. Users and Applications, Springer, Berlin, Germany, 2012, pp. 297-306, http://dx.doi.org/10.1007/978-3-642-21619-0_38.
  21. Y. Meng, X. Song, D. Zhao, Q. Liu, Alarm management optimization in chemical installations based on adapted HAZOP reports, J. Loss Prev. Process Ind. 72 (January) (2021) 104578, http://dx.doi.org/10.1016/j.jlp.2021.104578.
  22. B. Zhou, W. Hu, T. Chen, Pattern extraction from industrial alarm flood sequences by a modified CloFAST algorithm, IEEE Trans. Ind. Inform. 18 (1) (2022) 288-296, http://dx.doi.org/10.1109/TII.2021.3071361.
  23. G. Dorgo, J. Abonyi, Sequence mining based alarm suppression, IEEE Access 6 (2018) 15365-15379, http://dx.doi.org/10.1109/ACCESS.2018.2797247.
  24. M. Bauer, N.F. Thornhill, A practical method for identifying the propagation path of plant-wide disturbances, J. Process Control 18 (7-8) (2008) 707-719, http://dx.doi.org/10.1016/j.jprocont.2007.11.007.
  25. EPRI, Model-Based Root Cause Analysis for Information Overload Management, 1012490, Technical Report, Electric Power Research Institute, Electric Power Research Institute, Palo Alto, CA, 2006.
  26. M. Lind, Multilevel Flow Modelling of Process Plant for Diagnosis and Control, Riso-M-2357, Technical Report, RisoNational Laboratory, Roskilde, Denmark, 1982.
  27. J. Larsson, Model-based alarm analysis using MFM, Annu. Rev. Autom. Program. 16 (Artificial Intelligence in Real-time Control) (1991) 121-126, http://dx.doi.org/10.1016/0066-4138(91)90020-C.
  28. J.E. Larsson, Diagnosis based on explicit means-end models, Artificial Intelligence 80 (1) (1996) 29-93, http://dx.doi.org/10.1016/0004-3702(94)00043-3.
  29. M. Gomez-Fernandez, K. Higley, A. Tokuhiro, K. Welter, W.K. Wong, H. Yang, Status of research and development of learning-based approaches in nuclear science and engineering: A review, Nucl. Eng. Des. 359 (August 2019) (2020) 110479, http://dx.doi.org/10.1016/j.nucengdes.2019.110479.
  30. J. Ouyang, M. Yang, H. Yoshikawa, Y. Zhou, J. Liu, Alarm analysis and supervisory control of PWR plant, in: Proceedings of Cognitive Systems Engineering in Process Control, CSEPC 2004, 2004, pp. 61-68.
  31. J.E. Larsson, B. Ohman, C. Nihlwing, H. Jokstad, L. Iren, J. Kvalem, M. Lind, Alarm reduction and root cause analysis for nuclear power plant control rooms, in: Proceedings Enlarged Halden Program Group Meeting, 2005, pp. 1-11.
  32. O. Berg, M. Kaarstad, J.E. Farbrot, C. Nihlwing, T. Karlsson, B. Torralba, Alarm systems, in: A.B. Skjerve, A. Bye (Eds.), Simulator-Based Human Factors Studies Across 25 Years - the History of the Halden Man-Machine Laboratory, Springer-Verlag, London, UK, 2011, pp. 155-168, http://dx.doi.org/10.1007/978-0-85729-003-8_10.
  33. F. Dahlstrand, Consequence analysis theory for alarm analysis, Knowl.-Based Syst. 15 (1-2) (2002) 27-36, http://dx.doi.org/10.1016/S0950-7051(01)00118-6.
  34. F. Dahlstrand, Alarm analysis with fuzzy logic and multilevel flow models, in: Research and Development in Expert Systems XV, Springer, London, UK, 1998, pp. 173-188, http://dx.doi.org/10.1007/978-1-4471-0835-1_12.
  35. D. Kirchhubel, X. Zhang, M. Lind, O. Ravn, Identifying causality from alarm observations, in: International Symposium on Future Instrumentation & Control for Nuclear Power Plants, ISOFIC 2017, 2017, pp. 1-6.
  36. X. Zhang, Assessing Operational Situations (Ph.D. thesis), Technical University of Denmark, 2015.
  37. B. Chandrasekaran, Functional representation and causal processes, Adv. Comput. 38 (1994) 73-143, http://dx.doi.org/10.1016/S0065-2458(08)60176-X.
  38. J.C. Joe, C.R. Kovesdi, MTO-3 . 1 : A human factors evaluation of an advanced human system interface for the generic pressurized water reactor simulator, in: Enlarged Halden Programme Group Meeting, no. May, 2019, pp. 1-10.
  39. E.K. Nielsen, M.V. Bram, J. Frutiger, G. Sin, M. Lind, A water treatment case study for quantifying model performance with multilevel flow modeling, Nucl. Eng. Technol. 50 (4) (2018) http://dx.doi.org/10.1016/j.net.2018.02.006.
  40. E.K. Nielsen, A. Gofuku, X. Zhang, O. Ravn, M. Lind, Causality validation of multilevel flow modelling, Comput. Chem. Eng. 140 (2020) 106944, http: //dx.doi.org/10.1016/j.compchemeng.2020.106944.
  41. M. Lind, X. Zhang, Functional modelling for fault diagnosis and its application for NPP, Nucl. Eng. Technol. 46 (6) (2014) 753-772, http://dx.doi.org/10.5516/NET.04.2014.721.
  42. W. Wang, M. Yang, Implementation of an integrated real-time process surveillance and diagnostic system for nuclear power plants, Ann. Nucl. Energy 97 (2016) 7-26, http://dx.doi.org/10.1016/j.anucene.2016.06.002.
  43. M. Song, A. Gofuku, Planning of alternative countermeasures for a station blackout at a boiling water reactor using multilevel flow modeling, Nucl. Eng. Technol. 50 (4) (2018) 542-552, http://dx.doi.org/10.1016/j.net.2018.03.004.
  44. M. Song, A. Gofuku, M. Lind, Model-based and rule-based synthesis of operating procedures for planning severe accident management strategies, Prog. Nucl. Energy 123 (2020) 103318, http://dx.doi.org/10.1016/j.pnucene.2020.103318.
  45. M. Lind, Modeling goals and functions of complex industrial plants, Appl. Artif. Intell. 8 (2) (1994) 259-283, http://dx.doi.org/10.1080/08839519408945442.
  46. C. Reinartz, D. Kirchhubel, O. Ravn, M. Lind, Generation of signed directed graphs using functional models, in: A. A., K. J. (Eds.), IFAC PapersOnLine 52 (11) (2019) 37-42, http://dx.doi.org/10.1016/j.ifacol.2019.09.115.
  47. V. Venkatasubramanian, R. Rengaswamy, S.N. Kavuri, A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies, Comput. Chem. Eng. 27 (3) (2003) http://dx.doi.org/10.1016/S0098-1354(02)00161-8.
  48. J.A. Bondy, U.S.R. Murty, Graph Theory with Applications, Elsevier, New York, 1976.
  49. M. Lind, An introduction to multilevel flow modeling, Nucl. Saf. Simul. 2 (1) (2011) 22-32.
  50. E. Arroyo, Capturing and Exploiting Plant Topology and Process Information as a Basis to Support Engineering and Operational Activities in Process Plants (Ph.D. thesis), Helmut-Schmidt-Universitat, Hamburg, 2017.
  51. J. Wu, M. Lind, X. Zhang, K. Pardhasaradhi, S. Pathi, C. Myllerup, Knowledge acquisition and representation for intelligent operation support in offshore fields, Process Saf. Environ. Prot. 155 (2021) 415-443, http://dx.doi.org/10.1016/j.psep.2021.09.036.
  52. J. Itoh, A. Sakuma, K. Monta, An ecological interface for supervisory control of BWR nuclear power plants, Control Eng. Pract. 3 (2) (1995) 231-239, http://dx.doi.org/10.1016/0967-0661(94)00081-Q.