Advertisement

Means-end Models of Safety Related Organizational Processes

  • Johannes Petersen
Conference paper

Abstract

The paper provides an outline of Multilevel Flow Modeling (MFM) and demonstrates by means of example that it can be used to make explicit the means-end structure of safety related organizational process in the nuclear power domain.

Keywords

Control Process Control Function Plant Modification Safety Review Cognitive Engineer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wahlström, B, Gunsell, L. REAKTORSäKERHET; En beskrivning och värdering av säkerhetsarbetet i Norden. NKSIRAK-1(97)R8, ISBN: 87-7893-035-9, 1997Google Scholar
  2. 2.
    Gofuku A, Lind M. Combining multilevel flow modeling and hybrid phenomena theory for efficient design of engineering systems. In: Arzen, KE (ed) Proceedings of the IFAC Workshop on Computer Software Structures Integrating AI/KBS Systems in Process Control. Pergamon, 1994, pp 41-46Google Scholar
  3. 3.
    Larsson JE. Diagnosis based on explicit means-end models. Artif Intell 1996; 80:29–93CrossRefGoogle Scholar
  4. 4.
    Lind M. The Use of Flow Models for Automated Plant Diagnosis. In: Rasmussen, J. and Rouse W.B. (eds.), Human Detection and Diagnosis of System Failures. Plenum Press, New York, 1981, pp 411–432CrossRefGoogle Scholar
  5. 5.
    Petersen J. Situation Assessment of Complex Dynamic Systems Using MFM. In: Johannsen, G. (Ed.) Proc. of 8th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, 2001, Kassel, Germany, pp 645–650.Google Scholar
  6. 6.
    Larsen MN. Deriving Action Sequences for Start-Up Using Multilevel Flow Models, PhD thesis, Department of Automation, Technical University of Denmark, Kgs. Lyngby, 1993Google Scholar
  7. 7.
    de Souza LE, Veloso MM. AI planning in supervisory control systems. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 4, 1996, pp 3153–3158.Google Scholar
  8. 8.
    Lind M. Representing Goals and Functions of Complex Systems, Tech. Rep. 90-D-381, Department of Automation, Technical University of Denmark, Kgs. Lyngby, 1990.Google Scholar
  9. 9.
    Lind M. Modelling Goals and Functions of Complex Industrial Plants. Applied Artificial Intelligence 1994; 8:259–283CrossRefGoogle Scholar
  10. 10.
    Lind M. Plant modelling for supervisory control. Trans Inst MC, 1999; 21, 4/5, pp 171–180CrossRefGoogle Scholar
  11. 11.
    Lind M. Goals and Functions of an Auxiliary Feedwater System. In: Lind, M. et al., Interactive Planning for Integrated Supervision and Control in Complex Plant. Final report from CEC JRC project 4937-92-08-ED ISP DK, 1993Google Scholar
  12. 12.
    Lind M. Promoting and Opposing. A Semantic analysis of Von Wright’s action types. In: NKS project NKS-R-07: Barriers, Control and Management-Report from the pilot phase. Nordic nuclear safety research. Report no. NKS-87, 2003Google Scholar
  13. 13.
    Rasmussen J. Information Processing and Human-Machine Interaction. An Approach to Cognitive Engineering, Amsterdam: North-Holland, 1986Google Scholar
  14. 14.
    Petersen J. Analysis of the Plant Renewal Process at Forsmarks Kraftgrupp AB. In: NKS project NKS-R-07: Barriers, Control and Management — Report from the pilot phase. Nordic nuclear safety research. Report no. NKS-87, 2003Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Johannes Petersen
    • 1
  1. 1.Ørsted DTU, AutomationTechnical University of DenmarkKgs. LyngbyDenmark

Personalised recommendations