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MAS for Alarm Management System in Emergencies

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7637)

Abstract

Due to the imminent danger involved in the petroleum operation domain, only well trained workers are allowed to operate in offshore oil process plants. Although their vast experience, human errors may happen during emergency situations as a result of the overwhelmed amount of information generated by a great deal of triggered alarms. Alarm devices have become very cheap leading petroleum equipment manufacturers to overuse them transferring safety responsibility to operators. Not rarely, accident reports cite poor operators’ understanding of the actual plant status due to too many active alarms. In this paper, we present an alarm management system focused on guiding offshore platform operators’ attention to the essential information that calls for immediate action during emergency situations. We use a multi-agent based approach as the basis of our alarm management system for assisting operators to make sense of alarm avalanche scenarios.

Keywords

multi-agent systems emergencies alarm management oil industry fault detection sense making 

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References

  1. 1.
    Aizpurua, O., Galan, R., Jimenez, A.: A new cognitive-based massive alarm management system in electrical power administration. In: 2008 7th International Caribbean Conference on Devices, Circuits and Systems, pp. 1–6. IEEE (April 2008)Google Scholar
  2. 2.
    Cochran, T., Bullemer, P., Nimmo, I.: Managing abnormal situations in the process industries parts 1, 2, 3. In: NIST Proceedings of the Motor Vehicle Manufacturing Technology (MVMT) Workshop (1997)Google Scholar
  3. 3.
    Dheedan, A., Papadopoulos, Y.: Model-Based Distributed On-line Safety Monitoring. In: The Third International Conference on Emerging Network Intelligence (EMERGING 2011), Lisbon, Portugal, pp. 1–7 (2011)Google Scholar
  4. 4.
    Health and Safety Executive: The Explosion and Fires at the Texaco Refinery, Milford Haven, 24 July 1994 (Incident Report). HSE Books (1997)Google Scholar
  5. 5.
    Heydt, G.T., Vittal, V., Phadke, A.G.: The strategic power infrastructure defense (SPID) system. A conceptual design. IEEE Control Systems 20(4), 40–52 (2000)CrossRefGoogle Scholar
  6. 6.
    Hossack, J.A., Menal, J., McArthur, S.D.J., McDonald, J.R.: A multiagent architecture for protection engineering diagnostic assistance. In: 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), vol. 2, p. 640. IEEE (2003)Google Scholar
  7. 7.
    Kornelije, R.: A Combination of Reactive and Deliberative Agents in Hospital Logistics. In: Proceedings of 17th International Conference on Information and Intelligent Systems, Croatia, pp. 63–70 (2006)Google Scholar
  8. 8.
    McArthur, S.D.J., Strachan, S.M., Jahn, G.: The Design of a Multi-Agent Transformer Condition Monitoring System. IEEE Transactions on Power Systems 19(4), 1845–1852 (2004)CrossRefGoogle Scholar
  9. 9.
    Mendoza, B., Xu, P., Song, L.: A multi-agent model for fault diagnosis in petrochemical plants. In: 2011 IEEE Sensors A Applications Symposium, pp. 203–208. IEEE (February 2011)Google Scholar
  10. 10.
    Rabuzin, K., Malekovic, M., Cubrilo, M.: Resolving Physical Conflicts in Multiagent Systems. In: 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008), pp. 193–199. IEEE (July 2008)Google Scholar
  11. 11.
    Sayda, A.F., Taylor, J.H.: Toward a Practical Multi-Agent System for Integrated Control and Asset Management of Petroleum Production Facilities. In: 2007 IEEE 22nd International Symposium on Intelligent Control, pp. 511–517. IEEE (2007)Google Scholar
  12. 12.
    Skogdalen, J.E., Vinnem, J.E.: Combining precursor incidents investigations and QRA in oil and gas industry. Reliability Engineering & System Safety 101, 48–58 (2012)CrossRefGoogle Scholar
  13. 13.
    Zarri, G.P.: Knowledge representation and inference techniques to improve the management of gas and oil facilities. Knowledge-Based Systems 24(7), 989–1003 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Computer Science InstituteFluminense Federal UniversityNiteróiBrazil
  2. 2.Computer Science DepartmentCarlos III University of MadridMadridSpain

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