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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Health and Safety Executive: The Explosion and Fires at the Texaco Refinery, Milford Haven, 24 July 1994 (Incident Report). HSE Books (1997)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García, A.C.B., Leme, L.A.P.P., Pinto, F., Sanchez-Pi, N. (2012). MAS for Alarm Management System in Emergencies. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_72
Download citation
DOI: https://doi.org/10.1007/978-3-642-34654-5_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34653-8
Online ISBN: 978-3-642-34654-5
eBook Packages: Computer ScienceComputer Science (R0)