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Autonomous Agents for Distributed Problem Solving in Condition Monitoring

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Intelligent Problem Solving. Methodologies and Approaches (IEA/AIE 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1821))

Abstract

The application of intelligent systems for data interpretation and condition monitoring is an advancing field of research. In recent years autonomous intelligent agents and multi-agent systems have gained much attention within different real time applications. This paper introduces the novel idea of COMMAS (Condition Monitoring Multi-Agent System); a hierarchical decentralised multi-agent architecture developed for data interpretation and condition monitoring applications. It employs groups of different kinds of intelligent agents to cope with the variety of application functions by using distributed problem solving and different computational intelligence techniques. The design and functionality of the diversity of agents, along with the key issues of the multi-agent system as a whole are described. This paper demonstrates how agent technology overcomes problems associated with centralised approaches in condition monitoring, and illustrates the new opportunities agents can provide.

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6 References

  1. B. D. Gemmell, J. R. McDonald, R. W. Stewart, R. N. T. Brooke and B. J. Weir, (1994), “A Consultative Expert System for Fault Diagnosis On Turbine Generator Plant”, Institution of Mechanical Engineers’ Proceedings Part A, Journal of Power and Energy, December 1994.

    Google Scholar 

  2. S. D. J. McArthur, S. C. Bell, J. R. McDonald, R. Mather and S. M. Burt, “Knowledge and Model Based Decision Support for Power System Protection Engineers”, Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, ISAP’96, pp.215–219.

    Google Scholar 

  3. J. R. McDonald et al, (1997), “Intelligent knowledge based systems in electrical power engineering”, London: Chapman & Hall, ISBN 0412753200.

    Google Scholar 

  4. C. Booth, J. R. McDonald, W. Hagman, (1995), “The use of Artificial Neural Networks for the Prediction and Classification of Vibration Behaviour in Plant Transformers”, American Power Conference, Chicago.

    Google Scholar 

  5. J. H. Williams, A. Davies and P. R. Drake, (1992), Condition — Based Maintenance and machine diagnostics, CHAPMAN & HALL.

    Google Scholar 

  6. A. Moyes, G. M. Burt, J. R. McDonald, J. R. Capener, J. N. Dray and R. Goodfellow, “Combining design and operational knowledge to enhance generator plant diagnostics”, IEE Proc. — Gener. Transm. Distrib. Vol. 143, No 3, May 1996, pp. 300–304

    Article  Google Scholar 

  7. R. Barron, (1996), Engineering Condition Monitoring: Practice, Methods and Applications, Addison Wesley Longman.

    Google Scholar 

  8. S. M. C. Peers, “Knowledge Representation in a Blackboard System for Sensor Data Interpretation”, Methodology and Tools in knowledge based systems, vol. 1, 1998, pp.657–666.

    Article  Google Scholar 

  9. Wooldridge M. J. & Jennings N. R., (1998), Agent Technology Foundations, Applications, and Markets, Springer — Verlag.

    Google Scholar 

  10. Pattie Maes, (1991), “Designing Autonomous Agents”, Theory and Practice from Biology to Engineering and back, the MIT Press.

    Google Scholar 

  11. A. S. Rao and Georgeff, (1995), “BDI agents: From theory to practice”, Tech. Rep. 56, Australian Artificial Intelligence Institute, Melbourne, Australia.

    Google Scholar 

  12. R. Mather, T. Cumming, S.D.J. McArthur, S.C. Bell and J.D. McDonald, “The development of an advanced suite of data interpretation facilities for the analysis of power system disturbances”, Cigre 1998, Group 34, paper 34–102.

    Google Scholar 

  13. D. Cockburn and N. R. Jennings, (1996) “ARCHON: A distributed artificial intelligence system for industrial applications”, Foundations of Distributed Artificial Intelligence, John Wiley & Sons.

    Google Scholar 

  14. Jennings et al, (1996), ADEPT: Managing Business Processes using Intelligent Agents, Conference Proceedings BCS Expert Systems 1996.

    Google Scholar 

  15. Ferber Jacques, (1999), Multi Agent Systems: An Introduction to Distributed Artificial Intelligence, ADDISON-WESLEY.

    Google Scholar 

  16. The source for Java technology, http://java.sun.com/products/index.html

  17. JATLite, http://java.standford.edu

  18. JATLite Router, http://www.cdc.unict.it/~michele/JATLite/docs/

  19. E. H. Durfee, “Practically Co-ordinating”, AI Magazine, vol.220, pt. 1, 1999, pp. 99–116.

    Google Scholar 

  20. UMBC AgentWeb, KIF: Knowledge Interchange Format http://www.cs.umbc.edu/kse/kif/

  21. Finin T., Labrou Y. & Mayfield J., KQML as an agent communication language, in Bradshaw Jeffrey, (1997), Software Agents.

    Google Scholar 

  22. Jennings et al, (1993), Transforming Standalone Expert Systems into a Community of Cooperating Agents, International Journal of Engineering Applications of Artificial Intelligence Vol. 6, No 4.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronic & Electrical Engineering, Centre for Electrical Power Engineering, University of Strathclyde, Scotland, UK

    E. E. Mangina, S. D. J McArthur & J. R. McDonald

Authors
  1. E. E. Mangina
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  2. S. D. J McArthur
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  3. J. R. McDonald
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Editor information

Editors and Affiliations

  1. The Center for Advanced Computer Studies, University of Lousiana, 2 Rex Street, Lafayette, LA, 70504-4330, USA

    Rasiah Logananthara

  2. Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, 89069, Ulm, Germany

    Günther Palm

  3. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2000 Springer-Verlag Berlin Heidelberg

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Mangina, E.E., McArthur, S.D.J., McDonald, J.R. (2000). Autonomous Agents for Distributed Problem Solving in Condition Monitoring. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_82

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  • DOI: https://doi.org/10.1007/3-540-45049-1_82

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67689-8

  • Online ISBN: 978-3-540-45049-8

  • eBook Packages: Springer Book Archive

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