DYNAMIC DIAGNOSIS OF ACTIVE SYSTEMS WITH FRAGMENTED OBSERVATIONS

Gianfranco Lamperti, Marina Zanella

2004

Abstract

Diagnosis of discrete-event systems (DESs) is a complex and challenging task. Typical application domains include telecommunication networks, power networks, and digital-hardware networks. Recent blackouts in northern America and southern Europe offer evidence for the claim that automated diagnosis of large-scale DESs is a major requirement for the reliability of this sort of critical systems. The paper is meant as a little step toward this direction. A technique for the dynamic diagnosis of active systems with uncertain observations is presented. The essential contribution of the method lies in its ability to cope with uncertainty conditions while monitoring the systems, by generating diagnostic information at the occurrence of each newly-received fragment of observation. Uncertainty stems, on the one hand, from the complexity and distribution of the systems, where noise may affect the communication channels between the system and the control rooms, on the other, from the multiplicity of such channels, which is bound to relax the absolute temporal ordering of the observable events generated by the system during operation. The solution of these diagnostic problems requires nonmonotonic reasoning, where estimates of the system state and the relevant candidate diagnoses may not survive the occurrence of new observation fragments.

References

  1. Baroni, P., Lamperti, G., Pogliano, P., and Zanella, M. (1999). Diagnosis of large active systems. Arti¯cial Intelligence, 110(1):135{183.
  2. Console, L., Picardi, C., and Ribaudo, M. (2002). Process algebras for systems diagnosis. Arti¯- cial Intelligence, 142(1):19{51.
  3. Cordier, M. and LargouÄet, C. (2001). Using modelchecking techniques for diagnosing discreteevent systems. In Twelfth International Workshop on Principles of Diagnosis { DX'01, pages 39{46, San Sicario, I.
  4. Debouk, R., Lafortune, S., and Teneketzis, D. (2000). A diagnostic protocol for discrete-event systems with decentralized information. In Eleventh International Workshop on Principles of Diagnosis { DX'00, pages 41{48, Morelia, MX.
  5. Fattah, Y. E. and Provan, G. (1997). Modeling temporal behavior in the model-based diagnosis of discrete-event systems (a preliminary note). In Eighth International Workshop on Principles of Diagnosis { DX'97, Mont St. Michel, F.
  6. Lamperti, G. and Pogliano, P. (1997). Event-based reasoning for short circuit diagnosis in power transmission networks. In Fifteenth International Joint Conference on Arti¯cial Intelligence { IJCAI'97, pages 446{451, Nagoya, J.
  7. Lamperti, G. and Zanella, M. (2002). Diagnosis of discrete-event systems from uncertain temporal observations. Arti¯cial Intelligence, 137(1{ 2):91{163.
  8. Lamperti, G. and Zanella, M. (2003a). Continuous diagnosis of discrete-event systems. In Fourteenth International Workshop on Principles of Diagnosis { DX'03, pages 105{111, Washington DC.
  9. Lamperti, G. and Zanella, M. (2003b). Diagnosis of Active Systems { Principles and Techniques, volume 741 of The Kluwer International Series in Engineering and Computer Science. Kluwer Academic Publisher, Dordrecht, NL.
  10. Lunze, J. (2000). Diagnosis of quantized systems based on a timed discrete-event model. IEEE Transactions on Systems, Man, and Cybernetics { Part A: Systems and Humans, 30(3):322{335.
  11. Pencol¶e, Y., Cordier, M., and Roz¶e, L. (2001). Incremental decentralized diagnosis approach for the supervision of a telecommunication network. In Twelfth International Workshop on Principles of Diagnosis { DX'01, pages 151{158, San Sicario, I.
  12. Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., and Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9):1555{1575.
  13. Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., and Teneketzis, D. (1996). Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology, 4(2):105{124.
Download


Paper Citation


in Harvard Style

Lamperti G. and Zanella M. (2004). DYNAMIC DIAGNOSIS OF ACTIVE SYSTEMS WITH FRAGMENTED OBSERVATIONS . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 249-261. DOI: 10.5220/0002619202490261


in Bibtex Style

@conference{iceis04,
author={Gianfranco Lamperti and Marina Zanella},
title={DYNAMIC DIAGNOSIS OF ACTIVE SYSTEMS WITH FRAGMENTED OBSERVATIONS},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={249-261},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002619202490261},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - DYNAMIC DIAGNOSIS OF ACTIVE SYSTEMS WITH FRAGMENTED OBSERVATIONS
SN - 972-8865-00-7
AU - Lamperti G.
AU - Zanella M.
PY - 2004
SP - 249
EP - 261
DO - 10.5220/0002619202490261