Decentralised approach for the diagnosis of discrete event systems: application to telecommunication networks

Dr. Yannick Pencole (RSISE/CSL)

CSL SEMINAR SERIES

DATE: 2003-08-05
TIME: 16:00:00 - 17:00:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
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ABSTRACT:
In this seminar, I will present an approach for the monitoring and the diagnosis of complex dynamic systems such as telecommunication networks. These systems are composed of a set of interconnected equipments. With the help of sensors, a supervisor is able to receive alarms from the system. The objective of the supervisor is to deal with these alarms in order to guess the possible states and the possible failures of the system.

The purpose of the presented approach is to provide a help for interpreting alarms and giving the possible failures that could have occurred on the system. The developed approach uses model-based diagnosis techniques. Given a behavioural model of the set of supervised components, this approach consists in efficently using this model in order to analyse the alarm stream on-line. We propose to represent diagnoses as compacted transition systems based on failure events (reduced transducers).

Because of the complexity and the distributed nature of supervised systems, our approach is based on the ``divide and conquer'' paradigm. Firstly, we compute a set of local diagnoses based on local behaviours. Secondly, the computation of the global diagnosis is based on a local diagnoses merging operation. A strategy for applying this merging operation was developped in order to be efficient.

This work has been done in the context of a French scientific project called MAGDA. Finally, a platform for the decentralised diagnosis of dynamic systems has been developped and has been used to validate our approach on telecommunication network examples: Transpac network and a SDH network.
BIO:
Yannick Pencole is a research fellow in the Computer Science Laboratory, at the Australian National University. He previously was a PhD student at INRIA/University of Rennes, working on decentralised model-based diagnosis.