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Ph.D de YE Lina
YE Lina
Ph.D
Group : Artificial Intelligence and Inference Systems

Optimized Diagnosability of Distributed Discrete Event Systems Through Abstraction

Starts on 01/10/2007
Advisor : DAGUE, Philippe

Funding : A
Affiliation : Université Paris-Sud
Laboratory : LRI

Defended on 07/07/2011, committee :
Mme. Marie-Odile Cordier, professeur, Universite de Rennes 1, IRISA-INRIA
M. Stephane Lafortune, professeur, The University of Michigan, Dept. Of
Electrical Engineering and Computer Science
examinateur:
M. Paul Gastin, professeur, LSV, CNRS & ENS de Cachan
Mme Fatiha Zaïdi, MCF HDR, LRI, Université Paris Sud
directeur de thèse:
M. Philippe Dague, professeur, LRI, Université Paris Sud

Research activities :

Abstract :
The subject of this thesis focuses on methods for determining the
diagnosability property of discrete event systems in distributed way
without building the global model of the system. This framework is of
primary importance for real applications: distributed systems, systems are
too complex to manage their global model, confidentiality of local models
to each other. We first investigate how to optimize distributed
diagnosability analysis by abstracting necessary and sufficient
information
from local objects to decide global diagnosability decision. The algorithm
efficiency can be greatly improved by synchronization of abstracted local
objects compared to that of non abstracted local ones.

Then we extend the distributed diagnosability algorithm from fault event
first to simple pattern and then to general pattern, where pattern can
describe more general objects in the diagnosis problem, e.g. multiple
faults, multiple occurrence of the same fault, ordered occurrence of
significant events, etc. In the distributed framework, the pattern
recognition is first incrementally performed normally in a subsystem and
then pattern diagnosability can be determined by adjusting abstracted
method used in fault event case. We prove the correctness and efficiency
of
our proposed algorithm both in theory through proof and in practice
through
implementation.

Finally we study distributed diagnosability problem in systems with
autonomous components, i.e. observable information is distributed instead
of centralized. In other words, each component can only observe its own
observable events. We first describe cooperative diagnosis architecture
for
such a system before defining a new cooperative diagnosability definition.
Then we propose an efficient way for cooperative diagnosability
verification by analyzing communication compatibility between local
objects
through certain synchronization.

Ph.D. dissertations & Faculty habilitations
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DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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