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https://doi.org/10.1109/wodes....
Article . 2008 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2010
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Fault diagnosis with dynamic observers

Authors: Franck Cassez; Stavros Tripakis;

Fault diagnosis with dynamic observers

Abstract

In this paper, we review some recent results about the use of dynamic observers for fault diagnosis of discrete event systems. Fault diagnosis consists in synthesizing a diagnoser that observes a given plant and identifies faults in the plant as soon as possible after their occurrence. Existing literature on this problem has considered the case of fixed static observers, where the set of observable events is fixed and does not change during execution of the system. In this paper, we consider dynamic observers: an observer can "switch" sensors on or off, thus dynamically changing the set of events it wishes to observe. It is known that checking diagnosability (i.e., whether a given observer is capable of identifying faults) can be solved in polynomial time for static observers, and we show that the same is true for dynamic ones. We also solve the problem of dynamic observers' synthesis and prove that a most permissive observer can be computed in doubly exponential time, using a game-theoretic approach. We further investigate optimization problems for dynamic observers and define a notion of cost of an observer.

Extented version of the paper that appeared in Proc. of the 9th Workshop on Discrete Event Systems (WODES'08)

Keywords

FOS: Computer and information sciences, Formal Languages and Automata Theory (cs.FL), Computer Science - Formal Languages and Automata Theory

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
Green