Decentralized Diagnosis of Discrete Event Systems using Unconditional and Conditional Decisions

Yin, Wei; Yoo, Tae-Sic; Lafortune, Stéphane

doi:10.1109/cdc.2005.1583171

Cited by 24 publications

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Diagnosis of Discrete Event Systems Using Decentralized Architectures

Wang

Yoo

Lafortune

2007

Discrete Event Dyn Syst

141

View full text Add to dashboard Cite

Decentralized diagnosis of discrete event systems has received a lot of attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. This paper casts the problem of decentralized diagnosis in a new hierarchical framework. A key feature is the exploitation of different local decisions together with appropriate rules for their fusion. This includes local diagnosis decisions that can be interpreted as "conditional decisions." Under this new framework, a series of new decentralized architectures are defined and studied. The properties of their corresponding notions of decentralized diagnosability are characterized and their relationship with existing work described. Corresponding verification algorithms are also presented and on-line diagnosis strategies discussed.

show abstract

Diagnosis of Discrete Event Systems Using Decentralized Architectures

Wang

Yoo

Lafortune

2007

Discrete Event Dyn Syst

141

View full text Add to dashboard Cite

show abstract

Partial Order Techniques for Distributed Discrete Event Systems: Why You Cannot Avoid Using Them

Fabre

Benveniste

2007

Discrete Event Dyn Syst

View full text Add to dashboard Cite

Monitoring or diagnosis of large scale distributed Discrete Event Systems with asynchronous communication is a demanding task. Ensuring that the methods developed for Discrete Event Systems properly scale up to such systems is a challenge. In this paper we explain why the use of partial orders cannot be avoided in order to achieve this objective. To support this claim, we try to push classical techniques (parallel composition of automata and languages) to their limits and we eventually discover that partial order models arise at some point. We focus on online techniques, where a key difficulty is the choice of proper data structures to represent the set of all runs of a distributed system, in a modular way. We discuss the use of previously known structures such as execution trees and unfoldings. We propose a novel and more compact data structure called "trellis." Then, we show how all the above data structures can be used in performing distributed monitoring and diagnosis. The techniques reported here were used in an industrial context for fault management and alarm correlation in telecommunications networks. This paper is an extended and improved version of the plenary address that was given by the second author at WODES' 2006.

show abstract