Decentralized and Distributed Active Fault Diagnosis for Stochastic Systems with Indirect Observations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956351" target="_blank" >RIV/49777513:23520/19:43956351 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9011433" target="_blank" >https://ieeexplore.ieee.org/document/9011433</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Decentralized and Distributed Active Fault Diagnosis for Stochastic Systems with Indirect Observations
Original language description
The active fault diagnosis (AFD) framework for stochastic large scale systems is treated. The systems are assumed to be decomposed into weakly coupled input-decentralized subsystems. Each subsystem is represented by a set of models describing fault-free and faulty behavior of the subsystems. The paper focuses on the problem formulation where the state of each subsystem is observed only indirectly using noisy measurements. The decentralized and distributed architectures of the AFD are considered, the corresponding estimation and optimization problems are set up and approximate solutions to these problems are proposed. The performance of the active fault detector in both architectures is illustrated using a numerical example.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2019 22th International Conference on Information Fusion (FUSION)
ISBN
978-0-9964527-8-6
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Ottawa
Event location
Ottawa, Kanada
Event date
Jul 2, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000567728800272