Hierarchical Active Fault Diagnosis for Stochastic Large Scale Systems with Coupled Faults

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959774" target="_blank" >RIV/49777513:23520/20:43959774 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.23919/FUSION45008.2020.9190304" target="_blank" >https://doi.org/10.23919/FUSION45008.2020.9190304</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/FUSION45008.2020.9190304" target="_blank" >10.23919/FUSION45008.2020.9190304</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Active Fault Diagnosis for Stochastic Large Scale Systems with Coupled Faults

Popis výsledku v původním jazyce

The paper deals with the active fault diagnosis of large scale stochastic systems with faults modeled as mutually dependent Markov chains. The system is described by multiple models representing fault-free and faulty behavior of the system. The aim of the active fault detector in addition to detecting the faults is to excite the system to improve the detection quality. The algorithm consists of two stages: the off-line design of the Bellman function providing the optimal excitation and the on-line estimation, which generates the decisions and selects the optimal excitation according to the Bellman function. In particular, the paper focuses on the online estimation and proposes an algorithm in the hierarchical architecture. The local nodes estimate the continuous state of the subsystems, select the optimal excitations and send local likelihoods to the central node. The central node generates the decisions and submits the respective model probabilities to the local nodes. The performance of the proposed algorithm is validated using a simple numerical example.

Název v anglickém jazyce

Hierarchical Active Fault Diagnosis for Stochastic Large Scale Systems with Coupled Faults

Popis výsledku anglicky

The paper deals with the active fault diagnosis of large scale stochastic systems with faults modeled as mutually dependent Markov chains. The system is described by multiple models representing fault-free and faulty behavior of the system. The aim of the active fault detector in addition to detecting the faults is to excite the system to improve the detection quality. The algorithm consists of two stages: the off-line design of the Bellman function providing the optimal excitation and the on-line estimation, which generates the decisions and selects the optimal excitation according to the Bellman function. In particular, the paper focuses on the online estimation and proposes an algorithm in the hierarchical architecture. The local nodes estimate the continuous state of the subsystems, select the optimal excitations and send local likelihoods to the central node. The central node generates the decisions and submits the respective model probabilities to the local nodes. The performance of the proposed algorithm is validated using a simple numerical example.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion (FUSION)

ISBN

978-0-578-64709-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE

Místo vydání

Rustenburg

Místo konání akce

Rustenburg, Jihoafrická republika

Datum konání akce

6. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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