Adaptive Fault Diagnoser based on PSO Algorithm for a class of Timed Continuous Petri Nets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00462468" target="_blank" >RIV/67985556:_____/16:00462468 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ETFA.2016.7733587" target="_blank" >http://dx.doi.org/10.1109/ETFA.2016.7733587</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ETFA.2016.7733587" target="_blank" >10.1109/ETFA.2016.7733587</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Fault Diagnoser based on PSO Algorithm for a class of Timed Continuous Petri Nets

Popis výsledku v původním jazyce

This work is concerned with the implementation of an Adaptive Fault Diagnoser (AFD) for a system modeled by Timed Continuous Petri Nets under infinite server semantics, where the set of potential faults is a priori known, however their presence during system evolution, type, location, occurrence time, magnitude and behavior over time are unknown. There exist previous works reported in literature, where this problem has been solved, unfortunately the number of diagnosers used to detect, isolate and identify the fault is too large. Now, this work proposes a single diagnoser model where its structure is known and some of its parameters are updated depending on the fault occurrence. Considering this model, identification algorithms, based on heuristic optimization methods, are used to identify these unknown fault parameters. The analysis of the diagnoser parameters allows the faults detection, isolation and identification. The effectiveness of the proposed diagnoser is shown through two examples with different fault behaviors.

Název v anglickém jazyce

Adaptive Fault Diagnoser based on PSO Algorithm for a class of Timed Continuous Petri Nets

Popis výsledku anglicky

This work is concerned with the implementation of an Adaptive Fault Diagnoser (AFD) for a system modeled by Timed Continuous Petri Nets under infinite server semantics, where the set of potential faults is a priori known, however their presence during system evolution, type, location, occurrence time, magnitude and behavior over time are unknown. There exist previous works reported in literature, where this problem has been solved, unfortunately the number of diagnosers used to detect, isolate and identify the fault is too large. Now, this work proposes a single diagnoser model where its structure is known and some of its parameters are updated depending on the fault occurrence. Considering this model, identification algorithms, based on heuristic optimization methods, are used to identify these unknown fault parameters. The analysis of the diagnoser parameters allows the faults detection, isolation and identification. The effectiveness of the proposed diagnoser is shown through two examples with different fault behaviors.

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-20433S" target="_blank" >GA13-20433S: Analýza a řízení globálně dekomponovaných silně nelineárních stavových dynamických modelů se složitými interakcemi jejich komponent</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2016 IEEE 21th Conference on Emerging Technologies & Factory Automation (ETFA)

ISBN

978-1-5090-1314-2

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Berlin

Místo konání akce

Berlin

Datum konání akce

6. 9. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000389524200094