Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00010669%3A_____%2F10%3A%230001094" target="_blank" >RIV/00010669:_____/10:#0001094 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation

Popis výsledku v původním jazyce

Gas path analysis hols a central position in the engine condition monitoring and fault diagnostics technique. The success of gas path analysis depends mainly on the quality of the measurements obtained. This paper sets out to apply Artificial Neural Networks to provide a fast and accurate diagnostic tool for the identification of sensor faults. This method is effective even when different engines vary due to manufacturing or assembly tolerances. The network is also able to provide information of which sensor signal is degraded. Several architectures for networks were assessed to find the optimum design for the application. The engine performance was simulated by a computer program. This gave the data sets for the training and validation of the networks.

Název v anglickém jazyce

Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation

Popis výsledku anglicky

Gas path analysis hols a central position in the engine condition monitoring and fault diagnostics technique. The success of gas path analysis depends mainly on the quality of the measurements obtained. This paper sets out to apply Artificial Neural Networks to provide a fast and accurate diagnostic tool for the identification of sensor faults. This method is effective even when different engines vary due to manufacturing or assembly tolerances. The network is also able to provide information of which sensor signal is degraded. Several architectures for networks were assessed to find the optimum design for the application. The engine performance was simulated by a computer program. This gave the data sets for the training and validation of the networks.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JT - Pohon, motory a paliva

OECD FORD obor

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Projekt

<a href="/cs/project/1M0501" target="_blank" >1M0501: Centrum leteckého a kosmického výzkumu</a><br>

Návaznosti

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

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název periodika

Czech Aerospace Proceedings / Letecký zpravodaj

ISSN

1211-877X

e-ISSN

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Svazek periodika

1/2010

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

3

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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