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

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Application of Artifical Neural Network to Turbine Engine Gas Path Sensors Data Validation
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JT - Propulsion, engines and fuels
OECD FORD branch
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Project
<a href="/en/project/1M0501" target="_blank" >1M0501: Aerospace Research Centre</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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