Applications of neural networks to the real-time prediction of metal temperatures in gas turbine engine components

Michael Widrich, Alok Sinha, Eva Suarez, Brice Cassenti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Several methods for predicting metal temperatures in a turbine engine are presented. Proper Orthogonal Decomposition (POD) is used to determine the system modes from temperature data sets from an engine mission. The coefficients of the system POD modes are used to identify the system dynamics. The linear state space model in conjunction with a multi-layer feedforward neural network is shown to produce superior prediction values for untrained temperature data when compared to those values produced by the state space model alone.

Original languageEnglish (US)
Title of host publicationProceedings of the ASME Turbo Expo 2006 - Power for Land, Sea, and Air
Pages561-569
Number of pages9
DOIs
StatePublished - 2006
Event2006 ASME 51st Turbo Expo - Barcelona, Spain
Duration: May 6 2006May 11 2006

Publication series

NameProceedings of the ASME Turbo Expo
Volume2

Other

Other2006 ASME 51st Turbo Expo
Country/TerritorySpain
CityBarcelona
Period5/6/065/11/06

All Science Journal Classification (ASJC) codes

  • General Engineering

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