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Applications of Neural Networks to the Real-Time Prediction of Metal Temperatures in Gas Turbine Engine Components

[+] Author Affiliations
Michael Widrich, Alok Sinha

Pennsylvania State University, University Park, PA

Eva Suarez, Brice Cassenti

Pratt & Whitney, East Hartford, CT

Paper No. GT2006-90317, pp. 561-569; 9 pages
doi:10.1115/GT2006-90317
From:
  • ASME Turbo Expo 2006: Power for Land, Sea, and Air
  • Volume 2: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Controls, Diagnostics and Instrumentation; Environmental and Regulatory Affairs
  • Barcelona, Spain, May 8–11, 2006
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4237-1 | eISBN: 0-7918-3774-2
  • Copyright © 2006 by ASME

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.

Copyright © 2006 by ASME

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