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The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study

[+] Author Affiliations
Allan J. Volponi

Hamilton Sundstrand, A United Technologies Company, Windsor Locks, CT

Hans DePold, Ranjan Ganguli

Pratt & Whitney, A United Technologies Company, East Hartford, CT

Chen Daguang

Beijing University of Aeronautics and Astronautics, Beijing, China

Paper No. 2000-GT-0547, pp. V004T04A029; 9 pages
doi:10.1115/2000-GT-0547
From:
  • ASME Turbo Expo 2000: Power for Land, Sea, and Air
  • Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education
  • Munich, Germany, May 8–11, 2000
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-7857-6
  • Copyright © 2000 by ASME

abstract

The goal of Gas Turbine Performance Diagnostics is to accurately detect, isolate and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. Discernable shifts in engine speeds, temperatures, pressures, fuel flow, etc., provide the requisite information for determining the underlying shift in engine operation from a presumed nominal state. Historically, this type of analysis was performed through the use of a Kalman Filter or one of its derivatives to simultaneously estimate a plurality of engine faults. In the past decade, Artificial Neural Networks (ANN) have been employed as a pattern recognition device to accomplish the same task. Both methods have enjoyed a reasonable success.

Copyright © 2000 by ASME

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