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Detection of Gas Turbines Malfunctions From Emission Concentration Distributions

[+] Author Affiliations
C. Romessis, K. Mathioudakis

National Technical University of Athens, Athens, Greece

Paper No. GT2007-27107, pp. 515-522; 8 pages
  • ASME Turbo Expo 2007: Power for Land, Sea, and Air
  • Volume 1: Turbo Expo 2007
  • Montreal, Canada, May 14–17, 2007
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4790-X | eISBN: 0-7918-3796-3
  • Copyright © 2007 by ASME


A method for detecting gas turbines malfunctions through engine emissions concentration plots is presented. The method is materialized through the use of a bank of Probabilistic Neural Networks (PNNs). The main idea comes from the fact that specific operating and health conditions of an engine lead to specific concentrations of emissions on the exhaust area. By comparison of an emission concentration plot with emission plots of known engine health conditions, diagnostic conclusions can be extracted. The stochastic nature of emission concentrations can be handled by PNNs, a specific type of Artificial Neural Networks which are known to be efficient probabilistic classifiers. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the PNNs, follows. The case of an operating family of turbofan engines is used to evaluate the effectiveness of the method. The examined case demonstrates that the proposed method can act as an additional tool on the existing methods for better and safer fault diagnosis.

Copyright © 2007 by ASME



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