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A Generalized Fault Classification for Gas Turbine Diagnostics on Steady States and Transients

[+] Author Affiliations
Igor Loboda

National Polytechnic Institute, Mexico City, Mexico

Sergey Yepifanov

National Aerospace University, Kharkov, Ukraine

Yakov Feldshteyn

Compressor Controls Corporation, Des Moines, IA

Paper No. GT2006-90723, pp. 725-734; 10 pages
  • 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


Gas turbine diagnostic techniques are often based on the recognition methods using the deviations between actual and expected thermodynamic performances. The problem is that the deviations depend on real operating conditions. However, our studies show that such a dependency can be reduced. In this paper, we propose the generalized fault classification that is independent of the operating conditions. To prove this idea, the averaged probabilities of the correct diagnosis are computed and compared for two cases: the proposed classification and the traditional one based on the fixed operating conditions. The probabilities are calculated through a stochastic modeling of the diagnostic process, in which a thermodynamic model generates deviations that are induced by the faults. Artificial neural networks recognize these faults. The proposed classification principle has been realized for both, steady state and transient operation of the gas turbine units. The results show that the acceptance of the generalized classification practically does not reduce the diagnosis trustworthiness.

Copyright © 2006 by ASME



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