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Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study

[+] Author Affiliations
Igor Loboda

National Polytechnic Institute, Mexico City, Mexico

Yakov Feldshteyn

Compressor Controls Corporation, Des Moines, IA

Paper No. GT2010-23749, pp. 417-427; 11 pages
  • ASME Turbo Expo 2010: Power for Land, Sea, and Air
  • Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine
  • Glasgow, UK, June 14–18, 2010
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4398-7 | eISBN: 978-0-7918-3872-3
  • Copyright © 2010 by ASME


Gas turbine health monitoring includes the common stages of problem detection, fault identification, and prognostics. To extract useful diagnostic information from raw recorded data, these stages require a preliminary operation of computing differences between measurements and an engine baseline, which is a function of engine operating conditions. These deviations of measured values from the baseline data can be good indicators of engine health. However, their quality and success of all diagnostic stages strongly depend on an adequacy of the baseline model employed and, in particular, on mathematical techniques applied to create it. To create the baseline model we have applied polynomials and the least square method for computing their coefficients over a long period of time. Some methods were proposed to enhance such a polynomial-based model. The resulting accuracy was sufficient for reliable monitoring gas turbine deterioration effects. The polynomials previously investigated enough are used in the present study as a standard for evaluating artificial neural networks, a very popular technique in gas turbine diagnostics. The focus of this comparative study is to verify whether the use of networks results in a better description of the engine baseline. Extensive field data of two different industrial gas turbines were used to compare these two techniques in various conditions. The deviations were computed for all available data and quality of the resulting deviations plots was compared visually. A mean error of the baseline model was an additional criterion for the comparing the techniques. To find the best network configurations many network variations were realized and compared with the polynomials. Although the neural networks were found to be close to the polynomials in accuracy, they could not exceed the polynomials in any variation. In this way, it seems that polynomials can be successfully used for engine monitoring, at least for the analyzed gas turbines.

Copyright © 2010 by ASME



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