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.
Skip Nav Destination
ASME Turbo Expo 2010: Power for Land, Sea, and Air
June 14–18, 2010
Glasgow, UK
Conference Sponsors:
- International Gas Turbine Institute
ISBN:
978-0-7918-4398-7
PROCEEDINGS PAPER
Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study
Igor Loboda,
Igor Loboda
National Polytechnic Institute, Mexico City, Mexico
Search for other works by this author on:
Yakov Feldshteyn
Yakov Feldshteyn
Compressor Controls Corporation, Des Moines, IA
Search for other works by this author on:
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
Published Online:
December 22, 2010
Citation
Loboda, I, & Feldshteyn, Y. "Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study." Proceedings of the 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. pp. 417-427. ASME. https://doi.org/10.1115/GT2010-23749
Download citation file:
16
Views
0
Citations
Related Proceedings Papers
Related Articles
A Demonstration of Artificial Neural-Networks-Based Data Mining for
Gas-Turbine-Driven Compressor Stations
J. Eng. Gas Turbines Power (April,2002)
Measurement Selections for Multicomponent Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept
J. Eng. Gas Turbines Power (November,2011)
Real-Time Estimation of Gas Turbine Engine Damage Using a Control-Based Kalman Filter Algorithm
J. Eng. Gas Turbines Power (April,1992)
Related Chapters
Environmental Site Profiling: A Comparative Study
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Outlook
Closed-Cycle Gas Turbines: Operating Experience and Future Potential
Design-Point Calculations of Industrial Gas Turbines
Fundamentals of heat Engines: Reciprocating and Gas Turbine Internal Combustion Engines