This paper presents a successful demonstration of application of neural networks to perform various data mining functions on an RB211 gas-turbine-driven compressor station. Radial basis function networks were optimized and were capable of performing the following functions: (a) backup of critical parameters, (b) detection of sensor faults, (c) prediction of complete engine operating health with few variables, and (d) estimation of parameters that cannot be measured. A Kohonen SOM technique has also been applied to recognize the correctness and validity of any data once the network is trained on a good set of data. This was achieved by examining the activation levels of the winning unit on the output layer of the network. Additionally, it would also be possible to determine the suspicious, faulty or corrupted parameter(s) in the cases which are not recognized by the network by simply examining the activation levels of the input neurons.
A Demonstration of Artificial Neural-Networks-Based Data Mining for Gas-Turbine-Driven Compressor Stations
Contributed by the International Gas Turbine Institute (IGTI) of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Paper presented at the International Gas Turbine and Aeroengine Congress and Exhibition, Munich, Germany, May 8–11, 2000; Paper 00-GT-351. Manuscript received by IGTI, November 1999; final revision received by ASME Headquarters, February 2000. Associate Editor: D. Wisler.
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Botros, K. K., Kibrya , G., and Glover, A. (March 26, 2002). "A Demonstration of Artificial Neural-Networks-Based Data Mining for Gas-Turbine-Driven Compressor Stations ." ASME. J. Eng. Gas Turbines Power. April 2002; 124(2): 284–297. https://doi.org/10.1115/1.1414130
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