Abstract

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.

Issue Section:
Gas Turbines: Oil and Gas Applications
Keywords:
data mining, neural nets, gas turbines, compressors, adaptive control, fault diagnosis
Topics:
Artificial neural networks, Compressors, Data mining, Engines, Parameter estimation, Sensors, Turbines, Gas turbines, Radial basis function networks, Errors
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Copyright © 2002
 by ASME
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