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TECHNICAL PAPERS: Gas Turbines: Structures and Dynamics

Neural Network Emulation of a Magnetically Suspended Rotor

[+] Author and Article Information
A. Escalante, V. Guzmán, M. Parada, L. Medina, S. E. Diaz

Universidad Simon Bolivar, Decanato de Investigacion y Desarrollo, Caracas 1080-A, Venezuela

J. Eng. Gas Turbines Power 126(2), 373-384 (Jun 07, 2004) (12 pages) doi:10.1115/1.1689363 History: Received December 01, 2001; Revised March 01, 2002; Online June 07, 2004
Copyright © 2004 by ASME
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References

Figures

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Node or artificial neuron
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The multilayer perceptron
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System Identification procedure
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Magnetic bearing working principle
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Experimental setup lock diagram
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w and v-axis identification
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Single axis control scheme
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Ramp No. 2 training sequence (normalized amplitude versus number of samples)
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Validation sequence, 2000 samples per channel, with “double unbalance” and running at 3000 rpm. (3000 rpm2d) (normalized amplitude versus number of samples).
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Artificial neural network structure
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Autocorrelation functions, ANN trained with Ramp1 and tested with 3000 rpm validation sequence (correlations values versus number of delayed samples)
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Autocorrelation functions, ANN trained with Ramp2 and tested with 3000 rpm validation sequence (correlations values versus number of delayed samples)
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Autocorrelation functions, ANN trained with Ramp3 and tested with 3000 rpm validation sequence (correlations values versus number of delayed samples)
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Autocorrelation function, ANN trained with Ramp4 and tested with 3000 rpm validation sequence (correlations values versus number of delayed samples)
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TCV prediction error ANN trained with Ramp2 using the Levenberg-Marquardt method (currents values in A. versus number of samples).
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TCV correlation functions. ANN trained with Ramp2, using the Levenberg-Marquardt method (correlations values versus number of delayed samples).
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TCV output. ANN trained with Ramp2, using the back-propagation algorithm (currents values in A. versus number of samples).
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TCV correlation functions. ANN trained with Ramp2, using the back-propagation algorithm (correlations values versus number of delayed samples).
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TCW output, testing done with 3000 rpm sequence (currents values in A. versus number of samples)
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Correlation functions, TCW output, testing done with 3000 rpm sequence (correlations values versus number of delayed samples)
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TCW output, testing done with 3000 rpmd sequence (currents values in A. versus number of samples)
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Correlation functions, TCW output, testing done with 3000 rpmd sequence (correlations values versus number of delayed samples)
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TCW output, testing done with 3000 rpm2d sequence (currents values in A. versus number of samples)
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Correlation functions, TCW output, testing done with 3000 rpm2d sequence (correlations values versus number of delayed samples)

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