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Assessment of the Robustness of Gas Turbine Diagnostics Tools Based on Neural Networks

[+] Author Affiliations
R. Bettocchi, M. Pinelli, M. Venturini

University of Ferrara, Ferrara, Italy

P. R. Spina

University of Bologna, Bologna, Italy

G. A. Zanetta

CESI SpA, Milano, Italy

Paper No. GT2006-90118, pp. 603-613; 11 pages
doi:10.1115/GT2006-90118
From:
  • ASME Turbo Expo 2006: Power for Land, Sea, and Air
  • Volume 4: Cycle Innovations; Electric Power; Industrial and Cogeneration; Manufacturing Materials and Metallurgy
  • Barcelona, Spain, May 8–11, 2006
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4239-8
  • Copyright © 2006 by ASME

abstract

The paper deals with the set-up and the application of an Artificial Intelligence technique based on Neural Networks (NNs) to gas turbine diagnostics, in order to evaluate its capabilities and its robustness. The data used for both training and testing the NNs were generated by means of a Cycle Program, calibrated on a Siemens V94.3A gas turbine. Such data are representative of operating points characterized by different boundary, load and health state conditions. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, by evaluating NN robustness with respect to: • interpolation capability and accuracy in the presence of data affected by measurement errors; • extrapolation capability in the presence of data lying outside the range of variation adopted for NN training; • accuracy in the presence of input data corrupted by bias errors; • accuracy when one input is not available. This situation is simulated by replacing the value of the unavailable input with its nominal value.

Copyright © 2006 by ASME

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