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Artificial Intelligence for the Diagnostics of Gas Turbines: Part I — Neural Network Approach

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

University of Ferrara, Ferrara, Italy

P. R. Spina

University of Bologna, Bologna, Italy

Paper No. GT2005-68026, pp. 9-18; 10 pages
  • ASME Turbo Expo 2005: Power for Land, Sea, and Air
  • Volume 4: Turbo Expo 2005
  • Reno, Nevada, USA, June 6–9, 2005
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4727-6 | eISBN: 0-7918-3754-8
  • Copyright © 2005 by ASME


In the paper, Neural Network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, in terms of computational time of the NN training phase, accuracy and robustness with respect to measurement uncertainty. In particular, feed-forward NNs with a single hidden layer trained by using a back-propagation learning algorithm are considered and tested. Moreover, Multi-Input/Multi-Output NN architectures (i.e. NNs calculating all the system outputs) are compared to Multi-Input/Single-Output NNs, each of them calculating a single output of the system. The results obtained show that NNs are robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, Multi-Input/Multi-Output NNs trained with data corrupted with measurement errors seem to be the best compromise between the computational time required for NN training phase and the NN accuracy in performing gas turbine diagnostics.

Copyright © 2005 by ASME



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