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Artificial Intelligence for the Diagnostics of Gas Turbines: Part II — Neuro-Fuzzy Approach

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

University of Ferrara, Ferrara, Italy

P. R. Spina

University of Bologna, Bologna, Italy

Paper No. GT2005-68027, pp. 19-29; 11 pages
doi:10.1115/GT2005-68027
From:
  • 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

abstract

In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the set up of Neural Network (NN) models was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a Cycle Program, calibrated on a 255 MW single shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, such as efficiencies and characteristic flow passage areas of compressor and turbine) and the corresponding variations of the measured quantities with respect to the values in new and clean conditions. The analyses carried out are aimed at the selection of the most appropriate NFS structure for gas turbine diagnostics, in terms of computational time of the NFS training phase, accuracy and robustness towards measurement uncertainty during simulations. In particular, Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures were considered and tested, and their performance was compared to that obtainable by using the NN models. An analysis was also performed in order to identify the most significant ANFIS inputs. The results obtained show that ANFISs are robust with respect to measurement uncertainty, and, in all the cases analyzed, the performance (in terms of accuracy during simulations and time spent for the training phase) proved to be better than that obtainable by MIMO and MISO Neural Networks trained and tested on the same data.

Copyright © 2005 by ASME

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