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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

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

[+] Author and Article Information
R. Bettocchi, M. Pinelli, P. R. Spina, M. Venturini

ENDIF Engineering Department in Ferrara, University of Ferrara, Via Saragat, 1-44100 Ferrara, Italy

J. Eng. Gas Turbines Power 129(3), 711-719 (Sep 08, 2006) (9 pages) doi:10.1115/1.2431391 History: Received December 02, 2005; Revised September 08, 2006

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/multioutput 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 sufficiently robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, multi-input/multioutput 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.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 7

Standard deviation of the absolute relative error for different intervals of the normalized Euclidean distance d

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Figure 1

FIAT Avio 701F gas turbine model

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Figure 2

Influence of the number of outputs (MIMO and MISO) in the presence of measurement uncertainty—Number of training patterns: (a) 1000, (b) 2000, (c) 4000, and (d) 8000

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Figure 3

Success rate versus threshold values δ for MIMO NN trained by using 4000 patterns corrupted with measurement errors

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Figure 4

Number of generated patterns versus normalized Euclidean distance d

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Figure 5

Mean value of the absolute relative error for different intervals of the normalized Euclidean distance d

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Figure 6

Maximum absolute relative error for different intervals of the normalized Euclidean distance d

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