Affiliation:
1. ENDIF Engineering Department in Ferrara, University of Ferrara, Via Saragat, 1-44100 Ferrara, Italy
Abstract
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.
Subject
Mechanical Engineering,Energy Engineering and Power Technology,Aerospace Engineering,Fuel Technology,Nuclear Energy and Engineering
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