Artificial intelligence for the diagnostics of gas turbines - Part I: Neural network approach

被引:42
作者
Bettocchi, R. [1 ]
Pinelli, M. [1 ]
Spina, P. R. [1 ]
Venturini, M. [1 ]
机构
[1] Univ Ferrara, ENDIF Engn Dept Ferrara, I-44100 Ferrara, Italy
来源
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME | 2007年 / 129卷 / 03期
关键词
Computational time - Flow passage;
D O I
10.1115/1.2431391
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
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.
引用
收藏
页码:711 / 719
页数:9
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