Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach
Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach
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DOI：
10.1115/1.2431391
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发表时间：
2007-07
影响因子：
1.5
通讯作者：
R. Bettocchi;M. Pinelli;P. R. Spina;M. Venturini
中科院分区：
文献类型：
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作者：
R. Bettocchi;M. Pinelli;P. R. Spina;M. Venturini
本文研究和发展了燃气轮机诊断的神经网络模型。所进行的分析旨在选择最合适的神经网络结构用于燃气轮机诊断，就神经网络训练阶段的计算时间，精度和相对于测量不确定性的鲁棒性而言。特别是，考虑并测试了使用反向传播学习算法训练的具有单个隐藏层的前馈神经网络。此外，将多输入/多输出神经网络架构（即计算所有系统输出的神经网络）与多输入/单输出神经网络进行比较，每个神经网络计算系统的单个输出。得到的结果表明，如果使用足够数量的训练模式，神经网络在测量不确定性方面具有足够的鲁棒性。此外，用测量误差损坏的数据训练的多输入/多输出神经网络似乎是神经网络训练阶段所需的计算时间和执行燃气轮机诊断的神经网络精度之间的最佳折衷。
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.