Hybrid Model-Driven Spectroscopic Network for Rapid Retrieval of Turbine Exhaust Temperature
Hybrid Model-Driven Spectroscopic Network for Rapid Retrieval of Turbine Exhaust Temperature
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DOI：
10.1109/tim.2023.3328086
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发表时间：
2023
影响因子：
5.6
通讯作者：
Yale Fu;Rui Zhang;Jiangnan Xia;Andrew Gough;Stuart Clark;Abhishek Upadhyay;Godwin Enemali;I. Armstrong;Ihab Ahmed;M. Pourkashanian;Paul Wright;K. Ozanyan;M. Lengden;Walter Johnstone;N. Polydorides;Hugh McCann;Chang Liu
中科院分区：
文献类型：
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作者：
Yale Fu;Rui Zhang;Jiangnan Xia;Andrew Gough;Stuart Clark;Abhishek Upadhyay;Godwin Enemali;I. Armstrong;Ihab Ahmed;M. Pourkashanian;Paul Wright;K. Ozanyan;M. Lengden;Walter Johnstone;N. Polydorides;Hugh McCann;Chang Liu
排气温度是诊断燃气涡轮机健康状况的关键参数。在这篇文章中，我们提出了一个模型驱动的光谱网络具有很强的泛化能力，以监测EGT快速，准确。所提出的网络依赖于从经过充分验证的温度测量技术获得的数据，即，波长调制光谱（WMS），引入了一个基本的物理吸收模型，并建立了一个混合数据集从模拟和实验的新奇性。这种混合模型驱动（HMD）网络使神经网络对真实世界的实验数据具有很强的抗噪性。建议的网络进行评估，在现场测量的EGT的航空GTE在毫秒级的时间响应。实验结果表明，该网络大大优于以前的神经网络方法的准确性和精度的测量EGT时，GTE是稳定加载。
Exhaust gas temperature (EGT) is a key parameter in diagnosing the health of gas turbine engines (GTEs). In this article, we propose a model-driven spectroscopic network with strong generalizability to monitor the EGT rapidly and accurately. The proposed network relies on data obtained from a well-proven temperature measurement technique, i.e., wavelength modulation spectroscopy (WMS), with the novelty of introducing an underlying physical absorption model and building a hybrid dataset from simulation and experiment. This hybrid model-driven (HMD) network enables strong noise resistance of the neural network against real-world experimental data. The proposed network is assessed by in situ measurements of EGT on an aero-GTE at millisecond-level temporal response. Experimental results indicate that the proposed network substantially outperforms previous neural-network methods in terms of accuracy and precision of the measured EGT when the GTE is steadily loaded.