Hybrid model-driven spectroscopic network for rapid retrieval of turbine exhaust temperature

Yalei Fu, Rui Zhang, Jiangnan Xia, Andrew Gough, Stuart Clark, Abhishek Upadhyay, Godwin Enemali, Ian Armstrong, Ihab Ahmed, Mohamed Pourkashanian, Paul Wright, Krikor Ozanyan, Michael Lengden, Walter Johnstone, Nick Polydorides , Hugh McCann, Chang Liu

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Abstract

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.

Original languageEnglish
Article number2531710
Number of pages10
JournalIEEE Transactions on Instrumentation and Measurement
Volume72
Early online date27 Oct 2023
DOIs
Publication statusPublished - 9 Nov 2023

Keywords

  • deep neural network
  • signal processing
  • gas turbine engine
  • exhaust gas temperature
  • wavelength modulation spectroscopy

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