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

Fu, Yalei and Zhang, Rui and Xia, Jiangnan and Gough, Andrew and Clark, Stuart and Upadhyay, Abhishek and Enemali, Godwin and Armstrong, Ian and Ahmed, Ihab and Pourkashanian, Mohamed and Wright, Paul and Ozanyan, Krikor and Lengden, Michael and Johnstone, Walter and Polydorides, Nick and McCann, Hugh and Liu, Chang (2023) Hybrid model-driven spectroscopic network for rapid retrieval of turbine exhaust temperature. IEEE Transactions on Instrumentation and Measurement, 72. pp. 1-10. 2531710. ISSN 1557-9662 (https://doi.org/10.1109/TIM.2023.3328086)

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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.

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