Hybrid Model-Driven Spectroscopic Network for Rapid Retrieval of Turbine Exhaust Temperature

• Published in: IEEE transactions on instrumentation and measurement 2023, Vol.72, p.1-10
• Main Authors: Fu, Yalei, Zhang, Rui, Xia, Jiangnan, Gough, Andrew, Clark, Stuart, Upadhyay, Abhishek, Enemali, Godwin, Armstrong, Ian, Ahmed, Ihab, Pourkashanian, Mohamed, Wright, Paul, Ozanyan, Krikor, Lengden, Michael, Johnstone, Walter, Polydorides, Nick, McCann, Hugh, Liu, Chang
• Format: Article
• Language: English
• Subjects: Absorption; Data models; Deep neural network (DNN); exhaust gas temperature (EGT); Exhaust gases; Fitting; Gas lasers; Gas temperature; gas turbine engine (GTE); Gas turbine engines; In situ measurement; Measurement by laser beam; Neural networks; signal processing; Spectroscopy; Temperature measurement; Wavelength modulation; wavelength modulation spectroscopy (WMS)
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3328086

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.