Machine learning-enhanced laser absorption spectroscopy for harsh-environment combustion diagnosis

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Chen, Yuan and Xia, Jiangnan and Zhang, Rui and Xia, Yikai and Zhou, Minqiu and Fu, Yalei and Ahmed, Ihab and Armstrong, Ian and Upadhyay, Abhishek and Lengden, Michael and Johnstone, Walter and Wright, Paul and Ozanyan, Krikor and Pourkashanian, Mohamed and McCann, Hugh and Liu, Chang (2025) Machine learning-enhanced laser absorption spectroscopy for harsh-environment combustion diagnosis. IEEE Transactions on Instrumentation and Measurement, 74. pp. 1-10. 7011110. ISSN 0018-9456 (https://doi.org/10.1109/tim.2025.3586367)

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Abstract

Laser absorption spectroscopy (LAS) has been widely adopted as a diagnostic tool for reactive flow-field monitoring in industrial combustion applications. Despite various advancements in LAS signal processing schemes, these harsh environments inevitably impose noise and interference on the LAS measurement data, thus increasing inaccuracy and uncertainty in combustion analysis. This article proposes a machine learning (ML)-enhanced LAS methodology that significantly mitigates noise-induced distortions in absorption spectra, yielding a more accurate representation of the original spectral sequence through continuous measurements. The proposed method is a novel architecture that integrates a denoising autoencoder (DAE) with a long short-term memory (LSTM) network for enhanced LAS signal analysis. Developed entirely using in situ experimental data, this approach ensures strong portability for industrial combustion diagnostics, where multisource measurement noise is difficult to model or quantify. To validate the proposed method, we conducted a combustion experiment on an auxiliary power unit (APU), a full-scale commercial gas turbine aeroengine, focusing on exhaust temperature measurements using our advanced LAS technique. The experimental results demonstrate the efficacy of the proposed method in recovering high-fidelity absorption spectra from the noise-contaminated data, enabling more convenient, accurate, and stable APU exhaust temperature measurements with a standard deviation below 7.9°C. This indicates its significant potential in industrial combustion diagnostics, offering a reliable tool for precise analysis and assessment in harsh environments.

ORCID iDs

Chen, Yuan, Xia, Jiangnan, Zhang, Rui, Xia, Yikai, Zhou, Minqiu, Fu, Yalei, Ahmed, Ihab, Armstrong, Ian ORCID logoORCID: https://orcid.org/0009-0003-1652-7990, Upadhyay, Abhishek, Lengden, Michael ORCID logoORCID: https://orcid.org/0000-0002-9122-9462, Johnstone, Walter ORCID logoORCID: https://orcid.org/0000-0002-6376-9445, Wright, Paul, Ozanyan, Krikor, Pourkashanian, Mohamed, McCann, Hugh and Liu, Chang;
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