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Open AccessFeature PaperArticle

Combustion Instability Monitoring through Deep-Learning-Based Classification of Sequential High-Speed Flame Images

1
Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
2
Department of Mechanical Engineering, Incheon National University, Incheon 22012, Korea
3
Department of Safety Engineering, Incheon National University, Incheon 22012, Korea
4
Fire Disaster Prevention Research Center, Incheon National University, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 848; https://doi.org/10.3390/electronics9050848
Received: 27 April 2020 / Revised: 18 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Deep Learning Based Object Detection)
In this study, novel deep learning models based on high-speed flame images are proposed to diagnose the combustion instability of a gas turbine. Two different network layers that can be combined with any existing backbone network are established—(1) An early-fusion layer that can learn to extract the power spectral density of subsequent image frames, which is time-invariant under certain conditions. (2) A late-fusion layer which combines the outputs of a backbone network at different time steps to predict the current combustion state. The performance of the proposed models is validated by the dataset of high speed flame images, which have been obtained in a gas turbine combustor during the transient process from stable condition to unstable condition and vice versa. Excellent performance is achieved for all test cases with high accuracy of 95.1–98.6% and a short processing time of 5.2–12.2 ms. Interestingly, simply increasing the number of input images is as competitive as combining the proposed early-fusion layer to a backbone network. In addition, using handcrafted weights for the late-fusion layer is shown to be more effective than using learned weights. From the results, the best combination is selected as the ResNet-18 model combined with our proposed fusion layers over 16 time-steps. The proposed deep learning method is proven as a potential tool for combustion instability identification and expected to be a promising tool for combustion instability prediction as well. View Full-Text
Keywords: combustion instability; flame imaging; deep learning; residual network; power spectral density; temporal smoothing combustion instability; flame imaging; deep learning; residual network; power spectral density; temporal smoothing
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Choi, O.; Choi, J.; Kim, N.; Lee, M.C. Combustion Instability Monitoring through Deep-Learning-Based Classification of Sequential High-Speed Flame Images. Electronics 2020, 9, 848.

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