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

Abstract

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.

Feature selection and feature learning in machine learning applications for gas turbines: A review

“…The researchers discussed that although normal pattern extraction can be automatically performed through RL algorithms, it requires data to cover all ranges of operating conditions, which is infeasible in many cases. Choi et al (2020) presented a combustion stability monitoring pipeline that classifies the current combustion state and predicts the combustion state of the next step using CNN, ResNet, and RNN. Manual derivation processes were implemented to extract the per-pixel power spectral density of the flame images.…”

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

“…Therefore, many methods are used in the diagnosis, monitoring, and control of the combustion process, among which can be highlighted the Fourier transform [43], wavelet analysis [44], or recurrent neural networks [45][46][47][48][49][50]. Diagnostics of the combustion process can also use classification [51][52][53][54] and prediction [55,56].…”

Deep Learning Models for Automated Diagnosis of Retinopathy of Prematurity in Preterm Infants

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