A novel diagnostic method based on filter bank theory for fast and accurate detection of thermoacoustic instability

Joo, Seung–Ki; Choi, Jongwun; Kim, Nam Keun; Lee, Min Chul

doi:10.1038/s41598-020-80427-6

Cited by 4 publications

(2 citation statements)

References 59 publications

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“…Signal processing techniques, such as FFT and spectral analysis, are applied to acoustic and pressure signals to identify specific frequencies associated with thermoacoustic oscillations [12]. This helps in quantifying the characteristics of instability.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network

Zeng,

Hu,

Sun

et al. 2024

Symmetry

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Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network (DBN). Six samples of combustion state data were collected using two distinct types of burners to facilitate the training and validation of GA-DBN. The proposed methodology leverages RQA to extract intricate patterns and dynamic features from time series data representing combustion behavior. By quantifying the recurrence plot of specific patterns, the analysis provides valuable insights into the underlying thermoacoustic dynamics. Among three different feature extraction methods, RQA stands out remarkably in performance. These RQA-derived features serve as input to a carefully tuned DBN, which is trained to learn the complex relationships within the combustion process. The classification accuracy of deep belief network optimized by genetic algorithm (GA-DBN) reached an impressive 99.8%. Subsequent multiple comparisons were conducted between GA-DBN, DBN, and support vector machine (SVM), revealing that GA-DBN consistently demonstrated satisfactory classification results. This method holds significant importance in monitoring intricate combustion states.

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Section: Introductionmentioning

confidence: 99%

Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network

Zeng,

Hu,

Sun

et al. 2024

Symmetry

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“…At present, most combustion monitoring systems are designed based on the frequency-domain treatment of a signal. 14,15 And the amplitude of the dominant mode of the acoustic pressure from the Fourier transform spectrum is generally adopted for precursors of combustion instability. However, due to the inherent low-frequency spectral resolution defect of short-time data, the frequency domain transformation of transient signals (such as the dynamic pressure signal at the beginning of combustion oscillation) usually loses the time details of the signal.…”

Section: Introductionmentioning

confidence: 99%

Prediction of acoustic pressure of thermoacoustic combustion instability based on Elman neural network

Zeng

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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Accurate prediction of thermoacoustic instability is a prerequisite for thermoacoustic control to avoid the damage of combustion chamber, however, this problem has not been completely solved yet. This paper proposes a data-driven method based on the Elman neural network (ENN) to predict the value of acoustic pressure of combustion instability. As a comparison, a model based on support vector machine (SVM) was built. It is proved that ENN has better prediction performance with a certain predicted time horizon compared to the SVM method. What is more, the prediction model based on ENN can adapt to time-varying characteristics of the transition scenario which is characterized by amplitude modulation, multiple frequencies, and irregular bursts. ENN model still maintains enough prediction accuracy for various input training sets, indicating that ENN can fully mine the features of data and has a strong feature extraction ability in combustion oscillation prediction. Hence, it is demonstrated that ENN is a promising prediction tool for thermoacoustic instability under various combustion conditions. These findings are of great significance for the accurate prediction and control of thermoacoustic instability.

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Novel combustion instability diagnosis method in a hydrogen/natural gas co-firing gas turbine combustor using a combination of four criteria: Temporal kurtosis, permutation entropy, energy of entropy, and zero-crossing rate

Jang,

Joo,

Kim

et al. 2024

International Journal of Hydrogen Energy

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A novel diagnostic method based on filter bank theory for fast and accurate detection of thermoacoustic instability

Cited by 4 publications

References 59 publications

Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network

Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network

Prediction of acoustic pressure of thermoacoustic combustion instability based on Elman neural network

Novel combustion instability diagnosis method in a hydrogen/natural gas co-firing gas turbine combustor using a combination of four criteria: Temporal kurtosis, permutation entropy, energy of entropy, and zero-crossing rate

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