통계적 기법을 활용한 연소불안정 조기진단
Early Diagnosis of Combustion Instability Using Statistical Methods
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초록
Various statistical approaches have been recently proposed to detect combustion instabilities in a short time through a fast data processing on dynamic pressure of time domain measured in a gas turbine combustor. In the current study, four methods, PE(permutation entropy), TK(temporal kurtosis), ZCR(zero crossing rate), and STFT(short time Fourier transform), were applied and compared with the conventional instability onset-detection method, RMS(root mean square), using the same dynamic pressure data measured in a lab-scale gas turbine combustor. As a result of the comparison, it was found that PE, TK, and ZCR can detect combustion vibration earlier than the instability onset time determined by RMS. On the other hand, STFT did not show relatively good early detection characteristics
Abstract
Various statistical approaches have been recently proposed to detect combustion instabilities in a short time through a fast data processing on dynamic pressure of time domain measured in a gas turbine combustor. In the current study, four methods, PE(permutation entropy), TK(temporal kurtosis), ZCR(zero crossing rate), and STFT(short time Fourier transform), were applied and compared with the conventional instability onset-detection method, RMS(root mean square), using the same dynamic pressure data measured in a lab-scale gas turbine combustor. As a result of the comparison, it was found that PE, TK, and ZCR can detect combustion vibration earlier than the instability onset time determined by RMS. On the other hand, STFT did not show relatively good early detection characteristics
참고문헌 (18)
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