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2023 Vol.28, Issue 3 Preview Page

Research Article

30 September 2023. pp. 11-19
J. Beita, M. Talibi, S. Sadasivuni, R. Balachandran, Thermoacoustic instability considerations for high hydrogen combustion in lean premixed gas turbine combustor, Hydrogen, 2 (2021), 33-57. 10.3390/hydrogen2010003
D. Kim, Review on the Development Trend of Hydrogen Gas Turbine Combustion Technology, J. Korean Soc. Combust. 24(4) (2019), 1-10. 10.15231/jksc.2019.24.4.001
B. Noble, L. Angello, B. Emerson, T. Lieuwen, Advanced gas turbine combustor health monitoring using combustion dynamics data, high pressure condition, 59th ISA POWID/EPRI Symposium (2016).
J. Oh, Y. Yoon, Combustion Instability in Gas Turbine Engines, J. Korean Soc. Propulsion Engineers, 12(4) (2008), 63-77.
S. Joo, J. Choi, M. Lee, N. Kim, Prognosis of combustion instability in a gas turbine combustor using spectral centroid & spread, Energy, 224 (2021), 120180. 10.1016/
W. Song, D. Cha, Temporal kurtosis of dynamic pressure signal as a quantitative measure of combustion instability, Appl. Therm. Eng. 104 (2016), 577-586. 10.1016/j.applthermaleng.2016.05.094
W. Song, D. Cha, Combustion Instability Assessment Using Temporal Kurtosis of Dynamic Pressure Data, The 48th KOSCO Symposium, (2014), 39-41.
S. Choi, J. Baek, D. Kim, Early Diagnosis of Combustion Instability Using Statistical Methods, J. Korean Soc. Combust. 27(3) (2022), 1-7. 10.15231/jksc.2022.27.3.001
S. Joo, J. Choi, N. Kim, M. Lee, Zero-crossing rate method as an efficient tool for combustion instability diagnosis, Exp. Therm Fluid Sci. 123 (2021), 110340. 10.1016/j.expthermflusci.2020.110340
B. Jun, D. Jang, M. Lee, Combustion instability diagnosis using machine learning methodology of high-speed flame images for the safe operation of a gas turbine combustor, Trans. Korean Soc. Mech. Eng. B 45 (2021), 447-458. 10.3795/KSME-B.2021.45.8.447
T. Gangopadhyay, V. Ramanan, A. Akintayo, P.K. Boor, S. Sarkar, S.R. Chakravarthy, S. Sarkar, 3D convolutional selective autoencoder for instability detection in combustion systems, Energy and AI 4 (2021), 100067. 10.1016/j.egyai.2021.100067
G. Dong, G. Liao, H. Liu, G. Kuang, A review of the autoencoder and its variants: A comparative perspective from target recognition in synthetic-aperture radar images, IEEE GEOSCI REMOTE M 6 (2018), 44-68. 10.1109/MGRS.2018.2853555
C. Fan, F. Xiao, Y. Zhao, J. Wang, Analytical investigation of autoencoder based methoeds for unsupervised anomaly detetion in building energy data, Applied Energy 211 (2018), 1123-1135. 10.1016/j.apenergy.2017.12.005
G. Lee, M. Jung, M. Song, J. Choo, Unsupervised anomaly detection of the gas turbine operation via convolutional auto-encoder, 2020 IEEE International Conference on Prognostics and Health Management (2020), 19950224. 10.1109/ICPHM49022.2020.9187054
F. Fleurt, François Fleuret's deep-learning courses 14x050, the University of Geneva, Switzerland, Available at
C. Lee, S. Lee, P. Kim, Fault detection and diagnosis of chain transmission system using convolutional auto-encoder, Trans. the KSNVE 31 (2021), 563-573. 10.5050/KSNVE.2021.31.5.563
J. An, S. Cho, Variational autoencoder based anomaly detection using reconstruction probability, SNU Data Mining Center (2015).
Tensorflow 2.9.1, Available at
S. Joo, S. Kwak, S. Kim, J. Lee, Y. Yoon, High-frequency transition characteristics of synthetic natural gas combustion in gas turbine, Aeronaut. J. 123 (2019), 138-156. 10.1017/aer.2018.150
  • Publisher :The Korean Society of Combustion
  • Publisher(Ko) :한국연소학회
  • Journal Title :Journal of the Korean Society of Combustion
  • Journal Title(Ko) :한국연소학회지
  • Volume : 28
  • No :3
  • Pages :11-19
  • Received Date : 2023-03-28
  • Revised Date : 2023-04-14
  • Accepted Date : 2023-08-14