Early Detection of Thermoacoustic Instabilities Using Hidden Markov Models

References

• Akaike, H. 1974. A new look at the statistical model identification. IEEE Trans. Autom. Control, 19(6), 716–723. doi:10.1109/TAC.1974.1100705.
   Web of Science ®Google Scholar
• Bishop, C. 2007. Pattern Recognition and Machine Learning, Springer, New York, NY.
   Google Scholar
• Boujo, E., Denisov, A., Schuermans, B., and Noiray, N. 2016. Quantifying acoustic damping using flame chemiluminescence. J. Fluid Mech., 808, 245–257. doi:10.1017/jfm.2016.663.
   Web of Science ®Google Scholar
• Celeux, G., and Durand, J.-B. 2008. Selecting hidden Markov model state number with cross-validated likelihood. Comput. Stat., 23(4), 541–564. doi:10.1007/s00180-007-0097-1.
   Web of Science ®Google Scholar
• Chen, J., Hsu, T.-Y., Chen, C.-C., and Cheng, Y.-C. 2011. Online predictive monitoring using dynamic imaging of furnaces with the combinational method of multiway principal component analysis and hidden Markov model. Ind. Eng. Chem. Res., 50(5), 2946–2958. doi:10.1021/ie100671j.
   Web of Science ®Google Scholar
• Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognit. Lett., 27(8), 861–874. ROC Analysis in Pattern Recognition. doi:10.1016/j.patrec.2005.10.010.
   Web of Science ®Google Scholar
• Gelbert, G., Moeck, J.P., Paschereit, C.O., and King, R. 2012. Feedback control of unstable thermoacoustic modes in an annular rijke tube. Control Eng. Pract., 20(8), 770–782. doi:10.1016/j.conengprac.2012.03.016.
   Web of Science ®Google Scholar
• Gopalakrishnan, E.A., and Sujith, R.I. 2014. Influence of system parameters on the hysteresis characteristics of a horizontal rijke tube. Int. J. Spray Combust. Dyn., 6(3), 293–316. doi:10.1260/1756-8277.6.3.293.
   Web of Science ®Google Scholar
• Gotoda, H., Amano, M., Miyano, T., Ikawa, T., Maki, K., and Tachibana, S. 2012. Characterization of complexities in combustion instability in a lean premixed gas-turbine model combustor. Chaos: Interdiscip. J. Nonlinear Sci., 22(4), 043128. doi:10.1063/1.4766589.
   PubMed Web of Science ®Google Scholar
• Hajek, B. 2015. Random Processes for Engineers, Cambridge University Press, Cambridge, UK.
   Google Scholar
• Hummel, T., Berger, F., Stadlmair, N., Schuermans, B., and Sattelmayer, T. 2017. Extraction of linear growth and damping rates of high-frequency thermoacoustic oscillations from time domain data. ASME Turbo Expo: Power for Land, Sea, and Air, Vol. 4A, Combustion, Fuels and Emissions. doi:10.1115/GT2017-64233. ISBN: 978-0-7918-5084-8
   Google Scholar
• Jamieson, N.P., Rigas, G., and Juniper, M.P. 2017. Experimental sensitivity analysis via a secondary heat source in an oscillating thermoacoustic system. Int. J. Spray Combust. Dyn., 9(4), 230–240. doi:10.1177/1756827717696325.
   Web of Science ®Google Scholar
• Laera, D., Campa, G., Camporeale, S.M., Bertolotto, E., Rizzo, S., Bonzani, F., Ferrante, A., and Saponaro, A. 2014. Modelling of thermoacoustic combustion instabilities phenomena: application to an experimental test rig. Energy Procedia, 45, 1392–1401. ATI 2013-68th Conference of the Italian Thermal Machines Engineering Association. doi:10.1016/j.egypro.2014.01.146.
   Google Scholar
• Li, X., Zhao, D., Yang, X., Wen, H., Jin, X., Li, S., Zhao, H., Xie, C., and Liu, H. 2016. Transient growth of acoustical energy associated with mitigating thermoacoustic oscillations. Appl. Energy, 169, 481–490. doi:10.1016/j.apenergy.2016.01.060.
   Web of Science ®Google Scholar
• Lieuwen, T. 2005. Online combustor stability margin assessment using dynamic pressure data. J. Eng. Gas Turbines Power, 127(3), 478–482. doi:10.1115/1.1850493.
   Web of Science ®Google Scholar
• Lieuwen, T.C. 2003. Statistical characteristics of pressure oscillations in a premixed combustor. J. Sound Vib., 260(1), 3–17. doi:10.1016/S0022-460X(02)00895-7.
   Web of Science ®Google Scholar
• Lieuwen, T.C., and Yang, V. 2005. Combustion instabilities in gas turbine engines: operational experience, fundamental mechanisms, and modeling. Am. Inst. Aeronautics Astronautics, 1, 3–26. chapter
   Google Scholar
• Matveev, K. (2003), Thermoacoustic instabilities in the Rijke tube: experiments and modeling, PhD thesis, California Institute of Technology.
   Google Scholar
• Menon, S., Uluyol, O., Kim, K., and Nwadiogbu, E.O. (2003), Incipient fault detection and diagnosis in turbine engines using hidden Markov models.
   Google Scholar
• Moeck, J., Bothien, M., Paschereit, C., Gelbert, G., and King, R. (2007), Aerospace Sciences Meetings, American Institute of Aeronautics and Astronautics, chapter Two-Parameter Extremum Seeking for Control of Thermoacoustic Instabilities and Characterization of Linear Growth.
   Google Scholar
• Mondal, S., Bhattacharya, C., Chattopadhyay, P., Mukhopadhyay, A., and Ray, A. (2017), AIAA propulsion and energy forum, American Institute of Aeronautics and Astronautics, Chapter: Prediction of Thermoacoustic Instabilities in a Premixed Combustor based on FFT-based Dynamic Characterization.
   Google Scholar
• Mukherjee, K., and Ray, A. 2014. State splitting and merging in probabilistic finite state automata for signal representation and analysis. Signal Process., 104, 105–119. doi:10.1016/j.sigpro.2014.03.045.
   Web of Science ®Google Scholar
• Murphy, K. 2012. Machine Learning: A Probabilistic Perspective, 1st ed.. Cambridge, MA, USA: The MIT Press
   Google Scholar
• Nair, V., and Sujith, R.I. 2014. Multifractality in combustion noise: predicting an impending combustion instability. J. Fluid Mech., 747, 635–655. doi:10.1017/jfm.2014.171.
   Web of Science ®Google Scholar
• Noiray, N., and Denisov, A. 2017. A method to identify thermoacoustic growth rates in combustion chambers from dynamic pressure time series. Proc. Combust. Inst., 36(3), 3843–3850. doi:10.1016/j.proci.2016.06.092.
   Web of Science ®Google Scholar
• Noiray, N., and Schuermans, B. 2012. Theoretical and experimental investigations on damper performance for suppression of thermoacoustic oscillations. J. Sound Vib., 331(12), 2753–2763. doi:10.1016/j.jsv.2012.02.005.
   Web of Science ®Google Scholar
• Noiray, N., and Schuermans, B. 2013. Deterministic quantities characterizing noise driven Hopf bifurcations in gas turbine combustors. Int. J. Non Linear Mech., 50, 152–163. doi:10.1016/j.ijnonlinmec.2012.11.008.
   Web of Science ®Google Scholar
• Poor, H.V. 2013. An Introduction to Signal Detection and Estimation. New York, NY, USA: Springer Science & Business Media.
   Google Scholar
• Rabiner, L., and Juang, B.-H. 1993. Fundamentals of Speech Recognition, Prentice-Hall, Inc., Upper Saddle River, NJ.
   Google Scholar
• Rabiner, L.R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77(2), 257–286. doi:10.1109/5.18626.
   Web of Science ®Google Scholar
• Rajagopalan, V., and Ray, A. 2006. Symbolic time series analysis via wavelet-based partitioning. Signal Process., 86(11), 3309–3320. doi:10.1016/j.sigpro.2006.01.014.
   Web of Science ®Google Scholar
• Ray, A. 2004. Symbolic dynamic analysis of complex systems for anomaly detection. Signal Process., 84(7), 1115–1130. doi:10.1016/j.sigpro.2004.03.011.
   Web of Science ®Google Scholar
• Rigas, G., Jamieson, N.P., Li, L.K.B., and Juniper, M.P. 2016. Experimental sensitivity analysis and control of thermoacoustic systems. J. Fluid Mech., 787. doi.org/10.1017/jfm.2015.715
   Web of Science ®Google Scholar
• Rydén, T. 1995. Estimating the order of hidden Markov models. Statistics, 26(4), 345–354. doi:10.1080/02331889508802501.
   Web of Science ®Google Scholar
• Sarkar, S., Chakravarthy, S.R., Ramanan, V., and Ray, A. 2016. Dynamic data-driven prediction of instability in a swirl-stabilized combustor. Int. J. Spray Combust. Dyn., 8(4), 235–253. doi:10.1177/1756827716642091.
   Web of Science ®Google Scholar
• Schwarz, G. 1978. Estimating the dimension of a model. Ann. Statist., 6(2), 461–464. doi:10.1214/aos/1176344136.
   Web of Science ®Google Scholar
• Stadlmair, N.V., Hummel, T., and Sattelmayer, T. (2017), Thermoacoustic damping rate determination from combustion noise using bayesian statistics, ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition (50848), V04AT04A025.
   Google Scholar
• Thompson, J., and Stewart, H. 1986. Nonlinear Dynamics and Chaos, John Wiley, Chichester, UK
   Google Scholar
• Yi, T., and Gutmark, E.J. 2008. Online prediction of the onset of combustion instability based on the computation of damping ratios. J. Sound Vib., 310(1), 442–447. doi:10.1016/j.jsv.2007.07.072.
   Web of Science ®Google Scholar