References
- Ali, S. S., and M. U. Ghani. 2014. Handwritten digit recognition using dct and hmms. 2014 12th International Conference on Frontiers of Information Technology, Islamabad, Pakistan, 303–06.
- Bhattacharya, C., S. De, A. Mukhopadhyay, S. Sen, and A. Ray. 2020a. Detection and classification of lean blow-out and thermoacoustic instability in turbulent combustors. Appl. Therm. Eng. 180. 115808.
- Bhattacharya, C., S. Mondal, A. Ray, and A. Mukhopadhyay. 2020b. Reduced-order modelling of thermoacoustic instabilities in a two-heater Rijke tube. Combust. Theor. Model. 24 (3):530–48. doi:https://doi.org/10.1080/13647830.2020.1714080.
- Bhattacharya, C., and A. Ray. 2020a. Data-driven detection and classification of regimes in chaotic systems via hidden markov modeling. ASME Lett. Dyn. Syst. Control. 1:1–6.
- Bhattacharya, C., and A. Ray. 2020b. Online discovery and classification of operational regimes from an ensemble of time series data. J. Dyn. Syst. Meas. Control 142 (11). doi:https://doi.org/10.1115/1.4047449.
- Bishop, C. 2007. Pattern recognition and machine learning. New York, USA: Springer.
- Candel, S. 2002. Combustion dynamics and control: Progress and challenges. Proc. Combust. Inst. 29 (1):1–28. doi:https://doi.org/10.1016/S1540-7489(02)80007-4.
- Chattopadhyay, P., S. Mondal, C. Bhattacharya, A. Mukhopadhyay, and A. Ray. 2017. Dynamic data-driven design of lean premixed combustors for thermoacoustically stable operations. J. Mech. Des. 139 (11):111419–1–111419–10. doi:https://doi.org/10.1115/1.4037307.
- Culler, W., X. Chen, S. Peluso, D. Santavicca, J. O’Connor, and D. Noble. 2018a. Comparison of center nozzle staging to outer nozzle staging in a multi-flame combustor. In Turbo expo: Power for land, sea, and air, Vol. 51050, V04AT04A024. American Society of Mechanical Engineers.
- Culler, W., X. Chen, J. Samarasinghe, S. Peluso, D. Santavicca, and J. O’Connor. 2018b. The effect of variable fuel staging transients on self-excited instabilities in a multiple-nozzle combustor. Combust. Flame 194:472–84. doi:https://doi.org/10.1016/j.combustflame.2018.04.025.
- Ghalyan, N. F., S. Mondal, D. J. Miller, and A. Ray. 2019. Hidden Markov modeling-based decision-making using short-length sensor time series. J. Dyn. Syst. Meas. Control 141 (10). doi:https://doi.org/10.1115/1.4043428.
- Hajek, B. 2015. Random processes for engineers. 1st ed. Cambridge, UK: Cambridge University Press.
- Hauser, M., Y. Li, J. Li, and A. Ray. 2016. Real-time combustion state identification via image processing: A dynamic data-driven approach. 2016 American Control Conference (ACC), Boston, MA, 3316–21.
- Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Neural Comput. 9 (8):1735–80. doi:https://doi.org/10.1162/neco.1997.9.8.1735.
- Howie, A., D. Doleiden, S. Peluso, and J. O’Connor. 2020. The effect of the degree of premixedness on self-excited combustion instability. ASME Turbo Expo, London, England.
- Husken, M., and P. Stagge. 2003. Recurrent neural networks for time series classification. Neurocomputing 50:223–35. doi:https://doi.org/10.1016/S0925-2312(01)00706-8.
- Kabiraj, L., and R. I. Sujith. 2012. Nonlinear self-excited thermoacoustic oscillations: Intermittency and flame blowout. J. Fluid Mech. 713:376–97. doi:https://doi.org/10.1017/jfm.2012.463.
- Lacasa, L., B. Luque, F. Ballesteros, J. Luque, and J. C. Nuño. 2008. From time series to complex networks: The visibility graph. Proc. Natl. Acad. Sci. 105 (13):4972–75. doi:https://doi.org/10.1073/pnas.0709247105.
- Lee, J. G., and D. A. Santavicca. 2003. Experimental diagnostics for the study of combustion instabilities in lean premixed combustors. J. Propul. Power 19 (5):735–50. doi:https://doi.org/10.2514/2.6191.
- Lieuwen, T. 2005. Online combustor stability margin assessment using dynamic pressure data. J. Eng. Gas Turbines Power 127 (3):478–82. doi:https://doi.org/10.1115/1.1850493.
- Lieuwen, T., and V. Yang. 2005. Combustion instabilities in gas turbine engines: Operational experience, fundamental mechanisms, and modeling. Reston, VA: AIAA.
- Matveev, K. I. 2003. Thermoacoustic instabilities in the Rijke tube: Experiments and modeling. PhD thesis, California Institute of Technology, Pasadena, CA.
- Mondal, S., C. Bhattacharya, P. Chattopadhyay, A. Mukhopadhyay, and A. Ray. 2017. Prediction of thermoacoustic instabilities in a premixed combustor based on fft-based dynamic characterization. 53rd AIAA/SAE/ASEE Joint Propulsion Conference, Atlanta, GA.
- Mondal, S., N. F. Ghalyan, A. Ray, and A. Mukhopadhyay. 2019. Early detection of thermoacoustic instabilities using hidden markov models. Combust. Sci. Technol. 191 (8):1309–36. doi:https://doi.org/10.1080/00102202.2018.1523900.
- Mukherjee, K., and A. Ray. 2014. State splitting and merging in probabilistic finite state automata for signal representation and analysis. Signal Process. 104:105–19. doi:https://doi.org/10.1016/j.sigpro.2014.03.045.
- Murphy, K. 2012. Machine learning: A probabilistic perspective. 1st ed. Cambridge, MA: The MIT Press.
- Murugesan, M., and R. I. Sujith. 2015. Combustion noise is scale-free: Transition from scale-free to order at the onset of thermoacoustic instability. J. Fluid Mech. 772:225–45. doi:https://doi.org/10.1017/jfm.2015.215.
- Nair, V., G. Thampi, S. Karuppusamy, S. Gopalan, and R. I. Sujith. 2013. Loss of chaos in combustion noise as a precursor of impending combustion instability. Int. J. Spray Combust. Dyn. 5 (4):273–90. doi:https://doi.org/10.1260/1756-8277.5.4.273.
- Najkar, N., F. Razzazi, and H. Sameti. 2010. A novel approach to hmm-based speech recognition systems using particle swarm optimization. Math. Comput. Model. 52 (11):1910–20. The BIC-TA 2009 Special Issue.
- Noiray, N., and B. Schuermans. 2012. Theoretical and experimental investigations on damper performance for suppresion of thermoacoustic oscillations. J. Sound Vib. 331 (12):2753–863. doi:https://doi.org/10.1016/j.jsv.2012.02.005.
- O’Connor, J., V. Acharya, and T. Lieuwen. 2015. Transverse combustion instabilities: Acoustic, fluid mechanic, and flame processes. Prog. Energy Combust. Sci. 49:1–39. doi:https://doi.org/10.1016/j.pecs.2015.01.001.
- Oates, T., L. Firoiu, and P. Cohen. 2000. Using dynamic time warping to bootstrap HMM-based clustering of time series. In Sequence learning, 35–52. New York, USA: Springer.
- Rabiner, L., and B.-H. Juang. 1993. Fundamentals of speech recognition. Upper Saddle River, NJ: Prentice-Hall, Inc.
- Rabiner, L. R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77 (2):257–86. doi:https://doi.org/10.1109/5.18626.
- Rajagopalan, V., and A. Ray. 2006. Symbolic time series analysis via wavelet-based partitioning. Signal Process. 86 (11):3309–20. doi:https://doi.org/10.1016/j.sigpro.2006.01.014.
- Ray, A. 2004. Symbolic dynamic analysis of complex systems for anomaly detection. Signal Process. 84 (7):1115–30. doi:https://doi.org/10.1016/j.sigpro.2004.03.011.
- Rayleigh, L. 1845. The theory of sound. Mineola, NY: Dover Publications.
- Richecoeur, F., S. Ducruix, P. Scouflaire, and S. Candel. 2008. Experimental investigation of high-frequency combustion instabilities in liquid rocket engine. Acta Astronaut. 62 (1):18–27. doi:https://doi.org/10.1016/j.actaastro.2006.12.034.
- Rijke, P. L. 1859. Notiz über eine neue art, die in einer an beiden enden offenen röhre enthaltene luft in schwingungen zu versetzen. Annalen der Physik und Chemie 183 (6):339–43. doi:https://doi.org/10.1002/andp.18591830616.
- Samarasinghe, J., W. Culler, B. D. Quay, D. A. Santavicca, and J. O’connor. 2017. The effect of fuel staging on the structure and instability characteristics of swirl-stabilized flames in a lean premixed multinozzle can combustor. J. Eng. Gas Turbines Power 139 (12):121504.
- Sarkar, S., S. R. Chakravarthy, V. Ramanan, and A. Ray. 2016. Dynamic data-driven prediction of instability in a swirl-stabilized combustor. Int. J. Spray Combust. Dyn. 8 (4):235–53. doi:https://doi.org/10.1177/1756827716642091.
- Sarkar, S., K. G. Lore, S. Sarkar, V. Ramanan, S. R. Chakravarthy, S. Phoha, and A. Ray. 2015. Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis. Annual Conference of the Prognostics and Health Management Society, Coronado, CA.
- Sen, U., T. Gangopadhyay, C. Bhattacharya, A., . S. Misra, P. S. Karmakar, A. Mukhopadhyay, and S. Sen. 2016. Investigation of ducted inverse nonpremixed flame using dynamic systems approach. ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition, Seoul, Soth Korea, Vol. 4B.
- Sen, U., T. Gangopadhyay, C. Bhattacharya, A. Mukhopadhyay, and S. Sen. 2018. Dynamic characterization of a ducted inverse diffusion flame using recurrence analysis. Combust. Sci. Technol. 190 (1):32–56. doi:https://doi.org/10.1080/00102202.2017.1374952.
- Subbu, A., and A. Ray. 2008. Space partitioning via hilbert transform for symbolic time series analysis. Appl. Phys. Lett. 92 (8):084107. doi:https://doi.org/10.1063/1.2883958.
- Unni, V. R., A. Mukhopadhyay, and R. I. Sujith. 2015. Online detection of impending instability in a combustion system using tools from symbolic time series analysis. Int. J. Spray Combust. Dyn. 7 (3):243–55. doi:https://doi.org/10.1260/1756-8277.7.3.243.
- Wang, Z., W. Yan, and T. Oates. 2017. Time series classification from scratch with deep neural networks: A strong baseline, 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 1578–85.
- Zhao, D. 2012. Transient growth of flow disturbances in triggering a Rijke tube combustion instability. Combust. Flame 159 (6):2126–37. doi:https://doi.org/10.1016/j.combustflame.2012.02.002.
- Zhao, D., and Z. H. Chow. 2013. Thermoacoustic instability of a laminar premixed flame in Rijke tube with a hydrodynamic region. J. Sound Vib. 332 (14):3419–37. doi:https://doi.org/10.1016/j.jsv.2013.01.031.