Bayesian nonparametric modeling of Markov chains for detection of thermoacoustic instabilities

Sihan Xiong, Jihang Li, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper proposes a Bayesian nonparametric method for detecting thermoacoustic instabilities in gas turbine engines in real-time, where the underlying algorithms are formulated in the symbolic domain and the resulting patterns are constructed from symbolized pressure measurements as probabilistic finite state automata (PFSA) that is built upon a finite-memory Markov model, called D-Markov machine. The Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in the D-Markov machine, and (ii) online sequential testing, to provide a data-driven and coherent statistical analysis of combustion instability phenomena without relying on numerically intensive models of combustion dynamics. The proposed method has been experimentally validated on the time series generated from a laboratory-scale combustion apparatus. The results of instability prediction, derived from the time series, have been compared with those of other existing techniques.

    Original languageEnglish (US)
    Title of host publication2017 American Control Conference, ACC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3758-3763
    Number of pages6
    ISBN (Electronic)9781509059928
    DOIs
    StatePublished - Jun 29 2017
    Event2017 American Control Conference, ACC 2017 - Seattle, United States
    Duration: May 24 2017May 26 2017

    Other

    Other2017 American Control Conference, ACC 2017
    CountryUnited States
    CitySeattle
    Period5/24/175/26/17

    Fingerprint

    Thermoacoustics
    Markov processes
    Time series
    Finite automata
    Pressure measurement
    Gas turbines
    Statistical methods
    Turbines
    Data storage equipment
    Testing

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Xiong, S., Li, J., & Ray, A. (2017). Bayesian nonparametric modeling of Markov chains for detection of thermoacoustic instabilities. In 2017 American Control Conference, ACC 2017 (pp. 3758-3763). [7963530] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2017.7963530
    Xiong, Sihan ; Li, Jihang ; Ray, Asok. / Bayesian nonparametric modeling of Markov chains for detection of thermoacoustic instabilities. 2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3758-3763
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    abstract = "This paper proposes a Bayesian nonparametric method for detecting thermoacoustic instabilities in gas turbine engines in real-time, where the underlying algorithms are formulated in the symbolic domain and the resulting patterns are constructed from symbolized pressure measurements as probabilistic finite state automata (PFSA) that is built upon a finite-memory Markov model, called D-Markov machine. The Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in the D-Markov machine, and (ii) online sequential testing, to provide a data-driven and coherent statistical analysis of combustion instability phenomena without relying on numerically intensive models of combustion dynamics. The proposed method has been experimentally validated on the time series generated from a laboratory-scale combustion apparatus. The results of instability prediction, derived from the time series, have been compared with those of other existing techniques.",
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    Xiong, S, Li, J & Ray, A 2017, Bayesian nonparametric modeling of Markov chains for detection of thermoacoustic instabilities. in 2017 American Control Conference, ACC 2017., 7963530, Institute of Electrical and Electronics Engineers Inc., pp. 3758-3763, 2017 American Control Conference, ACC 2017, Seattle, United States, 5/24/17. https://doi.org/10.23919/ACC.2017.7963530

    Bayesian nonparametric modeling of Markov chains for detection of thermoacoustic instabilities. / Xiong, Sihan; Li, Jihang; Ray, Asok.

    2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3758-3763 7963530.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    N2 - This paper proposes a Bayesian nonparametric method for detecting thermoacoustic instabilities in gas turbine engines in real-time, where the underlying algorithms are formulated in the symbolic domain and the resulting patterns are constructed from symbolized pressure measurements as probabilistic finite state automata (PFSA) that is built upon a finite-memory Markov model, called D-Markov machine. The Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in the D-Markov machine, and (ii) online sequential testing, to provide a data-driven and coherent statistical analysis of combustion instability phenomena without relying on numerically intensive models of combustion dynamics. The proposed method has been experimentally validated on the time series generated from a laboratory-scale combustion apparatus. The results of instability prediction, derived from the time series, have been compared with those of other existing techniques.

    AB - This paper proposes a Bayesian nonparametric method for detecting thermoacoustic instabilities in gas turbine engines in real-time, where the underlying algorithms are formulated in the symbolic domain and the resulting patterns are constructed from symbolized pressure measurements as probabilistic finite state automata (PFSA) that is built upon a finite-memory Markov model, called D-Markov machine. The Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in the D-Markov machine, and (ii) online sequential testing, to provide a data-driven and coherent statistical analysis of combustion instability phenomena without relying on numerically intensive models of combustion dynamics. The proposed method has been experimentally validated on the time series generated from a laboratory-scale combustion apparatus. The results of instability prediction, derived from the time series, have been compared with those of other existing techniques.

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    Xiong S, Li J, Ray A. Bayesian nonparametric modeling of Markov chains for detection of thermoacoustic instabilities. In 2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3758-3763. 7963530 https://doi.org/10.23919/ACC.2017.7963530