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 Scopus citations

    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

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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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 (pp. 3758-3763). [7963530] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2017.7963530