Detection of Thermoacoustic Instabilities Via Nonparametric Bayesian Markov Modeling of Time-Series Data

Sihan Xiong, Sudeepta Mondal, Asok Ray

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    Real-time detection and decision and control of thermoacoustic instabilities in confined combustors are challenging tasks due to the fast dynamics of the underlying physical process. The objective here is to develop a dynamic data-driven algorithm for detecting the onset of instabilities with short-length time-series data, acquired by available sensors (e.g., pressure and chemiluminescence), which will provide sufficient lead time for active decision and control. To this end, this paper proposes a Bayesian nonparametric method of Markov modeling for real-time detection of thermoacoustic instabilities in gas turbine engines; 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). These PFSA models are built upon the framework of a (low-order) finitememory Markov model, called the D-Markov machine, where a Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in D-Markov machines and (ii) online sequential testing to provide dynamic data-driven and coherent statistical analyses of combustion instability phenomena without solely relying on computationally intensive (physics-based) models of combustion dynamics. The proposed method has been validated on an ensemble of pressure time series from a laboratory-scale combustion apparatus. The results of instability prediction have been compared with those of other existing techniques.

    Original languageEnglish (US)
    Article number024501
    JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
    Volume140
    Issue number2
    DOIs
    StatePublished - Feb 1 2018

    Fingerprint

    Thermoacoustics
    Time series
    Finite automata
    combustion stability
    gas turbine engines
    Chemiluminescence
    chemiluminescence
    Pressure sensors
    pressure sensors
    pressure measurement
    combustion chambers
    Pressure measurement
    Combustors
    Gas turbines
    Turbines
    Physics
    physics
    Testing
    predictions

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Information Systems
    • Instrumentation
    • Mechanical Engineering
    • Computer Science Applications

    Cite this

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    title = "Detection of Thermoacoustic Instabilities Via Nonparametric Bayesian Markov Modeling of Time-Series Data",
    abstract = "Real-time detection and decision and control of thermoacoustic instabilities in confined combustors are challenging tasks due to the fast dynamics of the underlying physical process. The objective here is to develop a dynamic data-driven algorithm for detecting the onset of instabilities with short-length time-series data, acquired by available sensors (e.g., pressure and chemiluminescence), which will provide sufficient lead time for active decision and control. To this end, this paper proposes a Bayesian nonparametric method of Markov modeling for real-time detection of thermoacoustic instabilities in gas turbine engines; 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). These PFSA models are built upon the framework of a (low-order) finitememory Markov model, called the D-Markov machine, where a Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in D-Markov machines and (ii) online sequential testing to provide dynamic data-driven and coherent statistical analyses of combustion instability phenomena without solely relying on computationally intensive (physics-based) models of combustion dynamics. The proposed method has been validated on an ensemble of pressure time series from a laboratory-scale combustion apparatus. The results of instability prediction have been compared with those of other existing techniques.",
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    Detection of Thermoacoustic Instabilities Via Nonparametric Bayesian Markov Modeling of Time-Series Data. / Xiong, Sihan; Mondal, Sudeepta; Ray, Asok.

    In: Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 140, No. 2, 024501, 01.02.2018.

    Research output: Contribution to journalArticle

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