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Technical Brief

Detection of Thermoacoustic Instabilities via Nonparametric Bayesian Markov Modeling of Time Series Data

[+] Author and Article Information
Sihan Xiong

Graduate Research Assistant, Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802-1412
sux101@psu.edu

Sudeepta Mondal

Graduate Research Assistant, Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802-1412
sbm5423@psu.edu

Dr. Asok Ray

Distinguished Professor, Mechanical Engineering Department, Pennsylvania State University, University Park, PA 16802-1412
axr2@psu.edu

1Corresponding author.

ASME doi:10.1115/1.4037288 History: Received March 15, 2017; Revised June 29, 2017

Abstract

Real-time detection and decision & control of thermoacoustic instabilities in confined combustors is a challenging task 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 & 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) finite-memory 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.

Copyright (c) 2017 by ASME
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