Online detection of impending instability in a combustion system using tools from symbolic time series analysis

Abstract

In this paper, we introduce a novel technique (anomaly detection) for the online detection of impending instability in a combustion system based on symbolic time series analysis. The experimental results presented in this paper illustrate the application of anomaly detection to a combustor in which the flame is stabilized either by a bluff body or by a swirler. The detection unit works on the principle that in the transition region from combustion noise to thermoacoustic instability, combustion systems exhibit peculiar dynamics which results in the formation of specific patterns in the time series. Further, tools from symbolic time series analysis is used to recognize these patterns and then define an anomaly measure indicative of the proximity of system to regimes of thermoacoustic instability.