2021 年 63 巻 203 号 p. 30-36
Machine learning technology is increasingly yielding major changes to scientific and engineering disciplines. The support vector machine and self-organizing map are the well-known classes of machine learning in the framework of statistical learning theory. Recent process in nonlinear time series analysis inspired by the theories of symbolic dynamics and complex networks, has opened up a new pathway for (i) an in-depth physical understanding and interpretation of nonlinear dynamics and (ii) the development of substitute detectors to capture a precursor of thermoacoustic combustion oscillations. This paper presents the applicability of the combined methodologies of nonlinear time series analysis, the support vector machine, and the self-organizing map, for an early detection of combustion oscillations in a swirl-stabilized combustor. We mainly consider two important analyses: complexity-entropy causality plane and the ordinal partition transition networks to capture a precursor of combustion oscillations.