  While analytical solutions of critical (phase) transitions in physical
systems are abundant for simple nonlinear systems, such analysis remains
intractable for real-life dynamical systems. A key example of such a physical
system is thermoacoustic instability in combustion, where prediction or early
detection of an onset of instability is a hard technical challenge, which needs
to be addressed to build safer and more energy-efficient gas turbine engines
powering aerospace and energy industries. The instabilities arising in
combustion chambers of engines are mathematically too complex to model. To
address this issue in a data-driven manner instead, we propose a novel deep
learning architecture called 3D convolutional selective autoencoder (3D-CSAE)
to detect the evolution of self-excited oscillations using spatiotemporal data,
i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory
surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to
learn, in a hierarchical fashion, the complex visual and dynamic features
related to combustion instability. We train the 3D-CSAE on frames of videos
obtained from a limited set of operating conditions. We select the 3D-CSAE
hyper-parameters that are effective for characterizing hierarchical and
multiscale instability structure evolution by utilizing the dynamic information
available in the video. The proposed model clearly shows performance
improvement in detecting the precursors of instability. The machine
learning-driven results are verified with physics-based off-line measures.
Advanced active control mechanisms can directly leverage the proposed online
detection capability of 3D-CSAE to mitigate the adverse effects of combustion
instabilities on the engine operating under various stringent requirements and
conditions.
