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While analytical solutions of critical (phase) transitions in physicalsystems are abundant for simple nonlinear systems, such analysis remainsintractable for real-life dynamical systems. A key example of such a physicalsystem is thermoacoustic instability in combustion, where prediction or earlydetection of an onset of instability is a hard technical challenge, which needsto be addressed to build safer and more energy-efficient gas turbine enginespowering aerospace and energy industries. The instabilities arising incombustion chambers of engines are mathematically too complex to model. Toaddress this issue in a data-driven manner instead, we propose a novel deeplearning 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 (laboratorysurrogate of gas turbine engine combustor). 3D-CSAE consists of filters tolearn, in a hierarchical fashion, the complex visual and dynamic featuresrelated to combustion instability. We train the 3D-CSAE on frames of videosobtained from a limited set of operating conditions. We select the 3D-CSAEhyper-parameters that are effective for characterizing hierarchical andmultiscale instability structure evolution by utilizing the dynamic informationavailable in the video. The proposed model clearly shows performanceimprovement in detecting the precursors of instability. The machinelearning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed onlinedetection capability of 3D-CSAE to mitigate the adverse effects of combustioninstabilities on the engine operating under various stringent requirements andconditions.

3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems