Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder
published: Nov. 7, 2016, recorded: August 2016, views: 1294
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The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining, large amplitude pressure oscillations that shows varying spatial scales of periodic coherent vortex structure shedding. Early detection and close monitoring of combustion instability are the keys to extending the remaining useful life (RUL) of any gas turbine engine. However, such impending instability to a stable combustion is extremely difficult to detect only from pressure data due to its sudden (bifurcation-type) nature. This paper proposes an endto-end deep convolutional selective autoencoder approach to capture the rich information in hi-speed flame video for instability prognostics. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. The network identifies subtle instability features as a combustion process makes transition from stable to unstable region. The proposed framework is validated on a set of real data collected from a laboratory scale combustor over varied operating conditions. As a result, the deep learning tool-chain can perform as an early detection framework for combustion instabilities that will have a transformative impact on the safety and performance of modern engines.
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