This paper proposes an end-to-end convolutional selective autoencoder approach for early detection of combustion instabilities using rapidly arriving flame image frames. The 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. These properties are described as self-sustaining, large amplitude pressure oscillations and show varying spatial scales periodic coherent vortex structure shedding. However, such instability is extremely difficult to detect before a combustion process becomes completely unstable due to its sudden (bifurcation-type) nature. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. In that process, the model learns to identify and extract rich descriptive and explanatory flame shape features. With such a training scheme, the selective autoencoder is shown to be able to detect subtle instability features as a combustion process makes transition from stable to unstable region. As a consequence, 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.

本文提出了一种利用卷积选择自编码器进行燃烧不稳定性早期检测的方法。模型训练过程中，自编码器通过对稳定火焰的选择性屏蔽，提取富含描述性和说明性的火焰特征，从而实现对燃烧过程稳定到不稳定转变时细微的不稳定特征进行检测，这对现代引擎的安全性和性能具有重要的影响。

使用深度卷积自编码器对高速火焰视频进行选择性自编码，早期检测燃烧不稳定性