Transitions from stable to unstable states occurring in dynamical systems can be sudden
leading to catastrophic failure and huge revenue loss. For detecting these transitions during
operation, it is of utmost importance to develop an accurate data-driven framework that is
robust enough to classify stable and unstable scenarios. In this paper, we propose deep
learning frameworks that show remarkable accuracy in the classification task of combustion
instability on carefully designed diverse training and test sets. We train our model with data …