Abstract
This paper presents an approach for data-driven modeling of hidden, stationary temporal dynamics in sequential images or videos using deep learning and Bayesian non-parametric techniques. In particular, a deep Convolutional Neural Network (CNN) is used to extract spatial features in an unsupervised fashion from individual images and then, a Gaussian process is used to model the temporal dynamics of the spatial features extracted by the deep CNN. By decomposing the spatial and temporal components and utilizing the strengths of deep learning and Gaussian processes for the respective sub-problems, we are able to construct a model that is able to capture complex spatio-temporal phenomena while using relatively small number of free parameters. The proposed approach is tested on high-speed grey-scale video data obtained of combustion flames in a swirl-stabilized combustor, where certain protocols are used to induce instability in combustion process. The proposed approach is then used to detect and predict the transition of the combustion process from stable to unstable regime. It is demonstrated that the proposed approach is able to detect unstable flame conditions using very few frames from high-speed video. This is useful as early detection of unstable combustion can lead to better control strategies to mitigate instability. Results from the proposed approach are compared and contrasted with several baselines and recent work in this area. The performance of the proposed approach is found to be significantly better in terms of detection accuracy, model complexity and lead-time to detection.
Highlights
Combustion instability still remains a puzzle for researchers and the current state-of-the-art techniques heavily rely on physics-based models
The current analysis presented a datadriven spatio-temporal analysis of combustion flames using deep neural networks and Gaussian processes using highspeed images of flames during lean pre-mixed combustion
We presented a framework for learning hidden, stationary dynamics in video data using deep convolutional networks and Gaussian processes
Summary
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstractions (LeCun, Bengio, & Hinton, 2015; Bengio, Courville, & Vincent, 2013; Hinton & Salakhutdinov, 2006). The proposed algorithm is used to detect stable and unstable combustion flame dynamics in a swirl-stabilized combustor Another motivation is that adding a Gaussian process-based filter might allow to reduce over-fitting of the deep CNN to some extent as it allows to relate to the causal dynamics present in the data in a much lower dimensional space. The goal of this paper is to present a statistical model for the instability phenomenon during combustion which could be used to design a statistical filter to accurately predict the system states This can potentially alleviate the problems with delay in the ACIC feedback loop and possibly improve the performance. We show that the deep CNN is able to achieve fairly good classification performance; the use of a Gaussian process filter to model temporality of extracted features results in perfect detection with very low false alarm rates and much shorter lead time to detection. The advantage of the proposed method is that it can be used for making early predictions of the transition from a stable regime to quasi-periodic unstable oscillations in combustion system
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