Image-Based Process Monitoring Using Deep Belief Networks

With the advances in optical sensing and image capture systems, process images certainly offer new perspectives to process monitoring. Compared to the process data collected by traditional sensors at local regions, process images, which can capture more significant variations in the whole space, enhance the monitoring performance in data-driven monitoring methods. In this paper, a popular deep learning method, namely deep belief network (DBN), is applied to effectively extract useful features from the images. Meanwhile, a new statistic is developed for the DBN model, which integrates feature extraction and fault detection into one model rather than separately accomplish them. The effectiveness of the proposed DBN based monitoring method is demonstrated in a real combustion system. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.