Deep Double Supervised Embedding Neural Network Enhancing Class Separation for Visual High-Dimensional Industrial Process Monitoring

Abstract

Visual process monitoring is the application of a visualization method to map the real-time operating information of an industrial process to a 2-D map, followed by process monitoring. However, owing to the complexity of industrial production processes and the complex correlations among industrial process variables, the structure and distribution of high-dimensional industrial data are very complicated. Therefore, a general visualization method cannot effectively separate the different fault data in a 2-D map for process monitoring. Accordingly, in this article, a deep double supervised embedding neural network (DDSE) is proposed for visualizing high-dimensional industrial data. The DDSE consists of two supervised deep neural networks: a deep class centres uniform distribution neural network (DCCUD), and a deep supervised t-stochastic neighbor embedding neural network (DSSNE). The DCCUD maps the high-dimensional industrial data to a new feature space in which the class centres obey a uniform distribution, promoting a good and separable situation for subsequent visualization procedures. The DSSNE then maps these high-dimensional features into a 2-D space. The training of the DDSE can be conducted through pre-training and fine-tuning. A proposed visual process monitoring approach combines the DDSE with the local outlier factor and k-nearest neighbor approaches. The proposed approach is tested on a Tennessee Eastman process, and the results illustrate that the proposed approach outperforms traditional methods in terms of visualization and visual process monitoring.