摘要：AbstractIn this work, a generic process visualization method is introduced using parametric t-SNE and used to visualize real-time process information and correlations among variables on a 2D map. A deep network is used to learn the Kullback-Leibler divergence between the original high-dimensional space and the latent space. In practice, it is observed that a model trained with historical data is not robust enough to visualize shifts into unknown states. Due to the effect of greedy learning, the response of the model is biased toward the most-contributing inputs. To relieve this effect, combinatorial variation creation is applied in the training stage to allow the model to respond to each input more evenly. The proposed method is tested on the Tennessee Eastman Process (TEP) data for four types of faults. The result indicates that the proposed method outperforms conventional methods such as PCA and Isomap, and is able to provide clear visual indication of process changes.
关键词：KeywordsData mining and multivariate statisticsData-Driven Decision Making
