Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE - Yuan, Xiaofeng; Ou, Chen; Wang, Yalin; Yang, Chunhua; Gui, Weihua (Neurocomputing (Amsterdam), 2020)
Soft sensors have been extensively used to predict difficult-to-measure quality variables for effective modeling, control and optimization of industrial processes. To construct accurate soft sensors, it is significant to carry out feature extraction for massive high-dimensional process data. Recently, deep learning has been introduced for feature representation in process data modeling. However, most of them cannot capture deep quality-related features for output prediction. In this paper, a hybrid variable-wise weighted stacked autoencoder (HVW-SAE) is developed to learn quality-related features for soft sensor modeling. By measuring the linear Pearson and nonlinear Spearman correlations for variables at the input layer with the quality variable at each encoder, a corresponding weighted reconstruction objective function is designed to successively pretrain the deep networks. With the constraint of preferential reconstruction for more quality-related variables, it can ensure that the learned features contain more information for quality prediction. Finally, the effectiveness of the proposed HVW-SAE based soft sensor method is validated on an industrial debutanizer column process.