Paper Info
Title
A Deep Supervised Learning Framework for Data-Driven Soft Sensor Modeling of Industrial Processes
Abstract
Deep learning has been recently introduced for soft sensors in industrial processes. However, most of the existing deep networks, such as stacked autoencoder, are pretrained in a layerwise unsupervised way to learn feature representations for the raw input data itself. For soft sensors, it is necessary to extract quality-relevant features for quality prediction. Thus, a deep layerwise supervised pretraining framework is proposed for quality-relevant feature extraction and soft sensor modeling in this article, which is based on stacked supervised encoder-decoder (SSED). In SSED, hierarchical quality-relevant features are successively learned by a number of supervised encoder-decoder (SED) models. For each SED, the features from the previous hidden layer are served as new inputs to generate the high-level features that are learned with the constraint of predicting the quality data as good as possible at the output layer of this SED. With this new structure, the SED can learn quality-relevant features that can largely improve the prediction performance. By stacking multiple SEDs, hierarchical quality-relevant features can be progressively learned, and irrelevant information is gradually reduced by deep SSED network. The effectiveness of the proposed model is demonstrated on a numerical example and an industrial process of the debutanizer column.
Year
DOI
Venue
2020
10.1109/TNNLS.2019.2957366
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Deep learning,feature representation,quality prediction,soft sensor,supervised learning
Journal
31
Issue
ISSN
Citations 
11
2162-237X
2
PageRank 
References 
Authors
0.37
0
5
Name
Order
Citations
PageRank
Xiaofeng Yuan15714.66
Yongjie Gu220.37
YaLin Wang36419.24
Chunhua Yang443571.63
Weihua Gui557790.82