Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE

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. Recentl...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 396; S. 375 - 382
Hauptverfasser: Yuan, Xiaofeng, Ou, Chen, Wang, Yalin, Yang, Chunhua, Gui, Weihua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 05.07.2020
Schlagworte:
Deep learning
Feature representation
Hybrid variable-wise weighted SAE (HVW-SAE)
Soft sensor
Stacked autoencoder (SAE)
Deep learning
Stacked autoencoder (SAE)
Soft sensor
Hybrid variable-wise weighted SAE (HVW-SAE)
Feature representation
ISSN:0925-2312, 1872-8286
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.11.107