Paper Info
Title
Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling
Abstract
Due to the limitations of sampling conditions and sampling techniques in many real industrial processes, the process data under different sampling conditions subject to different sampling frequencies, which leads to irregular interval sampling characteristics of the entire process data. The dynamic historical data information reflecting the production status under irregular sampling frequency has an important influence on the performance of data feature extraction. However, the existing soft sensor modeling methods based on deep learning do not consider introducing dynamic historical information into the feature extraction process. To combat this issue, a novel attention-based dynamic stacked autoencoder networks (AD-SAE) for soft sensor modeling is proposed in this paper. First, the sliding window technology and attention mechanism based on position coding are introduced to select dynamic historical samples and calculate the contribution of different historical samples to the current sample, respectively. Then, AD-SAE combines obtained historical sample information and current sample information as the input of the network for deep feature extraction and industrial soft sensor modeling. The experimental results on the actual hydrocracking process data set show that the proposed method has better performance than traditional methods.
Year
DOI
Venue
2022
10.1016/j.aei.2022.101590
Advanced Engineering Informatics
Keywords
DocType
Volume
Soft sensor,Deep learning,Stacked autoencoder,Attention mechanism,AD-SAE
Journal
52
ISSN
Citations 
PageRank 
1474-0346
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
YaLin Wang16419.24
Diju Liu200.34
Chenliang Liu322.07
Xiaofeng Yuan45714.66
Kai Wang586.58
Chunhua Yang643571.63