Back to Search Start Over

Semi-supervised LSTM with historical feature fusion attention for temporal sequence dynamic modeling in industrial processes.

Authors :
Tang, Yiyin
Wang, Yalin
Liu, Chenliang
Yuan, Xiaofeng
Wang, Kai
Yang, Chunhua
Source :
Engineering Applications of Artificial Intelligence. Jan2023:Part A, Vol. 117, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

In modern industrial processes, the data-driven soft sensor technology has been widely used for the prediction of key quality variables. Due to the important of dynamics and nonlinearity in industrial process data, deep learning models like long short-term memory (LSTM) network are well suited for temporal sequence dynamic modeling due to their excellent long-term memory function and feature extraction capability. Furthermore, industrial processes generate a large amount of process data with irregular sampling frequencies. However, traditional LSTM cannot fully utilize the process data with irregular sampling frequency and the guidance value of historical data samples for feature learning. To address these issues, a novel semi-supervised LSTM with history feature fusion attention (HFFA-SSLSTM) model is proposed in this paper. First, the semi-supervised learning strategy is implemented in LSTM to fully utilize the unlabeled data and mine the temporal sequence features of labeled samples and unlabeled samples with irregular sampling frequencies. Then, a novel historical feature fusion attention (HFFA) mechanism is developed, which utilizes historical hidden features to learn attention scores for obtaining weighted historical information-related features. Finally, the extracted features are combined to form the soft sensor model to perform time series prediction tasks for key quality variables in industrial processes. The experimental results on the actual industrial hydrocracking data set demonstrate the effectiveness of the proposed HFFA-SSLSTM model and its possibility of applicating in real industrial processes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09521976
Volume :
117
Database :
Academic Search Index
Journal :
Engineering Applications of Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
160692532
Full Text :
https://doi.org/10.1016/j.engappai.2022.105547