Dual Attention-Based Encoder-Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development

Soft sensor techniques have been applied to predict the hard-to-measure quality variables based on the easy-to-measure process variables in industry scenarios. Since the products are usually produced with prearranged processing orders, the sequential dependence among different variables can be impor...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 32; no. 8; pp. 3306 - 3317
Main Authors: Feng, Liangjun, Zhao, Chunhui, Sun, Youxian
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Attention mechanism
Coders
Customization
Decoding
encoder–decoder
Feature extraction
Humidity
Industries
Learning
Long short-term memory
long short-term memory (LSTM) network
Multiphase flow
Prediction algorithms
Predictive models
process industry
Process variables
Sensors
sequence to sequence learning
soft sensor modeling
Task analysis
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Soft sensor techniques have been applied to predict the hard-to-measure quality variables based on the easy-to-measure process variables in industry scenarios. Since the products are usually produced with prearranged processing orders, the sequential dependence among different variables can be important for the process modeling. To use this property, a dual attention-based encoder-decoder is developed in this article, which presents a customized sequence-to-sequence learning for soft sensor. We reveal that different quality variables in the same process are sequentially dependent on each other and the process variables are natural time sequences. Hence, the encoder-decoder is constructed to explicitly exploit the sequential information of both the input, that is, the process variables, and the output, that is, the quality variables. The encoder and decoder modules are specified as the long short-term memory network. In addition, since different process variables and time points impose different effects on the quality variables, a dual attention mechanism is embedded into the encoder-decoder to concurrently search the quality-related process variables and time points for a fine-grained quality prediction. Comprehensive experiments are performed based on a real cigarette production process and a benchmark multiphase flow process, which illustrate the effectiveness of the proposed encoder-decoder and its sequence to sequence learning for soft sensor.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3015929