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Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network
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DOI：10.1109/TII.2019.2902129.png)
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Abstract
En 中文
Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly. Recently, a long short-term memory (LSTM) network has shown great modeling ability on various time series, in which basic LSTM units can handle data nonlinearities and dynamics with a dynamic latent variable structure. However, the hidden variables in the basic LSTM unit mainly focus on describing the dynamics of input variables, which lack representation for the quality data. In this paper, a supervised LSTM (SLSTM) network is proposed to learn quality-relevant hidden dynamics for soft sensor application, which is composed of basic SLSTM unit at each sampling instant. In the basic SLSTM unit, the quality and input variables are simultaneously utilized to learn the dynamic hidden states, which are more relevant and useful for quality prediction. The effectiveness of the proposed SLSTM network is demonstrated on a penicillin fermentation process and an industrial debutanizer column.
Keywords:
Logic gates
Informatics
Principal component analysis
Process control
Artificial neural networks
Recurrent neural networks
Input variables
Deep learning
long short-term memory (LSTM)
quality prediction
soft sensor
supervised LSTM (SLSTM)
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