Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application

Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose. Ba...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2019-01, Vol.27 (1), p.323-331
Main Authors: Ge, Zhiqiang, Chen, Xinru
Format: Article
Language:English
Subjects:
Analytical models
Analytics
Data models
Data processing
Dynamical data modeling
expectation–maximization (EM)
Load modeling
Machine learning
Modelling
Noise measurement
Principal component analysis
Probabilistic logic
probabilistic regression modeling
Regression analysis
Sensors
soft sensor
Statistical analysis
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Summary:Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose. Based on the probabilistic latent variable modeling framework, the linear dynamic system is introduced for incorporation of the dynamical data feature. The expectation-maximization Algorithm is introduced for parameter learning of the dynamical probabilistic latent variable model, based on which a new soft sensing scheme is then formulated for online prediction of key/quality variables in the process. An industrial case study illustrates the necessity and effectiveness of introducing the dynamical data information into the probabilistic latent variable model.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2017.2767022