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Dynamic probabilistic Latent Variable Model for Process Data modeling and regression Application

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2019年第1期27卷 323-331页

作者： Ge, Zhiqiang Chen, Xinru Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

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.

关键词： Dynamical data modeling expectation-maximization (EM) probabilistic regression modeling soft sensor

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Supervised Latent Factor Analysis for Process Data regression modeling and Soft Sensor Application

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2016年第3期24卷 1004-1011页

作者： Ge, Zhiqiang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

This brief proposed a new supervised latent factor analysis (FA) method for process data regression modeling. Different from the traditional principal component analysis/regression model, the new model can successfully estimate heterogeneous variances from different process variables, which is more practical. Under the same probabilistic modeling framework, the single supervised latent FA model is further extended to the mixture form. Efficient expectation-maximization algorithms are developed for parameter learning in both single and mixture supervised latent FA models. Based on the regression modeling between easy-to-measure and difficult-to-measure process variables, two soft sensors are built for quality prediction in the process. Two case studies are provided to evaluate the modeling and performances of the new methods.

关键词： Expectation-maximization algorithm probabilistic regression modeling process data analysis soft sensor supervised latent factor analysis (FA)

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