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Mixture Bayesian regularization method of PPCA for multimode process monitoring
Author(s) -
Ge Zhiqiang,
Song Zhihuan
Publication year - 2010
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.12200
Subject(s) - regularization (linguistics) , bayesian probability , probabilistic logic , curse of dimensionality , computer science , principal component analysis , latent variable , artificial intelligence , process (computing) , data mining , machine learning , operating system
Abstract This article intends to address two drawbacks of the traditional principal component analysis (PCA)‐based monitoring method: (1) nonprobabilistic; (2) single operation mode assumption. On the basis of the monitoring framework of probabilistic PCA (PPCA), a Bayesian regularization method is introduced for performance improvement, through which the effective dimensionality of the latent variable can be determined automatically. For monitoring processes with multiple operation modes, the Bayesian regularization method is extended to its mixture form, thus a mixture Bayesian regularization method of PPCA has been developed. To enhance the monitoring performance, a novel probabilistic strategy has been proposed for result combination in different operation modes. In addition, a new mode localization approach has also been developed, which can provide additional information and improve process comprehension for the operation engineer. A numerical example and a real industrial application case study have been used to evaluate the efficiency of the proposed method. © 2010 American Institute of Chemical Engineers AIChE J, 2010