Mixture Bayesian regularization method of PPCA for multimode process monitoring

Zhiqiang Ge

doi:10.1002/aic.12200

journal article Feb 05, 2010

Mixture Bayesian regularization method of PPCA for multimode process monitoring

Zhiqiang Ge

AIChE Journal Vol. 56 No. 11 pp. 2838-2849 · Wiley

View at Publisher Save 10.1002/aic.12200

Abstract

AbstractThis 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

Topics

No keywords indexed for this article. Browse by subject →

References

16

[1]

10.1002/cem.800

[2]

10.1016/s0967-0661(02)00096-5

[3]

10.1002/aic.11576

[4]

10.1002/aic.11405

[5]

10.1002/aic.11617

[6]

10.1016/j.conengprac.2007.04.014

[7]

Probabilistic Principal Component Analysis

Michael E. Tipping, Christopher M. Bishop

Journal of the Royal Statistical Society Series B:... 10.1111/1467-9868.00196

[8]

10.1016/s0169-7439(03)00063-7

[9]

10.1162/089976699300016728

[10]

10.1021/ie980577d

[11]

10.1021/ie9905613

[12]

10.1016/j.compchemeng.2003.09.031

[13]

10.1021/ie049081o

[14]

Bishop CM Advanced in Neural Information Proceeding Systems (1999)

[15]

10.1002/cem.759

[16]

Bishop CM (2006)

Cited By

142

Image-based process monitoring using deep learning framework

Yuting Lyu, Junghui Chen · 2019

Chemometrics and Intelligent Labora...

A review of the Expectation Maximization algorithm in data-driven process identification

Nima Sammaknejad, Yujia Zhao · 2019

Journal of Process Control

Multimode process monitoring using improved dynamic neighborhood preserving embedding

Bing Song, Hongbo Shi · 2014

Chemometrics and Intelligent Labora...

Mixture semisupervised principal component regression model and soft sensor application

Zhiqiang Ge, Biao Huang · 2013

AIChE Journal

Metrics

142

Citations

16

References

Details

Published: Feb 05, 2010
Vol/Issue: 56(11)
Pages: 2838-2849
License: View

Authors

Z

Zhiqiang Ge

Cite This Article

Zhiqiang Ge (2010). Mixture Bayesian regularization method of PPCA for multimode process monitoring. AIChE Journal, 56(11), 2838-2849. https://doi.org/10.1002/aic.12200

Mixture Bayesian regularization method of PPCA for multimode process monitoring

You May Also Like