Fault Detection Based on a Maximum-Likelihood Principal Component Analysis (PCA) Mixture-Reference-Cited by-同舟云学术

Fault Detection Based on a Maximum-Likelihood Principal Component Analysis (PCA) Mixture

Published:2005-02-25 Issue:7 Volume:44 Page:2316-2327
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Choi Sang Wook¹,Martin Elaine B.¹,Morris A. Julian¹,Lee In-Beum²

Affiliation:

1. Centre for Process Analytics and Control Technology, School of Chemical Engineering and Advanced Materials, University of Newcastle upon Tyne, NE1 7RU, United Kingdom

2. Department of Chemical Engineering, Pohang University of Science and Technology, San 31 Hyoja Dong, Pohang, 790-784, Korea

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ie049081o

Reference29 articles.

1. Multivariate statistical monitoring of process operating performance

2. Nonlinear principal component analysis using autoassociative neural networks

3. Nonlinear principal component analysis—Based on principal curves and neural networks

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