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Hierarchical multivariate analysis of cockle phenomena
Author(s) -
Stefanov Zdravko I.,
Hoo Karlene A.
Publication year - 2003
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.825
Subject(s) - cockle , multivariate statistics , operability , computer science , curse of dimensionality , decomposition , process (computing) , hierarchy , data mining , artificial intelligence , machine learning , ecology , software engineering , economics , market economy , biology , operating system
The phenomena called cockle are small wrinkles on the paper surface that appear during paper production. This condition poses significant economic and operability problems in the production of magazine paper, as it deteriorates the printabilty of the paper. There are many and varied sources that can lead to cockle, and their detection is often very complicated. In this work a multivariate hierarchical approach is proposed to analyze the cause of cockle. The hierarchy has two levels, the first of which is a three‐way decomposition and analysis of the data collected from sections of a paper machine. The second level is a two‐way decomposition and analysis between the combined loadings from the three‐way decomposition and the measured cockle data. The results show that this approach is capable of identifying the important process sections and process variables, in spite of the large dimensionality of the problem. Data analyzed from two real industrial paper machines, involving several grades of paper, are used to demonstrate the proposed hierarchical approach. Copyright © 2003 John Wiley & Sons, Ltd.