Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/70506

TitleMultivariate statistical process control based on principal component analysis: implementation of framework in R
Author(s)Braga, A. C.
Barros, Cláudia
Delgado, Pedro
Martins, Cristina
Sousa, Sandra
Velosa, J. C.
Delgado, Isabel
Sampaio, Paulo
KeywordsContribution plots
Control charts
Multivariate Statistical Process Control (MSPC)
Principal Component Analysis (PCA)
R language
Issue date1-Jan-2018
PublisherSpringer
JournalLecture Notes in Computer Science
Abstract(s)The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it.
TypeConference paper
URIhttps://hdl.handle.net/1822/70506
ISBN978-3-319-95164-5
DOI10.1007/978-3-319-95165-2_26
ISSN0302-9743
Publisher versionhttps://link.springer.com/chapter/10.1007/978-3-319-95165-2_26
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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