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https://hdl.handle.net/1822/70506
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Campo DC | Valor | Idioma |
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dc.contributor.author | Braga, A. C. | por |
dc.contributor.author | Barros, Cláudia | por |
dc.contributor.author | Delgado, Pedro | por |
dc.contributor.author | Martins, Cristina | por |
dc.contributor.author | Sousa, Sandra | por |
dc.contributor.author | Velosa, J. C. | por |
dc.contributor.author | Delgado, Isabel | por |
dc.contributor.author | Sampaio, Paulo | por |
dc.date.accessioned | 2021-03-01T16:17:52Z | - |
dc.date.available | 2021-03-01T16:17:52Z | - |
dc.date.issued | 2018-01-01 | - |
dc.identifier.isbn | 978-3-319-95164-5 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/1822/70506 | - |
dc.description.abstract | 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. | por |
dc.description.sponsorship | FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013) | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.rights | openAccess | por |
dc.subject | Contribution plots | por |
dc.subject | Control charts | por |
dc.subject | Multivariate Statistical Process Control (MSPC) | por |
dc.subject | Principal Component Analysis (PCA) | por |
dc.subject | R language | por |
dc.title | Multivariate statistical process control based on principal component analysis: implementation of framework in R | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-95165-2_26 | por |
oaire.citationStartPage | 366 | por |
oaire.citationEndPage | 381 | por |
oaire.citationVolume | 10961 LNCS | por |
dc.date.updated | 2021-03-01T15:52:17Z | - |
dc.identifier.doi | 10.1007/978-3-319-95165-2_26 | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 8983 | - |
sdum.journal | Lecture Notes in Computer Science | por |
sdum.conferencePublication | International Conference on Computational Science and Its Applications | por |
sdum.bookTitle | Computational Science and Its Applications – ICCSA 2018 | por |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ICCSA2018_artigo_PCA_R_v02_draft.pdf | 800,97 kB | Adobe PDF | Ver/Abrir |