Heteroscedastic latent variable modelling with applications to multivariate statistical process control
Main Author: | |
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Publication Date: | 2006 |
Other Authors: | |
Format: | Article |
Language: | eng |
Source: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Download full: | http://hdl.handle.net/10316/3796 https://doi.org/10.1016/j.chemolab.2005.07.002 |
Summary: | We present an approach for conducting multivariate statistical process control (MSPC) in noisy environments, i.e., when the signal to noise ratio is low, and, furthermore, noise standard deviation (uncertainty) affecting each collected value can vary over time, and is assumingly known. This approach is based upon a latent variable model structure, HLV (standing for heteroscedastic latent variable model), that explicitly integrates information regarding data uncertainty. Moderate amounts of missing data can also be handled in a coherent and fully integrated way through HLV. Several examples show the added value achieved under noisy conditions by adopting such an approach and a case study illustrates its application to a real industrial context of pulp and paper product quality data analysis. |
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Heteroscedastic latent variable modelling with applications to multivariate statistical process controlMultivariate statistical process controlMeasurement uncertaintyLatent variable modellingWe present an approach for conducting multivariate statistical process control (MSPC) in noisy environments, i.e., when the signal to noise ratio is low, and, furthermore, noise standard deviation (uncertainty) affecting each collected value can vary over time, and is assumingly known. This approach is based upon a latent variable model structure, HLV (standing for heteroscedastic latent variable model), that explicitly integrates information regarding data uncertainty. Moderate amounts of missing data can also be handled in a coherent and fully integrated way through HLV. Several examples show the added value achieved under noisy conditions by adopting such an approach and a case study illustrates its application to a real industrial context of pulp and paper product quality data analysis.http://www.sciencedirect.com/science/article/B6TFP-4GX1HVW-2/1/c5e6b0a181b2fb4ffd7803ff38c9dace2006info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleaplication/PDFhttp://hdl.handle.net/10316/3796http://hdl.handle.net/10316/3796https://doi.org/10.1016/j.chemolab.2005.07.002engChemometrics and Intelligent Laboratory Systems. 80:1 (2006) 57-66Reis, Marco S.Saraiva, Pedro M.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-11-06T16:48:52Zoai:estudogeral.uc.pt:10316/3796Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:59:15.427270Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
title |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
spellingShingle |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control Reis, Marco S. Multivariate statistical process control Measurement uncertainty Latent variable modelling |
title_short |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
title_full |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
title_fullStr |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
title_full_unstemmed |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
title_sort |
Heteroscedastic latent variable modelling with applications to multivariate statistical process control |
author |
Reis, Marco S. |
author_facet |
Reis, Marco S. Saraiva, Pedro M. |
author_role |
author |
author2 |
Saraiva, Pedro M. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Reis, Marco S. Saraiva, Pedro M. |
dc.subject.por.fl_str_mv |
Multivariate statistical process control Measurement uncertainty Latent variable modelling |
topic |
Multivariate statistical process control Measurement uncertainty Latent variable modelling |
description |
We present an approach for conducting multivariate statistical process control (MSPC) in noisy environments, i.e., when the signal to noise ratio is low, and, furthermore, noise standard deviation (uncertainty) affecting each collected value can vary over time, and is assumingly known. This approach is based upon a latent variable model structure, HLV (standing for heteroscedastic latent variable model), that explicitly integrates information regarding data uncertainty. Moderate amounts of missing data can also be handled in a coherent and fully integrated way through HLV. Several examples show the added value achieved under noisy conditions by adopting such an approach and a case study illustrates its application to a real industrial context of pulp and paper product quality data analysis. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/3796 http://hdl.handle.net/10316/3796 https://doi.org/10.1016/j.chemolab.2005.07.002 |
url |
http://hdl.handle.net/10316/3796 https://doi.org/10.1016/j.chemolab.2005.07.002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Chemometrics and Intelligent Laboratory Systems. 80:1 (2006) 57-66 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
aplication/PDF |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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