A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation
A new approach to identify and diagnose the quality of extensive and multivariate data is presented, using the gage repeatability and reproducibility (GR&R) study through the weighting of rotated factor scores. The proposal uses axis rotation to improve the explanation and interpretations of...
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doaj-a1bc402135a9407fbb06443093b8732a2021-03-30T04:00:15ZengIEEEIEEE Access2169-35362020-01-01818355718357010.1109/ACCESS.2020.30190319174975A Gage Study Through the Weighting of Latent Variables Under Orthogonal RotationFabricio Alves De Almeida0https://orcid.org/0000-0002-6460-1580Simone Carneiro Streitenberger1https://orcid.org/0000-0001-6393-0078Alexandre Fonseca Torres2https://orcid.org/0000-0002-7341-6387Anderson Paulo De Paiva3https://orcid.org/0000-0002-8199-411XJose Henrique De Freitas Gomes4https://orcid.org/0000-0002-7676-554XInstitute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá, BrazilInstitute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá, BrazilInstitute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá, BrazilInstitute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá, BrazilInstitute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá, BrazilA new approach to identify and diagnose the quality of extensive and multivariate data is presented, using the gage repeatability and reproducibility (GR&R) study through the weighting of rotated factor scores. The proposal uses axis rotation to improve the explanation and interpretations of latent information, providing a statistically appropriate alternative when dealing with two or more correlated data sets. To analyze data with a significant variance-covariance structure, factor analysis (FA) is applied for calculating the eigenvalues and extracting of the rotated scores. Once obtained, these scores are then weighted with their respective eigenvalue for each factor. This procedure results in a single response vector, which is capable of properly interpreting all of the quality responses analyzed. To illustrate an application of the method, a real data set from a resistance spot welding process is selected, and two different types of rotation are compared. The proposed method provided an output that contemplated all of the significant variability of the data in a unique and significant way. In addition, the method enabled a reduction in the data dimensionality, thus minimizing the time for analysis and computational effort.https://ieeexplore.ieee.org/document/9174975/Multivariate measurement systemrepeatability and reproducibilityorthogonal rotationweighted factor analysisresistance spot welding |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fabricio Alves De Almeida Simone Carneiro Streitenberger Alexandre Fonseca Torres Anderson Paulo De Paiva Jose Henrique De Freitas Gomes |
spellingShingle |
Fabricio Alves De Almeida Simone Carneiro Streitenberger Alexandre Fonseca Torres Anderson Paulo De Paiva Jose Henrique De Freitas Gomes A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation IEEE Access Multivariate measurement system repeatability and reproducibility orthogonal rotation weighted factor analysis resistance spot welding |
author_facet |
Fabricio Alves De Almeida Simone Carneiro Streitenberger Alexandre Fonseca Torres Anderson Paulo De Paiva Jose Henrique De Freitas Gomes |
author_sort |
Fabricio Alves De Almeida |
title |
A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation |
title_short |
A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation |
title_full |
A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation |
title_fullStr |
A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation |
title_full_unstemmed |
A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation |
title_sort |
gage study through the weighting of latent variables under orthogonal rotation |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
A new approach to identify and diagnose the quality of extensive and multivariate data is presented, using the gage repeatability and reproducibility (GR&R) study through the weighting of rotated factor scores. The proposal uses axis rotation to improve the explanation and interpretations of latent information, providing a statistically appropriate alternative when dealing with two or more correlated data sets. To analyze data with a significant variance-covariance structure, factor analysis (FA) is applied for calculating the eigenvalues and extracting of the rotated scores. Once obtained, these scores are then weighted with their respective eigenvalue for each factor. This procedure results in a single response vector, which is capable of properly interpreting all of the quality responses analyzed. To illustrate an application of the method, a real data set from a resistance spot welding process is selected, and two different types of rotation are compared. The proposed method provided an output that contemplated all of the significant variability of the data in a unique and significant way. In addition, the method enabled a reduction in the data dimensionality, thus minimizing the time for analysis and computational effort. |
topic |
Multivariate measurement system repeatability and reproducibility orthogonal rotation weighted factor analysis resistance spot welding |
url |
https://ieeexplore.ieee.org/document/9174975/ |
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