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.
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Acknowledgments
This work has been supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n\(^o\) 002814; Funding Reference: POCI-01-0247-FEDER-002814], COMPETE: POCI-01-0145-FEDER-007043 and FCT - (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013.
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Braga, A.C. et al. (2018). Multivariate Statistical Process Control Based on Principal Component Analysis: Implementation of Framework in R. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_26
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DOI: https://doi.org/10.1007/978-3-319-95165-2_26
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