Stable and Unstable Pattern Recognition Using D 2 and SVM: A Multivariate Approach

Pamela Chiñas-Sanchez, Ismael Lopez-Juarez, Jose Antonio Vazquez-Lopez, Abdelkader El Kamel and Jose Luis Navarro-Gonzalez
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Pamela Chiñas-Sanchez: Tecnologico Nacional de Mexico, Instituto Tecnologico de Saltillo, Saltillo 25280, Mexico
Ismael Lopez-Juarez: Centre for Research and Advanced Studies (CINVESTAV), Ramos Arizpe 25900, Mexico
Jose Antonio Vazquez-Lopez: Tecnologico Nacional de Mexico, Instituto Tecnologico de Celaya, Celaya 38010, Mexico
Abdelkader El Kamel: Ecole Centrale de Lille, 59650 Villeneuve d’ascq, France
Jose Luis Navarro-Gonzalez: IJ Robotics SA de CV, Saltillo 25000, Mexico

Mathematics, 2020, vol. 9, issue 1, 1-12

Abstract: Control charts are used to visually identify the signals that define the behavior of industrial processes in univariate cases. However, whenever the statistical quality of more than one critical variable needs to be monitored simultaneously, the procedure becomes much more complicated. This paper presents a methodology on multivariate pattern recognition using the Mahalanobis distance ( D 2 ) and the Support Vector Machine (SVM) technique to recognise two multivariate patterns. The relevance of the study lies in the monitoring of the variables while considering the correlation between them and the effects of interchangeably using a stable multivariate case against an unstable pattern that results in recognition rates up to 91.6 % .

Keywords: multivariate control charts; Mahalanobis distance; Support Vector Machine (SVM) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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