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 (D2) 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%.
Highlights
To this day, industrial processes have been pushed towards the mass employment of machinery, devices, personal, and work operations, among others; these objects of production increase the sheer complexity of monitoring tasks and of stability analyses.Control Charts (CC) are used in processes of monitoring and stability analysis
The method determines the association of multivariate patterns with univariate variables
Multivariate patterns formed by Mahalanobis distances were calculated with different multivariate variables
Summary
Pamela Chiñas-Sanchez 1 , Ismael Lopez-Juarez 2, *,† , Jose Antonio Vazquez-Lopez 3 , Abdelkader El Kamel 4 and Jose Luis Navarro-Gonzalez 5. Current address: Ind Metalurgica 1062, P Ind Saltillo-Ramos Arizpe, Ramos Arizpe, Coahuila 25900, Mexico
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