Stable and Unstable Pattern Recognition Using <i>D</i>² and SVM: A Multivariate Approach

• Main Authors: Pamela Chiñas-Sanchez, Ismael Lopez-Juarez, Jose Antonio Vazquez-Lopez, Abdelkader El Kamel, Jose Luis Navarro-Gonzalez
• Format: Article
• Language: English
• Published: MDPI AG 2021-12-01
• Series: Mathematics
• Subjects: multivariate control charts; Mahalanobis distance; Support Vector Machine (SVM)
• Online Access: https://www.mdpi.com/2227-7390/9/1/10
• ISSN: 2227-7390

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 <inline-formula><math display="inline"><semantics><mrow><mo stretchy="false">(</mo><msup><mi>D</mi><mn>2</mn></msup><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula> 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 <inline-formula><math display="inline"><semantics><mrow><mn>91.6</mn><mo>%</mo></mrow></semantics></math></inline-formula>.