An assessment of the kernel-distance-based multivariate control chart through an industrial application
Article first published online: 15 JUL 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Quality and Reliability Engineering International
Volume 27, Issue 4, pages 391–401, June 2011
How to Cite
Gani, W., Taleb, H. and Limam, M. (2011), An assessment of the kernel-distance-based multivariate control chart through an industrial application. Qual. Reliab. Engng. Int., 27: 391–401. doi: 10.1002/qre.1117
- Issue published online: 23 MAY 2011
- Article first published online: 15 JUL 2010
- Laboratory of operational research, decision and control (LARODEC) of the Institut Supérieur de Gestion de Tunis, University of Tunis, Tunisia
- statistical quality control;
- support vector machines;
- Hotelling's T2;
- Average Run Length
Traditional multivariate quality control charts assume that quality characteristics follow a multivariate normal distribution. However, in many industrial applications the process distribution is not known, implying the need to construct a flexible control chart appropriate for real applications. A promising approach is to use support vector machines in statistical process control. This paper focuses on the application of the ‘kernel-distance-based multivariate control chart’, also known as the ‘k-chart’, to a real industrial process, and its assessment by comparing it to Hotelling's T2 control chart, based on the number of out-of-control observations and on the Average Run Length. The industrial application showed that the k-chart is sensitive to small shifts in mean vector and outperforms the T2 control chart in terms of Average Run Length. Copyright © 2010 John Wiley & Sons, Ltd.