An assessment of the kernel-distance-based multivariate control chart through an industrial application

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.