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

Authors

  • Walid Gani,

    Corresponding author
    1. Laboratory of Operational Research, Decision and Control (LARODEC), Institut Supérieur de Gestion de Tunis, 41 Avenue de la Liberté, 2000 Bardo, University of Tunis, Tunisia
    • Laboratory of Operational Research, Decision and Control (LARODEC), Institut Supérieur de Gestion de Tunis, 41 Avenue de la Liberté, 2000 Bardo, University of Tunis, Tunisia
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  • Hassen Taleb,

    1. Laboratory of Operational Research, Decision and Control (LARODEC), Institut Supérieur de Gestion de Tunis, 41 Avenue de la Liberté, 2000 Bardo, University of Tunis, Tunisia
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  • Mohamed Limam

    1. Laboratory of Operational Research, Decision and Control (LARODEC), Institut Supérieur de Gestion de Tunis, 41 Avenue de la Liberté, 2000 Bardo, University of Tunis, Tunisia
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    • Professor.


Abstract

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.

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