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An assessment of the kernel‐distance‐based multivariate control chart through an industrial application
Author(s) -
Gani Walid,
Taleb Hassen,
Limam Mohamed
Publication year - 2011
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1117
Subject(s) - control chart , chart , multivariate statistics , \bar x and r chart , ewma chart , shewhart individuals control chart , statistical process control , control limits , radar chart , computer science , x bar chart , process capability , statistics , kernel (algebra) , process (computing) , data mining , mathematics , engineering , work in process , operations management , combinatorics , operating system
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 T 2 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 T 2 control chart in terms of Average Run Length. Copyright © 2010 John Wiley & Sons, Ltd.