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A clustering approach to identify the time of a step change in Shewhart control charts
Author(s) -
Ghazanfari Mehdi,
Alaeddini Adel,
Niaki Seyed Taghi Akhavan,
Aryanezhad MirBahador
Publication year - 2008
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.925
Subject(s) - control chart , shewhart individuals control chart , cluster analysis , statistical process control , process (computing) , computer science , chart , ewma chart , statistics , data mining , mathematics , artificial intelligence , operating system
Control charts are the most popular statistical process control tools used to monitor process changes. When a control chart indicates an out‐of‐control signal it means that the process has changed. However, control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as the change‐point estimation problem. Most of the traditional methods of estimating the process change point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart change points. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase‐I and phase‐II of normal and non‐normal processes. At the end, we discuss the performance of the proposed method in comparison with the traditional procedures through extensive simulation studies. Copyright © 2008 John Wiley & Sons, Ltd.