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A Phase II depth‐based variable dimension EWMA control chart for monitoring process mean
Author(s) -
Yang SuFen,
Lin YiChi,
Yeh Arthur B.
Publication year - 2021
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.2863
Subject(s) - ewma chart , control chart , statistical process control , statistic , process (computing) , statistics , computer science , multivariate statistics , shewhart individuals control chart , dimension (graph theory) , chart , data mining , mathematics , operating system , pure mathematics
Statistical process control consists of tools and techniques that are useful for improving a process or ensuring that a process is in a stable and satisfactory state. In many modern industrial applications, it is critically important to simultaneously monitor two or more correlated process quality variables, thus necessitating the development of multivariate statistical process control (MSPC) as an important area of research for the new century. Nevertheless, the existing MSPC research is mostly based on the assumption that the process data follow a multinormal distribution or a known distribution. However, it is well recognized that in many applications the underlying process distribution is unknown. In practice, among a set of correlated variables to be monitored, there is oftentimes a subset of variables that are easy and/or inexpensive to measure, whereas the remaining variables are difficult and/or expensive to measure but contain information that may help more quickly detect a shift in the process mean. We are motivated to develop a Phase II control chart to monitor variable dimension (VD) mean vector for unknown multivariate processes. The proposed chart is based on the exponentially weighted moving average (EWMA) of a depth‐based statistic. The proposed chart is shown to lead to faster detection of mean shifts than the existing VD T 2 and VD EWMA T 2 charts studied in Aparisi et al. and Epprecht et al., respectively.