Premium
A sum of squares triple exponentially weighted moving average control chart
Author(s) -
Chatterjee Kashinath,
Koukouvinos Christos,
Lappa Angeliki
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.2865
Subject(s) - ewma chart , control chart , x bar chart , statistical process control , chart , statistics , explained sum of squares , total sum of squares , control limits , moving average , mathematics , process (computing) , least squares function approximation , residual sum of squares , lack of fit sum of squares , standard deviation , computer science , non linear least squares , estimator , operating system
Abstract Control charts are widely known quality tools used to detect and control industrial process deviations in statistical process control. In the current paper, we propose a new single memory‐type control chart, called the sum of squares triple exponentially weighted moving average control chart (referred as SS‐TEWMA chart), that simultaneously detects shifts in the process mean and/or process dispersion. The run length performance of the proposed SS‐TEWMA control chart is compared with that of the sum of squares EWMA, sum of squares double EWMA, sum of squares generally weighted moving average, and sum of squares double generally weighted moving average, control charts, through Monte Carlo simulations. The comparisons indicate that the proposed chart is more efficient, than the competing ones, in detecting small shifts in the process mean and/or variability for most of the considered scenarios, while it has comparable performance for some others in identifying large shifts in the process mean and small to large shifts in the process variability. Finally, two illustrative examples are provided to explain the application of the SS‐TEWMA control chart.