Computers and Industrial Engineering
Control charts applications for multivariate attribute processes
Computers and Industrial Engineering
Computers and Industrial Engineering
Phase II monitoring of multivariate simple linear profiles
Computers and Industrial Engineering
Using neural networks to detect the bivariate process variance shifts pattern
Computers and Industrial Engineering
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In this article, we propose a multivariate synthetic double sampling T^2 chart to monitor the mean vector of a multivariate process. The proposed chart combines the double sampling (DS) T^2 chart and the conforming run length (CRL) chart. On the whole, the proposed chart performs better than its standard counterparts, namely, the Hotelling's T^2, DS T^2, and synthetic T^2 charts, in terms of the average run length (ARL) and average number of observations to sample (ANOS). The proposed chart also outperforms the multivariate exponentially weighted moving average (MEWMA) chart for moderate and large shifts but the latter is more sensitive than the former towards small shifts. For a variable sample size chart, like the synthetic DS T^2 chart, ANOS is a more meaningful performance measure than ARL. ANOS relates to the actual number of observations sampled but ARL merely deals with the number of sampling stages taken. Interpretation based on ARL is more complicated as either n"1 or n"1+n"2 observations are taken in each sampling stage.