Multivariate variability monitoring using EWMA control charts based on squared deviation of observations from target

Recent research works have shown that control statistics based on squared deviation of observations from target have the ability to monitor variability in both univariate and multivariate processes. In the current research, the properties of the control statistic St that has been proposed by Huwang et al. (2007) are first reviewed and three new St-based multivariate schemes are then presented. Extensive simulation experiments are performed to compare the performances of the proposed schemes with those of the multivariate exponentially weighted mean squared deviation (MEWMS) and the L1-norm distance of the MEWMS deviation from its expected value (MEWMSL1) charts. The results show that one of the proposed schemes outperforms the others in detecting shifts in correlation coefficients and another has the best general performance among the compared charts in detecting shifts in which at least one of the variances changes.