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Multivariate Control Charts for Monitoring the Mean Vector and Covariance Matrix

Summary: [This abstract is based on the authors' abstract.]Multivariate control charts are investigated for the simultaneous monitoring of the mean vector and covariance matrix when the joint distribution of process variables is multivariate normal. The focus of the study is on the use of combinations of multivariate exponentially weighted moving average (MEWMA) control charts based on sample means and the sum of the squared deviations from target. The performance of these combinations is compared with the performance of standard multivariate Shewhart charts and to combinations of univariate EWMA charts. Since the performance of many control charts depends on the direction of the shift in the mean vector or covariance matrix, performance is also investigated for specific shift directions, as well as for averages over all directions. The best performance is achieved using a combination of MEWMA charts based on the sample means and on the sum of squared regression adjusted deviations from target.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Average time to signal,Exponentially weighted moving average (EWMA),Multivariate control charts,Regression analysis,Shewhart control chart,Statistical process control (SPC),Steady state processes,Variation from target
  • Author: Reynolds, Marion R., Jr.; Cho, Gyo-Young
  • Journal: Journal of Quality Technology