Monitoring Variability and Analyzing Multivariate Autocorrelated Processes

Jeffrey E. Jarrett · Xia Pan

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Advanced Statistical Process Monitoring Statistical Methods and Applications Fault Detection and Control Systems

Abstract

Traditional multivariate quality control charts are based on independent observations. In this paper, we explain how to extend univariate residual charts to multivariate cases and how to combine the traditional statistical process control (SPC) approaches to monitor changes in process variability in a dynamic environment. We propose using Alt's (1984) Alt, F. B. 1984. Multivariate quality control. The Encyclopedia of Statistical Sciences, : 110–122. [Google Scholar] W chart on vector autoregressive (VAR) residuals to monitor the variability for multivariate processes in the presence of autocorrelation. We study examples jointly using the Hotelling T2 chart on VAR residuals, the W chart, and the Portmanteau test to diagnose the types of shift in process parameters.

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References

1992A Multivariate Exponentially Weighted Moving Average Control Chart 1988Time-Series Modeling for Statistical Process Control 1991Some Statistical Process Control Methods for Autocorrelated Data 1992Multivariate Control Charts for Individual Observations 1991Statistical process control procedures for correlated observations 1991Multivariate Quality Control Based on Regression-Adjusted Variables 1994Run-Length Distributions of Special-Cause Control Charts for Correlated Processes 1999EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes

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2020An adaptive variable-parameters scheme for the simultaneous monitoring of the mean and variability of an autocorrelated multivariate normal process 2015Total quality management (TQM) movement in public health 2022Monitoring the multivariate coefficient of variation in presence of autocorrelation with variable parameters control charts 2016Monitoring multistage processes with autocorrelated observations 2009Multivariate Process Control charts and their use in monitoring output quality: a perspective

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