Improved Monitoring of Multivariate Process Variability

Maman A. Djauhari

85 citations0/0 files storedJournal of Quality Technology

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Management Systems and Quality Improvement

Abstract

The standard way of using the determinant of the average of sample covariance matrices for multivariate process variability control charting leads to a biased estimate of the control limits. In this paper, we present an improved control chart having unbiased control limits. It has smaller ARL than the standard chart and, hence, it is more sensitive to shifts in the process variability. An example is given to illustrate these advantages.

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References

2001Introduction to Statistical Quality Control 1999Research Issues and Ideas in Statistical Process Control 1995Decomposition of T 2 for Multivariate Control Chart Interpretation 1996A Comparison of Multivariate Control Charts for Individual Observations 1994Multivariate statistical process control—recent results and directions for future research 1997Assessment of Multivariate Process Control Techniques

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2009New capability indices for evaluating the performance of multivariate manufacturing processes 2007Step-Down Analysis for Changes in the Covariance Matrix and Other Parameters 2008Multivariate Process Variability Monitoring 2008Multivariate EWMA control charts using individual observations for process mean and variance monitoring and diagnosis 2008A Multivariate Synthetic Control Chart for Process Dispersion 2009Monitoring Variation in a Multivariate Process When the Dimension is Large Relative to the Sample Size 2019Tr(R 2 ) control charts based on kernel density estimation for monitoring multivariate variability process

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