Self-Starting CUSCORE Control Charts for Individual Multivariate Observations

Giovanna Capizzi · Guido Masarotto

24 citations0/0 files storedJournal of Quality Technology

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Advanced Statistical Process Monitoring Management and Optimization Techniques Fault Detection and Control Systems

Abstract

In some manufacturing settings, such as during process start-up and in the case of short production runs, process parameters are unknown, and Phase I samples cannot be gathered to accurately estimate control limits for prospective monitoring. Self-starting charts can be applied to these low-volume applications. In this article, two new self-starting multivariate control charts, both based on a CUSCORE-type procedure, are proposed for monitoring the unknown mean of a multivariate normal distribution. These charting procedures, which weight current observations according to the information contained in the fault signature, are able to outperform the previously suggested self-starting charts, which neglect the dynamic pattern of the mean change.

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References

1992A Multivariate Exponentially Weighted Moving Average Control Chart 1995Detection of Abrupt Changes: Theory and Applications.2006Effects of Parameter Estimation on Control Chart Properties: A Literature Review 2003An Adaptive Exponentially Weighted Moving Average Control Chart 1993Regression Adjustment for Variables in Multivariate Quality Control 1993The Effect of Sample Size on Estimated Limits forXandXControl Charts 2001The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters 1991SPCQCharts for Start-Up Processes and Short or Long Runs

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2024A nonparametric adaptive EWMA control chart for monitoring mixed continuous and categorical data using self-starting strategy 2018Self-Starting control charts for monitoring logistic regression profiles 2013A Directional Multivariate Sign EWMA Control Chart 2018Nonparametric monitoring of multiple count data 2014Adaptive estimation with change detection for streaming data

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