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Journal ArticleDOI

A review of multivariate control charts

TLDR
A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s as mentioned in this paper, where several recent articles that give methods for interpreting an out-of control signal on a multivariate control chart are analyzed and discussed.
Abstract
A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s. Multivariate cumulative sum (CUSUM) control procedures and a multivariate exponentially weighted moving average (EWMA) control chart are reviewed and recommendations are made regarding their use. Several recent articles that give methods for interpreting an out-of-control signal on a multivariate control chart are analyzed and discussed. Other topics such as the use of principal components and regression adjustment of variables in MQC, as well as frequently used approximations in MQC, are discussed.

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Journal ArticleDOI

Research Issues and Ideas in Statistical Process Control

TL;DR: An overview of current research on control charting methods for process monitoring and improvement and a historical perspective and ideas for future research are given.
Journal ArticleDOI

On-line monitoring when the process yields a linear profile

TL;DR: In this article, control charts monitor processes where performance is measured by one or more quality characteristics, such as a profile or function, but some processes are characterized by a profile and function.
Journal ArticleDOI

Effects of Parameter Estimation on Control Chart Properties; A Literature Review

TL;DR: In this article, the control chart limits are calculated using parameter estimates from an in-control Phase I reference sample, and statistics based on new samples are compared with the estimated control limits to monitor for departures from the in..
Journal ArticleDOI

Multivariate statistical process control charts: an overview

TL;DR: In this paper, the basic procedures for the implementation of multivariate statistical process control via control charting are discussed, and the most significant methods for the interpretation of an out-of-control signal are described.
Posted Content

Multivariate Statistical Process Control Charts: An Overview

TL;DR: In this paper, the basic procedures for the implementation of multivariate statistical process control via control charting are discussed, and the most significant methods for the interpretation of an out-of-control signal are described.
References
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Book

Introduction to Statistical Quality Control

TL;DR: In this article, the authors present a survey of statistical process control and capability analysis techniques for improving the quality of a business process in the modern business environment, using a variety of techniques.
Journal Article

Exponentially weighted moving average control schemes: Properties and enhancements

TL;DR: The recognition that an EWMA control scheme can be represented as a Markov chain allows its properties to be evaluated more easily and completely than has previously been done.
Journal ArticleDOI

Exponentially weighted moving average control schemes: properties and enhancements

TL;DR: In this article, the authors evaluate the properties of an exponentially weighted moving average (EWMA) control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value.
Journal ArticleDOI

A multivariate exponentially weighted moving average control chart

TL;DR: In this article, a multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure.
Journal ArticleDOI

Multivariate statistical monitoring of process operating performance

TL;DR: Using this method of statistical data compression a multivariate monitoring procedure analogous to the univariate Shewart Chart has been developed to efficiently monitor the performance of large processes, and to rapidly detect and identify important process changes.