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Multivariate Process Monitoring Using the Dynamic Biplot

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  • Ross Sparks
  • Allan Adolphson
  • Aloke Phatak

Abstract

In this article, we present a method for monitoring multivariate process data based on the Gabriel biplot. In contrast to existing methods that are based on some form of dimension reduction, we use reduction to two dimensions for displaying the state of the process but all the data for determining whether it is in a state of statistical control. This approach allows us to detect changes in location, variation, and correlational structure accurately yet display a large amount of information concisely. We illustrate the use of the biplot on an example of industrial data and also discuss some of the issues related to a practical implementation of the method.

Suggested Citation

  • Ross Sparks & Allan Adolphson & Aloke Phatak, 1997. "Multivariate Process Monitoring Using the Dynamic Biplot," International Statistical Review, International Statistical Institute, vol. 65(3), pages 325-349, December.
    • Handle: RePEc:bla:istatr:v:65:y:1997:i:3:p:325-349
    DOI: 10.1111/j.1751-5823.1997.tb00312.x
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    Cited by:

    1. Maravelakis, Petros & Bersimis, Sotiris & Panaretos, John & Psarakis, Stelios, 2002. "Identifying the Out of Control Variable in a Multivariate Control Chart," MPRA Paper 6387, University Library of Munich, Germany.
    2. Trevor Cox, 2001. "Multidimensional scaling used in multivariate statistical process control," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 365-378.
    3. Petros Maravelakis & Sotirios Bersimis, 2009. "The use of Andrews curves for detecting the out-of-control variables when a multivariate control chart signals," Statistical Papers, Springer, vol. 50(1), pages 51-65, January.

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