Detection of process upsets—sample autocorrelation control chart and group autocorrelation control chart applications

Steven A. Yourstone, Douglas Montgomery

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

This paper presents an application of the sample autocorrelation function to statistical process control where the process data are serially correlated. Two innovative control charts are illustrated: the sample autocorrelation control chart and the group autocorrelation control chart. The important feature is that these control charts will detect shifts in the autocorrelative structure as well as shifts in the mean of the process. The sample autocorrelation function is typically used to identify an appropriate ARIMA model for a time series. The sample autocorrelation function may also be used as the basis of control charts to detect process upsets. Two unique features distingush this application of the sample autocorrelation function to statistical process control. First, the sample autocorrelations are exponentially smoothed estimates. This allows the user to control the sensitivity of the sample autocorrelation control chart. Secondly, the sample autocorrelation control chart is applied to a continuous stream of data—rather than to a static set of data that has been used to fit an ARIMA model.

Original languageEnglish (US)
Pages (from-to)133-140
Number of pages8
JournalQuality and Reliability Engineering International
Volume7
Issue number3
DOIs
StatePublished - Jan 1 1991

Keywords

  • Exponentially weighted moving average
  • Group autocorrelation control chart
  • Model shift
  • Pattern
  • Sample autocorrelation control chart
  • Sample autocorrelation function

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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