SPC with correlated observations for the chemical and process industries

Christina M. Mastrangelo, Douglas Montgomery

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

In the use of statistical control charts, violating the basic assumption of independent or uncorrelated data results in a chart that exhibits poor statistical performance, resulting in an increased number of false alarms. Autocorrelated data requires modifications to traditional control chart techniques. A method based on the exponentially weighted moving average that uses variable control limits is presented. Using simulation, we explore the shift detection properties of this moving centre-line technique and will show how the detection capability of the procedure can be enhanced using supplemental tracking signal tests. Guidelines and conditions for use are also presented.

Original languageEnglish (US)
Pages (from-to)79-89
Number of pages11
JournalQuality and Reliability Engineering International
Volume11
Issue number2
StatePublished - Mar 1995

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Industry
Process industry
Control charts
Chemical industry
Exponentially weighted moving average
Control variable
Simulation
Charts

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Engineering (miscellaneous)

Cite this

SPC with correlated observations for the chemical and process industries. / Mastrangelo, Christina M.; Montgomery, Douglas.

In: Quality and Reliability Engineering International, Vol. 11, No. 2, 03.1995, p. 79-89.

Research output: Contribution to journalArticle

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