Tuned artificial contrasts to detect signals

J. Hu, George Runger, E. Tuv

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Quick detection of shifts under specific faults in multivariate statistical process control has been of interest. Process knowledge can be exploited to design a control chart to be sensitive to more specific mean shifts. Out-of-control observations are simulated representing the shifts resulted from the specific faults and thus the detection problem is converted to a supervised learning task. A control region can be learned through the classifier. The effectiveness of this approach is shown here through graphical illustrations in comparison with the results from normal theory and error rate tables.

Original languageEnglish (US)
Pages (from-to)5527-5534
Number of pages8
JournalInternational Journal of Production Research
Volume45
Issue number23
DOIs
StatePublished - Dec 1 2007

Keywords

  • Artificial contrasts
  • Classification
  • Multivariate SPC

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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