Multivariate statistical process control with artificial contrasts

Wookyeon Hwang, George Runger, Eugene Tuv

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


A multivariate control region can be considered to be a pattern that represents the normal operating conditions of a process. Reference data can then be generated and used to learn the difference between this region and random noise. Then multivariate statistical process control can be converted to a supervised learning task. This can dramatically reshape the control region and open the control problem to a rich collection of supervised learning tools. Such tools provide generalization error estimates that can be used to specify error rates. The effectiveness of such an approach is shown here. Such a computational approach is now easily accomplished with modern computing resources. Examples use random forests and a regularized least squares classifier as the learners.

Original languageEnglish (US)
Pages (from-to)659-669
Number of pages11
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number6
StatePublished - Jun 2007


  • Classification
  • Control chart
  • False alarm
  • Random forest
  • Regularization
  • Supervised learning

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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