An application of supervised multivariate control charts on a power plant data

Thuntee Sukchotrat, Seoung Bum Kim, Victoria C.P. Chen, Clint Carter, William Dockery, Robert Tapia

Research output: Contribution to conferencePaperpeer-review


We propose a supervised data mining approach that takes advantage of available out-of-control data. By using classification techniques, our proposed approach can utilize both the in-control and out-of-control data information to construct a control chart. The probability that a data point belongs to a certain class from the classification result is plotted and the control limits can be established. A real power plant data set is used to illustrate our proposed approach. The comparative study shows that our proposed approach performs better than traditional multivariate control chart techniques.

Original languageEnglish
Number of pages6
Publication statusPublished - 2008
Externally publishedYes
EventIIE Annual Conference and Expo 2008 - Vancouver, BC, Canada
Duration: 2008 May 172008 May 21


OtherIIE Annual Conference and Expo 2008
CityVancouver, BC


  • Classification analysis
  • Data mining
  • Multivariate control chart
  • Power plant
  • Supervised method

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Industrial and Manufacturing Engineering


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