Integration of support vector machines and control charts for multivariate process monitoring

Panitarn Chongfuangprinya, Seoung Bum Kim, Sun Kyoung Park, Thuntee Sukchotrat

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Statistical process control tools have been used routinely to improve process capabilities through reliable on-line monitoring and diagnostic processes. In the present paper, we propose a novel multivariate control chart that integrates a support vector machine (SVM) algorithm, a bootstrap method, and a control chart technique to improve multivariate process monitoring. The proposed chart uses as the monitoring statistic the predicted probability of class (PoC) values from an SVM algorithm. The control limits of SVM-PoC charts are obtained by a bootstrap approach.A simulation study was conducted to evaluate the performance of the proposed SVM-PoC chart and to compare it with other data mining-based control charts and Hotelling's T 2 control charts under various scenarios. The results showed that the proposed SVM-PoC charts outperformed other multivariate control charts in nonnormal situations. Further, we developed an exponential weighed moving average version of the SVM-PoC charts for increasing sensitivity to small shifts.

Original languageEnglish
Pages (from-to)1157-1173
Number of pages17
JournalJournal of Statistical Computation and Simulation
Issue number9
Publication statusPublished - 2011 Sept

Bibliographical note

Funding Information:
We would like to thank the Editor and reviewers, whose comments helped greatly in improving the presentation of this paper. S.B. Kim’s work was supported by startup funds from Korea University.


  • Bootstrap
  • Data mining
  • Multivariate control charts
  • Statistical quality control
  • Support vector machines

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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