Optimal false alarm controlled support vector data description for multivariate process monitoring

Younghoon Kim, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


One-class classification plays a key role in the detection of outliers and abnormalities. Recently, several attempts have been made to extend the application of one-class classification techniques to statistical process control problems, where many of these one-class classification-based approaches have used a support vector data description algorithm. The monitoring statistics for a support vector data description-based control chart are sufficiently defined. However, the control limits are not obvious because the procedure used to derive the control limit does not include a method for controlling the false alarm rate (i.e., Type I error rate), which clearly limits its use in process monitoring. In this study, we propose a new multivariate control chart based on a technique for optimal false alarm-controlled support vector data description, which minimizes the radius of a spherically shaped boundary so that it includes the normal data that are equal to an assigned constant value. By modifying this constant value, users can precisely control the proportion of abnormal data determined by the spherically shaped boundary, which equals the expected Type I error rate. We demonstrated the usefulness of the proposed charts in experiments with simulated data and real process data based on a thin film transistor–liquid crystal display.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalJournal of Process Control
Publication statusPublished - 2018 May

Bibliographical note

Funding Information:
The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were very helpful in improving the quality of the paper. This research was supported by Brain Korea PLUS , Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning ( NRF-2016R1A2B1008994 ), and the Ministry of Trade, Industry & Energy under the Industrial Technology Innovation Program ( R1623371 ).

Publisher Copyright:
© 2017 Elsevier Ltd


  • Control chart
  • Machine learning
  • One-class classification
  • Process control
  • Support vector data description

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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