Clustering algorithm-based control charts

Ji Hoon Kang, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hotelling's T2 control chart is widely used as a representative method to efficiently monitor multivariate processes. However, they have some parametric restrictions that may not be applicable for modern manufacturing systems complicated. In the present study we propose a clustering algorithm-based control chart that overcomes the limitation posed by the parametric assumption in existing control chart methods. The simulation results showed that the proposed clustering algorithm-based control charts outperformed Hotelling's T2 control charts especially when process data follow the nonnormal distributions.

Original languageEnglish
Title of host publicationProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Pages272-277
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China
Duration: 2011 Jul 102011 Jul 12

Publication series

NameProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

Other

Other2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
CountryChina
CityBeijing
Period11/7/1011/7/12

Keywords

  • Bootstrap method
  • Hotelling's T One class classification
  • Multivariate control chart
  • k-means data description
  • k-means-based T

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Kang, J. H., & Kim, S. B. (2011). Clustering algorithm-based control charts. In Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 (pp. 272-277). [5984096] (Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011). https://doi.org/10.1109/ISI.2011.5984096