Approach to identifying out-of-control variables in multivariate T 2 control chart using AIC

Yasuhiko Takemoto, Rumi Tanaka, Ikuo Arizono

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

Abstract

The control chart is a primary tool of judging whether a manufacturing process is in-control or not. Especially, the T2 control chart is known as a multivariate control chart that monitors a mean vector of several related quality characteristics. It is an important issue to identify which quality characteristics are responsible for out-of-control signal when a multivariate control chart signals. This paper considers a method of identifying quality characteristics responsible for out-of-control signal on operating the T2 control chart.

Original languageEnglish
Title of host publication2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013 - Proceedings
Pages39-44
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013 - Hiroshima, Japan
Duration: Jul 13 2013Jul 13 2013

Other

Other2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013
CountryJapan
CityHiroshima
Period7/13/137/13/13

    Fingerprint

Keywords

  • Akaike information criterion. Decomposition of T statistic
  • Hotelling T control chart
  • Multivariate statistical process control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Takemoto, Y., Tanaka, R., & Arizono, I. (2013). Approach to identifying out-of-control variables in multivariate T 2 control chart using AIC. In 2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013 - Proceedings (pp. 39-44). [6624780] https://doi.org/10.1109/IWCIA.2013.6624780