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 language | English |
|---|---|
| Title of host publication | Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 |
| Pages | 272-277 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2011 Sep 22 |
| Event | 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China Duration: 2011 Jul 10 → 2011 Jul 12 |
Other
| Other | 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 11/7/10 → 11/7/12 |
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Keywords
- Bootstrap method
- Hotelling's T One class classification
- k-means data description
- k-means-based T
- Multivariate control chart
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
Cite this
Clustering algorithm-based control charts. / Kang, Ji Hoon; Kim, Seoung Bum.
Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011. 2011. p. 272-277 5984096.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Clustering algorithm-based control charts
AU - Kang, Ji Hoon
AU - Kim, Seoung Bum
PY - 2011/9/22
Y1 - 2011/9/22
N2 - 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.
AB - 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.
KW - Bootstrap method
KW - Hotelling's T One class classification
KW - k-means data description
KW - k-means-based T
KW - Multivariate control chart
UR - http://www.scopus.com/inward/record.url?scp=80052883903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052883903&partnerID=8YFLogxK
U2 - 10.1109/ISI.2011.5984096
DO - 10.1109/ISI.2011.5984096
M3 - Conference contribution
AN - SCOPUS:80052883903
SN - 9781457700828
SP - 272
EP - 277
BT - Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
ER -