Abstract
Control chart patterns (CCPs) are widely used to identify the potential process problems in modern manufacturing industries. The earliest statistical techniques, including $${\bar{\rm X}}$$ chart and R chart, are respectively used for monitoring process mean and process variance. Recently, pattern recognition techniques based on artificial neural network (ANN) are very popular to be applied to recognize unnatural CCPs. However, most of them are limited to recognize simple CCPs arising from single type of unnatural variation. In other words, they are incapable to handle the problem of concurrent CCPs where two types of unnatural variation exist together within the manufacturing process. To facilitate the research gap, this paper presents a hybrid approach based on independent component analysis (ICA) and decision tree (DT) to identify concurrent CCPs. Without loss of generality, six types of concurrent CCPs are used to validate the proposed method. Experimental results show that the proposed approach is very successful to handle most of the concurrent CCPs. The proposed method has two limitations in real application: it needs at least two concurrent CCPs to reconstruct their source patterns and it may be incapable to handle the concurrent pattern incurred by two correlated process ("upward trend" and "upward shift").
| Original language | English |
|---|---|
| Pages (from-to) | 409-419 |
| Number of pages | 11 |
| Journal | Journal of Intelligent Manufacturing |
| Volume | 20 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2009 Aug 1 |
Fingerprint
Keywords
- Concurrent control chart
- Decision tree
- Independent component analysis
- Pattern recognition
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Industrial and Manufacturing Engineering
Cite this
A hybrid approach for identification of concurrent control chart patterns. / Wang, Chih Hsuan; Dong, Tse-ping; Kuo, Way.
In: Journal of Intelligent Manufacturing, Vol. 20, No. 4, 01.08.2009, p. 409-419.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A hybrid approach for identification of concurrent control chart patterns
AU - Wang, Chih Hsuan
AU - Dong, Tse-ping
AU - Kuo, Way
PY - 2009/8/1
Y1 - 2009/8/1
N2 - Control chart patterns (CCPs) are widely used to identify the potential process problems in modern manufacturing industries. The earliest statistical techniques, including $${\bar{\rm X}}$$ chart and R chart, are respectively used for monitoring process mean and process variance. Recently, pattern recognition techniques based on artificial neural network (ANN) are very popular to be applied to recognize unnatural CCPs. However, most of them are limited to recognize simple CCPs arising from single type of unnatural variation. In other words, they are incapable to handle the problem of concurrent CCPs where two types of unnatural variation exist together within the manufacturing process. To facilitate the research gap, this paper presents a hybrid approach based on independent component analysis (ICA) and decision tree (DT) to identify concurrent CCPs. Without loss of generality, six types of concurrent CCPs are used to validate the proposed method. Experimental results show that the proposed approach is very successful to handle most of the concurrent CCPs. The proposed method has two limitations in real application: it needs at least two concurrent CCPs to reconstruct their source patterns and it may be incapable to handle the concurrent pattern incurred by two correlated process ("upward trend" and "upward shift").
AB - Control chart patterns (CCPs) are widely used to identify the potential process problems in modern manufacturing industries. The earliest statistical techniques, including $${\bar{\rm X}}$$ chart and R chart, are respectively used for monitoring process mean and process variance. Recently, pattern recognition techniques based on artificial neural network (ANN) are very popular to be applied to recognize unnatural CCPs. However, most of them are limited to recognize simple CCPs arising from single type of unnatural variation. In other words, they are incapable to handle the problem of concurrent CCPs where two types of unnatural variation exist together within the manufacturing process. To facilitate the research gap, this paper presents a hybrid approach based on independent component analysis (ICA) and decision tree (DT) to identify concurrent CCPs. Without loss of generality, six types of concurrent CCPs are used to validate the proposed method. Experimental results show that the proposed approach is very successful to handle most of the concurrent CCPs. The proposed method has two limitations in real application: it needs at least two concurrent CCPs to reconstruct their source patterns and it may be incapable to handle the concurrent pattern incurred by two correlated process ("upward trend" and "upward shift").
KW - Concurrent control chart
KW - Decision tree
KW - Independent component analysis
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=67749088653&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67749088653&partnerID=8YFLogxK
U2 - 10.1007/s10845-008-0115-3
DO - 10.1007/s10845-008-0115-3
M3 - Article
AN - SCOPUS:67749088653
VL - 20
SP - 409
EP - 419
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
IS - 4
ER -