Abstract
When building a multivariate SPC model, it is commonly assumed that there is only one operational mode in the baseline data. However, multiple operational modes may exist. It is important to know the number of modes in the data in order to construct an effective process control system. Each operational mode appears as a cluster in the baseline data. This paper proposes a method to identify the correct number of clusters in baseline data. None of the existing methods for finding the number of clusters has all three of the following critical features: (i) the proposed method can determine if there is only one cluster, the most common case in baseline data; (ii) it can identify clusters that are convex or non-convex; and (iii) it is insensitive to user-specified parameters. The paper includes a comparison of the existing and proposed methods on four datasets. The proposed method consistently gives the correct number of clusters whereas the existing methods are unable to do so.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1103-1110 |
| Number of pages | 8 |
| Journal | IIE Transactions (Institute of Industrial Engineers) |
| Volume | 39 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2007 |
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All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
Keywords
- Clustering
- Data mining
- Multivariate statistical process control
- Number of clusters
- Phase I
Cite this
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Determining the number of operational modes in baseline multivariate SPC data. / Zhang, Hang; Albin, Susan.
In: IIE Transactions (Institute of Industrial Engineers), Vol. 39, No. 12, 01.12.2007, p. 1103-1110.Research output: Contribution to journal › Article
TY - JOUR
T1 - Determining the number of operational modes in baseline multivariate SPC data
AU - Zhang, Hang
AU - Albin, Susan
PY - 2007/12/1
Y1 - 2007/12/1
N2 - When building a multivariate SPC model, it is commonly assumed that there is only one operational mode in the baseline data. However, multiple operational modes may exist. It is important to know the number of modes in the data in order to construct an effective process control system. Each operational mode appears as a cluster in the baseline data. This paper proposes a method to identify the correct number of clusters in baseline data. None of the existing methods for finding the number of clusters has all three of the following critical features: (i) the proposed method can determine if there is only one cluster, the most common case in baseline data; (ii) it can identify clusters that are convex or non-convex; and (iii) it is insensitive to user-specified parameters. The paper includes a comparison of the existing and proposed methods on four datasets. The proposed method consistently gives the correct number of clusters whereas the existing methods are unable to do so.
AB - When building a multivariate SPC model, it is commonly assumed that there is only one operational mode in the baseline data. However, multiple operational modes may exist. It is important to know the number of modes in the data in order to construct an effective process control system. Each operational mode appears as a cluster in the baseline data. This paper proposes a method to identify the correct number of clusters in baseline data. None of the existing methods for finding the number of clusters has all three of the following critical features: (i) the proposed method can determine if there is only one cluster, the most common case in baseline data; (ii) it can identify clusters that are convex or non-convex; and (iii) it is insensitive to user-specified parameters. The paper includes a comparison of the existing and proposed methods on four datasets. The proposed method consistently gives the correct number of clusters whereas the existing methods are unable to do so.
KW - Clustering
KW - Data mining
KW - Multivariate statistical process control
KW - Number of clusters
KW - Phase I
UR - http://www.scopus.com/inward/record.url?scp=36148930994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36148930994&partnerID=8YFLogxK
U2 - 10.1080/07408170701291787
DO - 10.1080/07408170701291787
M3 - Article
AN - SCOPUS:36148930994
VL - 39
SP - 1103
EP - 1110
JO - IISE Transactions
JF - IISE Transactions
SN - 2472-5854
IS - 12
ER -