Abstract
One-class classification plays a key role in the detection of outliers and abnormalities. Recently, several attempts have been made to extend the application of one-class classification techniques to statistical process control problems, where many of these one-class classification-based approaches have used a support vector data description algorithm. The monitoring statistics for a support vector data description-based control chart are sufficiently defined. However, the control limits are not obvious because the procedure used to derive the control limit does not include a method for controlling the false alarm rate (i.e., Type I error rate), which clearly limits its use in process monitoring. In this study, we propose a new multivariate control chart based on a technique for optimal false alarm-controlled support vector data description, which minimizes the radius of a spherically shaped boundary so that it includes the normal data that are equal to an assigned constant value. By modifying this constant value, users can precisely control the proportion of abnormal data determined by the spherically shaped boundary, which equals the expected Type I error rate. We demonstrated the usefulness of the proposed charts in experiments with simulated data and real process data based on a thin film transistor-liquid crystal display.
| Original language | English |
|---|---|
| Journal | Journal of Process Control |
| DOIs | |
| Publication status | Accepted/In press - 2017 |
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Keywords
- Control chart
- Machine learning
- One-class classification
- Process control
- Support vector data description
ASJC Scopus subject areas
- Control and Systems Engineering
- Modelling and Simulation
- Computer Science Applications
- Industrial and Manufacturing Engineering
Cite this
Optimal false alarm controlled support vector data description for multivariate process monitoring. / Kim, Younghoon; Kim, Seoung Bum.
In: Journal of Process Control, 2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimal false alarm controlled support vector data description for multivariate process monitoring
AU - Kim, Younghoon
AU - Kim, Seoung Bum
PY - 2017
Y1 - 2017
N2 - One-class classification plays a key role in the detection of outliers and abnormalities. Recently, several attempts have been made to extend the application of one-class classification techniques to statistical process control problems, where many of these one-class classification-based approaches have used a support vector data description algorithm. The monitoring statistics for a support vector data description-based control chart are sufficiently defined. However, the control limits are not obvious because the procedure used to derive the control limit does not include a method for controlling the false alarm rate (i.e., Type I error rate), which clearly limits its use in process monitoring. In this study, we propose a new multivariate control chart based on a technique for optimal false alarm-controlled support vector data description, which minimizes the radius of a spherically shaped boundary so that it includes the normal data that are equal to an assigned constant value. By modifying this constant value, users can precisely control the proportion of abnormal data determined by the spherically shaped boundary, which equals the expected Type I error rate. We demonstrated the usefulness of the proposed charts in experiments with simulated data and real process data based on a thin film transistor-liquid crystal display.
AB - One-class classification plays a key role in the detection of outliers and abnormalities. Recently, several attempts have been made to extend the application of one-class classification techniques to statistical process control problems, where many of these one-class classification-based approaches have used a support vector data description algorithm. The monitoring statistics for a support vector data description-based control chart are sufficiently defined. However, the control limits are not obvious because the procedure used to derive the control limit does not include a method for controlling the false alarm rate (i.e., Type I error rate), which clearly limits its use in process monitoring. In this study, we propose a new multivariate control chart based on a technique for optimal false alarm-controlled support vector data description, which minimizes the radius of a spherically shaped boundary so that it includes the normal data that are equal to an assigned constant value. By modifying this constant value, users can precisely control the proportion of abnormal data determined by the spherically shaped boundary, which equals the expected Type I error rate. We demonstrated the usefulness of the proposed charts in experiments with simulated data and real process data based on a thin film transistor-liquid crystal display.
KW - Control chart
KW - Machine learning
KW - One-class classification
KW - Process control
KW - Support vector data description
UR - http://www.scopus.com/inward/record.url?scp=85032979858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032979858&partnerID=8YFLogxK
U2 - 10.1016/j.jprocont.2017.10.012
DO - 10.1016/j.jprocont.2017.10.012
M3 - Article
AN - SCOPUS:85032979858
JO - Journal of Process Control
JF - Journal of Process Control
SN - 0959-1524
ER -