Abstract
Statistical process control tools have been used routinely to improve process capabilities through reliable on-line monitoring and diagnostic processes. In the present paper, we propose a novel multivariate control chart that integrates a support vector machine (SVM) algorithm, a bootstrap method, and a control chart technique to improve multivariate process monitoring. The proposed chart uses as the monitoring statistic the predicted probability of class (PoC) values from an SVM algorithm. The control limits of SVM-PoC charts are obtained by a bootstrap approach.A simulation study was conducted to evaluate the performance of the proposed SVM-PoC chart and to compare it with other data mining-based control charts and Hotelling's T 2 control charts under various scenarios. The results showed that the proposed SVM-PoC charts outperformed other multivariate control charts in nonnormal situations. Further, we developed an exponential weighed moving average version of the SVM-PoC charts for increasing sensitivity to small shifts.
| Original language | English |
|---|---|
| Pages (from-to) | 1157-1173 |
| Number of pages | 17 |
| Journal | Journal of Statistical Computation and Simulation |
| Volume | 81 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2011 Sep 1 |
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Keywords
- Bootstrap
- Data mining
- Multivariate control charts
- Statistical quality control
- Support vector machines
ASJC Scopus subject areas
- Applied Mathematics
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
Cite this
Integration of support vector machines and control charts for multivariate process monitoring. / Chongfuangprinya, Panitarn; Kim, Seoung Bum; Park, Sun Kyoung; Sukchotrat, Thuntee.
In: Journal of Statistical Computation and Simulation, Vol. 81, No. 9, 01.09.2011, p. 1157-1173.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Integration of support vector machines and control charts for multivariate process monitoring
AU - Chongfuangprinya, Panitarn
AU - Kim, Seoung Bum
AU - Park, Sun Kyoung
AU - Sukchotrat, Thuntee
PY - 2011/9/1
Y1 - 2011/9/1
N2 - Statistical process control tools have been used routinely to improve process capabilities through reliable on-line monitoring and diagnostic processes. In the present paper, we propose a novel multivariate control chart that integrates a support vector machine (SVM) algorithm, a bootstrap method, and a control chart technique to improve multivariate process monitoring. The proposed chart uses as the monitoring statistic the predicted probability of class (PoC) values from an SVM algorithm. The control limits of SVM-PoC charts are obtained by a bootstrap approach.A simulation study was conducted to evaluate the performance of the proposed SVM-PoC chart and to compare it with other data mining-based control charts and Hotelling's T 2 control charts under various scenarios. The results showed that the proposed SVM-PoC charts outperformed other multivariate control charts in nonnormal situations. Further, we developed an exponential weighed moving average version of the SVM-PoC charts for increasing sensitivity to small shifts.
AB - Statistical process control tools have been used routinely to improve process capabilities through reliable on-line monitoring and diagnostic processes. In the present paper, we propose a novel multivariate control chart that integrates a support vector machine (SVM) algorithm, a bootstrap method, and a control chart technique to improve multivariate process monitoring. The proposed chart uses as the monitoring statistic the predicted probability of class (PoC) values from an SVM algorithm. The control limits of SVM-PoC charts are obtained by a bootstrap approach.A simulation study was conducted to evaluate the performance of the proposed SVM-PoC chart and to compare it with other data mining-based control charts and Hotelling's T 2 control charts under various scenarios. The results showed that the proposed SVM-PoC charts outperformed other multivariate control charts in nonnormal situations. Further, we developed an exponential weighed moving average version of the SVM-PoC charts for increasing sensitivity to small shifts.
KW - Bootstrap
KW - Data mining
KW - Multivariate control charts
KW - Statistical quality control
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=80051690841&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80051690841&partnerID=8YFLogxK
U2 - 10.1080/00949651003789074
DO - 10.1080/00949651003789074
M3 - Article
AN - SCOPUS:80051690841
VL - 81
SP - 1157
EP - 1173
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
SN - 0094-9655
IS - 9
ER -