Process Monitoring Based on Dissimilarity of Time Series Data
For process monitoring, principal component analysis (PCA) has been widely used. Since PCA can describe the correlation among variables, PC-based monitoring outperforms traditional statistical process control methods, such as the Shewhart chart. Nevertheless, PC-based monitoring cannot detect change...
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| Published in | KAGAKU KOGAKU RONBUNSHU Vol. 25; no. 6; pp. 1004 - 1009 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
The Society of Chemical Engineers, Japan
1999
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0386-216X 1349-9203 |
| DOI | 10.1252/kakoronbunshu.25.1004 |
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| Abstract | For process monitoring, principal component analysis (PCA) has been widely used. Since PCA can describe the correlation among variables, PC-based monitoring outperforms traditional statistical process control methods, such as the Shewhart chart. Nevertheless, PC-based monitoring cannot detect changes in the correlation while the indices monitored are within their control limits. In the present work, a new monitoring method based on distributions of data is proposed, noting that distributions of data reflect the corresponding operational conditions. In order to quantitatively evaluate differences between two data sets, dissimilarity is defined and calculated by applying PCA to transformed-data matrices. The proposed monitoring method and the traditional PC-based method are compared using simulated data. The results of this study clearly indicate the advantage of the proposed method. |
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| AbstractList | For process monitoring, principal component analysis (PCA) has been widely used. Since PCA can describe the correlation among variables, PC-based monitoring outperforms traditional statistical process control methods, such as the Shewhart chart. Nevertheless, PC-based monitoring cannot detect changes in the correlation while the indices monitored are within their control limits. In the present work, a new monitoring method based on distributions of data is proposed, noting that distributions of data reflect the corresponding operational conditions. In order to quantitatively evaluate differences between two data sets, dissimilarity is defined and calculated by applying PCA to transformed-data matrices. The proposed monitoring method and the traditional PC-based method are compared using simulated data. The results of this study clearly indicate the advantage of the proposed method. |
| Author | KANO, MANABU OHNO, HIROMU HASEBE, SHINJI HASHIMOTO, IORI |
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| References | Ku, W., R. H. Storer and C. Georgakis ; “Disturbance Detection and Isolation by Dynamic Principal Component Analysis, ” Chemometrics and Intelligent Laboratory Systems, 30,179-196 (1995) Fukunaga, K. and W. L. G. Koontz ; “Application of the KarhunenLoeve Expansion to Feature Selection and Ordering, ” IEEE Trans. Comput., C-19,311-318 (1970) Nomikos, P. and J. F. MacGregor ; “Monitoring Batch Processes Using Multiway Principal Component Analysis, ” AIChE J., 40, 1361-1375 (1994) Kano, M., K. Nagao, H. Ohno, S. Hasebe and I. Hashimoto ; “Process Monitoring Using Moving Principal Component Analysis, ” Kagaku Kogaku Ronbunshu, 25 (to appear) Kourti, T., J. Lee, and J. F. MacGregor ; “Experiences with Industrial Applications of Projection Methods for Multivariate Statistical Process Control, ” Computers Chem. Engng., 20, S 745-S 750 (1996) |
| References_xml | – reference: Kourti, T., J. Lee, and J. F. MacGregor ; “Experiences with Industrial Applications of Projection Methods for Multivariate Statistical Process Control, ” Computers Chem. Engng., 20, S 745-S 750 (1996) – reference: Ku, W., R. H. Storer and C. Georgakis ; “Disturbance Detection and Isolation by Dynamic Principal Component Analysis, ” Chemometrics and Intelligent Laboratory Systems, 30,179-196 (1995) – reference: Fukunaga, K. and W. L. G. Koontz ; “Application of the KarhunenLoeve Expansion to Feature Selection and Ordering, ” IEEE Trans. Comput., C-19,311-318 (1970) – reference: Kano, M., K. Nagao, H. Ohno, S. Hasebe and I. Hashimoto ; “Process Monitoring Using Moving Principal Component Analysis, ” Kagaku Kogaku Ronbunshu, 25 (to appear) – reference: Nomikos, P. and J. F. MacGregor ; “Monitoring Batch Processes Using Multiway Principal Component Analysis, ” AIChE J., 40, 1361-1375 (1994) |
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| Title | Process Monitoring Based on Dissimilarity of Time Series Data |
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