Statistical process monitoring with integration of data projection and one-class classification
Author(s)
Liu, Yiqi
Pan, Yongping
Wang, Qilin
Huang, Daoping
Griffith University Author(s)
Year published
2015
Metadata
Show full item recordAbstract
One-class classification (OCC) has attracted a great deal of attentions from various disciplines. Few attempts are made to extend the scope of such application for process monitoring. In the present work, the Principal Component Analysis (PCA) and Variational Bayesian Principal Component Analysis (VBPCA) approach provides a powerful tool to project original data into lower data set as well as spreading different types of faults with different directions. This, along with multiple types of one-class classifiers (density-based, boundary-based, reconstruction-based and combination-based) that are able to isolate abnormal data ...
View more >One-class classification (OCC) has attracted a great deal of attentions from various disciplines. Few attempts are made to extend the scope of such application for process monitoring. In the present work, the Principal Component Analysis (PCA) and Variational Bayesian Principal Component Analysis (VBPCA) approach provides a powerful tool to project original data into lower data set as well as spreading different types of faults with different directions. This, along with multiple types of one-class classifiers (density-based, boundary-based, reconstruction-based and combination-based) that are able to isolate abnormal data from normal one, supported the design of process monitoring. These methodologies have been validated by process data collected from a Wastewater Treatment Plant (WWTP). The results showed that the proposed methodology is capable of detecting sensor faults and process faults with good accuracy under different scenarios.
View less >
View more >One-class classification (OCC) has attracted a great deal of attentions from various disciplines. Few attempts are made to extend the scope of such application for process monitoring. In the present work, the Principal Component Analysis (PCA) and Variational Bayesian Principal Component Analysis (VBPCA) approach provides a powerful tool to project original data into lower data set as well as spreading different types of faults with different directions. This, along with multiple types of one-class classifiers (density-based, boundary-based, reconstruction-based and combination-based) that are able to isolate abnormal data from normal one, supported the design of process monitoring. These methodologies have been validated by process data collected from a Wastewater Treatment Plant (WWTP). The results showed that the proposed methodology is capable of detecting sensor faults and process faults with good accuracy under different scenarios.
View less >
Journal Title
Chemometrics and Intelligent Laboratory Systems
Volume
149
Subject
Applied mathematics
Applied mathematics not elsewhere classified
Analytical chemistry