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  • 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)
    Wang, Qilin
    Year published
    2015
    Metadata
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    Abstract
    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 ...
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    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.
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    Journal Title
    Chemometrics and Intelligent Laboratory Systems
    Volume
    149
    DOI
    https://doi.org/10.1016/j.chemolab.2015.08.012
    Subject
    Applied Mathematics not elsewhere classified
    Applied Mathematics
    Analytical Chemistry
    Publication URI
    http://hdl.handle.net/10072/339798
    Collection
    • Journal articles

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