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    PLS-based memory control scheme for enhanced process monitoring

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    INDIN2016_PD-009717.pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Harrou, Fouzi cc
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2017-01-20
    Online Publication Date
    2017-01-20
    Print Publication Date
    2016-07
    Permanent link to this record
    http://hdl.handle.net/10754/622826
    
    Metadata
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    Abstract
    Fault detection is important for safe operation of various modern engineering systems. Partial least square (PLS) has been widely used in monitoring highly correlated process variables. Conventional PLS-based methods, nevertheless, often fail to detect incipient faults. In this paper, we develop new PLS-based monitoring chart, combining PLS with multivariate memory control chart, the multivariate exponentially weighted moving average (MEWMA) monitoring chart. The MEWMA are sensitive to incipient faults in the process mean, which significantly improves the performance of PLS methods and widen their applicability in practice. Using simulated distillation column data, we demonstrate that the proposed PLS-based MEWMA control chart is more effective in detecting incipient fault in the mean of the multivariate process variables, and outperform the conventional PLS-based monitoring charts.
    Citation
    Harrou F, Sun Y (2016) PLS-based memory control scheme for enhanced process monitoring. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2016.7819208.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE 14th International Conference on Industrial Informatics (INDIN)
    DOI
    10.1109/INDIN.2016.7819208
    Additional Links
    http://ieeexplore.ieee.org/document/7819208/
    ae974a485f413a2113503eed53cd6c53
    10.1109/INDIN.2016.7819208
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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