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    Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

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    Name:
    Improved Anomaly Detection Using Multi-scale PLS.pdf
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    Type
    Conference Paper
    Authors
    Madakyaru, Muddu
    Harrou, Fouzi cc
    Sun, Ying cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2017-02-16
    Online Publication Date
    2017-02-16
    Print Publication Date
    2016-12
    Permanent link to this record
    http://hdl.handle.net/10754/623850
    
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    Abstract
    Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
    Citation
    Madakyaru M, Harrou F, Sun Y (2016) Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2016.7849880.
    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 Symposium Series on Computational Intelligence (SSCI)
    Conference/Event name
    2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
    DOI
    10.1109/SSCI.2016.7849880
    Additional Links
    http://ieeexplore.ieee.org/document/7849880/
    ae974a485f413a2113503eed53cd6c53
    10.1109/SSCI.2016.7849880
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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