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    Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory

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    Enhanced Anomaly Detection via PLS Regression.pdf
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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
    Date
    2016-01-11
    Online Publication Date
    2016-01-11
    Print Publication Date
    2015-12
    Permanent link to this record
    http://hdl.handle.net/10754/595955
    
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    Abstract
    Accurate and effective fault detection and diagnosis of modern engineering systems is crucial for ensuring reliability, safety and maintaining the desired product quality. In this work, we propose an innovative method for detecting small faults in the highly correlated multivariate data. The developed method utilizes partial least square (PLS) method as a modelling framework, and the symmetrized Kullback-Leibler divergence (KLD) as a monitoring index, where it is used to quantify the dissimilarity between probability distributions of current PLS-based residual and reference one obtained using fault-free data. The performance of the PLS-based KLD fault detection algorithm is illustrated and compared to the conventional PLS-based fault detection methods. Using synthetic data, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional methods, especially when data are highly correlated and small faults are of interest.
    Citation
    Fouzi, H., & Sun, Y. (2015). Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory. 2015 IEEE Symposium Series on Computational Intelligence. doi:10.1109/ssci.2015.64
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2015 IEEE Symposium Series on Computational Intelligence
    Conference/Event name
    2015 IEEE Symposium Series on Computational Intelligence
    DOI
    10.1109/SSCI.2015.64
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7376637
    ae974a485f413a2113503eed53cd6c53
    10.1109/SSCI.2015.64
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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