Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory

Handle URI:
http://hdl.handle.net/10754/595955
Title:
Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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
Issue Date:
7-Dec-2015
DOI:
10.1109/SSCI.2015.64
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7376637
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.date.accessioned2016-02-09T13:38:58Zen
dc.date.available2016-02-09T13:38:58Zen
dc.date.issued2015-12-07en
dc.identifier.doi10.1109/SSCI.2015.64en
dc.identifier.urihttp://hdl.handle.net/10754/595955en
dc.description.abstractAccurate 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7376637en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleEnhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theoryen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2015 IEEE Symposium Series on Computational Intelligenceen
dc.conference.date7-10 Dec. 2015en
dc.conference.name2015 IEEE Symposium Series on Computational Intelligenceen
dc.conference.locationCape Townen
dc.eprint.versionPost-printen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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