Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

Handle URI:
http://hdl.handle.net/10754/623873
Title:
Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring
Authors:
Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Harrou F, Madakyaru M, Sun Y (2016) Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2016.7849878.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
KAUST Grant Number:
OSR-2015-CRG4-2582
Conference/Event name:
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Issue Date:
16-Feb-2017
DOI:
10.1109/SSCI.2016.7849878
Type:
Conference Paper
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.
Additional Links:
http://ieeexplore.ieee.org/document/7849878/
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.authorMadakyaru, Mudduen
dc.contributor.authorSun, Yingen
dc.date.accessioned2017-05-31T11:23:11Z-
dc.date.available2017-05-31T11:23:11Z-
dc.date.issued2017-02-16en
dc.identifier.citationHarrou F, Madakyaru M, Sun Y (2016) Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2016.7849878.en
dc.identifier.doi10.1109/SSCI.2016.7849878en
dc.identifier.urihttp://hdl.handle.net/10754/623873-
dc.description.abstractThis paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.en
dc.description.sponsorshipThis 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7849878/en
dc.subjectComputational modelingen
dc.subjectData modelsen
dc.subjectHigh definition videoen
dc.subjectMeasurementen
dc.subjectMonitoringen
dc.subjectPredictive modelsen
dc.subjectProbability distributionen
dc.titleNonlinear partial least squares with Hellinger distance for nonlinear process monitoringen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2016 IEEE Symposium Series on Computational Intelligence (SSCI)en
dc.conference.date2016-12-06 to 2016-12-09en
dc.conference.name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016en
dc.conference.locationAthens, GRCen
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal University, Indiaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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