Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

dc.conference.date2016-12-06 to 2016-12-09
dc.conference.locationAthens, GRC
dc.conference.name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorMadakyaru, Muddu
dc.contributor.authorSun, Ying
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal University, India
dc.date.accessioned2017-05-31T11:23:11Z
dc.date.available2017-05-31T11:23:11Z
dc.date.issued2017-02-16
dc.date.published-online2017-02-16
dc.date.published-print2016-12
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.
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.
dc.eprint.versionPost-print
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.
dc.identifier.doi10.1109/SSCI.2016.7849878
dc.identifier.journal2016 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.identifier.urihttp://hdl.handle.net/10754/623873
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7849878/
dc.rights(c) 2016 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.
dc.subjectComputational modeling
dc.subjectData models
dc.subjectHigh definition video
dc.subjectMeasurement
dc.subjectMonitoring
dc.subjectPredictive models
dc.subjectProbability distribution
dc.titleNonlinear partial least squares with Hellinger distance for nonlinear process monitoring
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0002-2138-319X&spc.sf=dc.date.issued&spc.sd=DESC">Harrou, Fouzi</a> <a href="https://orcid.org/0000-0002-2138-319X" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Madakyaru, Muddu,equals">Madakyaru, Muddu</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-6703-4270&spc.sf=dc.date.issued&spc.sd=DESC">Sun, Ying</a> <a href="https://orcid.org/0000-0001-6703-4270" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Applied Mathematics and Computational Science Program,equals">Applied Mathematics and Computational Science Program</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,equals">Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Statistics Program,equals">Statistics Program</a><br><br><h5>KAUST Grant Number</h5>OSR-2015-CRG4-2582<br><br><h5>Online Publication Date</h5>2017-02-16<br><br><h5>Print Publication Date</h5>2016-12<br><br><h5>Date</h5>2017-02-16</span>
display.details.right<span><h5>Abstract</h5>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.<br><br><h5>Citation</h5>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.<br><br><h5>Acknowledgements</h5>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.<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Institute of Electrical and Electronics Engineers (IEEE),equals">Institute of Electrical and Electronics Engineers (IEEE)</a><br><br><h5>Journal</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.journal=2016 IEEE Symposium Series on Computational Intelligence (SSCI),equals">2016 IEEE Symposium Series on Computational Intelligence (SSCI)</a><br><br><h5>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016,equals">2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1109/SSCI.2016.7849878">10.1109/SSCI.2016.7849878</a><br><br><h5>Additional Links</h5>http://ieeexplore.ieee.org/document/7849878/</span>
kaust.grant.numberOSR-2015-CRG4-2582
kaust.personHarrou, Fouzi
kaust.personSun, Ying
orcid.authorHarrou, Fouzi::0000-0002-2138-319X
orcid.authorMadakyaru, Muddu
orcid.authorSun, Ying::0000-0001-6703-4270
orcid.id0000-0001-6703-4270
orcid.id0000-0002-2138-319X
refterms.dateFOA2018-06-14T05:27:29Z
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