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Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

dc.contributor.authorHarrou, Fouzi
dc.contributor.authorMadakyaru, Muddu
dc.contributor.authorSun, Ying
dc.date.accessioned2017-05-31T11:23:11Z
dc.date.available2017-05-31T11:23:11Z
dc.date.issued2017-02-16
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.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.
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.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
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journal2016 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.conference.date2016-12-06 to 2016-12-09
dc.conference.name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
dc.conference.locationAthens, GRC
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal University, India
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2018-06-14T05:27:29Z
dc.date.published-online2017-02-16
dc.date.published-print2016-12


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