Show simple item record

dc.contributor.authorMadakyaru, Muddu
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.date.accessioned2017-05-31T11:23:09Z
dc.date.available2017-05-31T11:23:09Z
dc.date.issued2017-02-16
dc.identifier.citationMadakyaru M, Harrou F, Sun Y (2016) Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2016.7849880.
dc.identifier.doi10.1109/SSCI.2016.7849880
dc.identifier.urihttp://hdl.handle.net/10754/623850
dc.description.abstractProcess monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
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/7849880/
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.subjectFault detection
dc.subjectMonitoring
dc.subjectPredictive models
dc.subjectTesting
dc.subjectWavelet transforms
dc.titleImproved anomaly detection using multi-scale PLS and generalized likelihood ratio test
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.dateFOA2018-06-14T02:55:47Z
dc.date.published-online2017-02-16
dc.date.published-print2016-12


Files in this item

Thumbnail
Name:
Improved Anomaly Detection Using Multi-scale PLS.pdf
Size:
698.1Kb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record