Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

Handle URI:
http://hdl.handle.net/10754/623850
Title:
Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
Authors:
Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Process 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Madakyaru 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.
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.2016.7849880
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/7849880/
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMadakyaru, Mudduen
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.date.accessioned2017-05-31T11:23:09Z-
dc.date.available2017-05-31T11:23:09Z-
dc.date.issued2017-02-16en
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.en
dc.identifier.doi10.1109/SSCI.2016.7849880en
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.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/7849880/en
dc.subjectComputational modelingen
dc.subjectData modelsen
dc.subjectFault detectionen
dc.subjectMonitoringen
dc.subjectPredictive modelsen
dc.subjectTestingen
dc.subjectWavelet transformsen
dc.titleImproved anomaly detection using multi-scale PLS and generalized likelihood ratio testen
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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