Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

Handle URI:
http://hdl.handle.net/10754/623037
Title:
Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring
Authors:
Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Fault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault 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 obtained using fault-free data. Furthermore, to enhance further the robustness of these methods to measurement noise, and reduce the false alarms due to modeling errors, wavelet-based multiscale filtering of residuals is used before the application of the HD-based monitoring scheme. The performances of the developed NLPLS-HD fault detection technique is illustrated using simulated plug flow reactor data. The results show that the proposed method provides favorable performance for detection of faults compared to the conventional NLPLS method.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Harrou F, Madakyaru M, Sun Y (2017) Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring. Energy and Buildings. Available: http://dx.doi.org/10.1016/j.enbuild.2017.03.033.
Publisher:
Elsevier BV
Journal:
Energy and Buildings
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
18-Mar-2017
DOI:
10.1016/j.enbuild.2017.03.033
Type:
Article
ISSN:
0378-7788
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://www.sciencedirect.com/science/article/pii/S0378778816308520
Appears in Collections:
Articles; 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.accessioned2017-03-20T08:46:09Z-
dc.date.available2017-03-20T08:46:09Z-
dc.date.issued2017-03-18en
dc.identifier.citationHarrou F, Madakyaru M, Sun Y (2017) Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring. Energy and Buildings. Available: http://dx.doi.org/10.1016/j.enbuild.2017.03.033.en
dc.identifier.issn0378-7788en
dc.identifier.doi10.1016/j.enbuild.2017.03.033en
dc.identifier.urihttp://hdl.handle.net/10754/623037-
dc.description.abstractFault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault 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 obtained using fault-free data. Furthermore, to enhance further the robustness of these methods to measurement noise, and reduce the false alarms due to modeling errors, wavelet-based multiscale filtering of residuals is used before the application of the HD-based monitoring scheme. The performances of the developed NLPLS-HD fault detection technique is illustrated using simulated plug flow reactor data. The results show that the proposed method provides favorable performance for detection of faults compared to the conventional NLPLS method.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.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0378778816308520en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Energy and Buildings. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy and Buildings, [, , (2017-03-18)] DOI: 10.1016/j.enbuild.2017.03.033 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectAnomaly detectionen
dc.subjectHellinger distanceen
dc.subjectNonlinear PLSen
dc.subjectNonlinear processesen
dc.subjectMultiscale filteringen
dc.titleImproved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoringen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalEnergy and Buildingsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal University, Manipal, Indiaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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