Improved Data-based Fault Detection Strategy and Application to Distillation Columns

Handle URI:
http://hdl.handle.net/10754/622854
Title:
Improved Data-based Fault Detection Strategy and Application to Distillation Columns
Authors:
Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Madakyaru M, Harrou F, Sun Y (2017) Improved Data-based Fault Detection Strategy and Application to Distillation Columns. Process Safety and Environmental Protection. Available: http://dx.doi.org/10.1016/j.psep.2017.01.017.
Publisher:
Elsevier BV
Journal:
Process Safety and Environmental Protection
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
31-Jan-2017
DOI:
10.1016/j.psep.2017.01.017
Type:
Article
ISSN:
0957-5820
Sponsors:
The work reported in this paper was 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/S0957582017300228
Appears in Collections:
Articles; 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.accessioned2017-02-09T12:55:03Z-
dc.date.available2017-02-09T12:55:03Z-
dc.date.issued2017-01-31en
dc.identifier.citationMadakyaru M, Harrou F, Sun Y (2017) Improved Data-based Fault Detection Strategy and Application to Distillation Columns. Process Safety and Environmental Protection. Available: http://dx.doi.org/10.1016/j.psep.2017.01.017.en
dc.identifier.issn0957-5820en
dc.identifier.doi10.1016/j.psep.2017.01.017en
dc.identifier.urihttp://hdl.handle.net/10754/622854-
dc.description.abstractChemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.en
dc.description.sponsorshipThe work reported in this paper was 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/S0957582017300228en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Process Safety and Environmental Protection. 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 Process Safety and Environmental Protection, [, , (2017-01-31)] DOI: 10.1016/j.psep.2017.01.017 . © 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.subjectMulti-scale PLS modelsen
dc.subjectGLR hypothesis testingen
dc.subjectData uncertaintyen
dc.subjectProcess monitoringen
dc.subjectDistillation Columnsen
dc.titleImproved Data-based Fault Detection Strategy and Application to Distillation Columnsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProcess Safety and Environmental Protectionen
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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