Ozone Measurements Monitoring Using Data-Based Approach

Handle URI:
http://hdl.handle.net/10754/595441
Title:
Ozone Measurements Monitoring Using Data-Based Approach
Authors:
Harrou, Fouzi; Kadri, Farid; Khadraoui, Sofiane; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
The complexity of ozone (O3) formation mechanisms in the troposphere make the fast and accurate modeling of ozone very challenging. In the absence of a process model, principal component analysis (PCA) has been extensively used as a data-based monitoring technique for highly correlated process variables; however conventional PCA-based detection indices often fail to detect small or moderate anomalies. In this work, we propose an innovative method for detecting small anomalies in highly correlated multivariate data. The developed method combine the multivariate exponentially weighted moving average (MEWMA) monitoring scheme with PCA modelling in order to enhance anomaly detection performance. Such a choice is mainly motivated by the greater ability of the MEWMA monitoring scheme to detect small changes in the process mean. The proposed PCA-based MEWMA monitoring scheme is successfully applied to ozone measurements data collected from Upper Normandy region, France, via the network of air quality monitoring stations. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Ozone Measurements Monitoring Using Data-Based Approach 2016 Process Safety and Environmental Protection
Publisher:
Elsevier BV
Journal:
Process Safety and Environmental Protection
Issue Date:
1-Feb-2016
DOI:
10.1016/j.psep.2016.01.015
Type:
Article
ISSN:
09575820
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0957582016000203
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.authorKadri, Fariden
dc.contributor.authorKhadraoui, Sofianeen
dc.contributor.authorSun, Yingen
dc.date.accessioned2016-02-02T13:57:58Zen
dc.date.available2016-02-02T13:57:58Zen
dc.date.issued2016-02-01en
dc.identifier.citationOzone Measurements Monitoring Using Data-Based Approach 2016 Process Safety and Environmental Protectionen
dc.identifier.issn09575820en
dc.identifier.doi10.1016/j.psep.2016.01.015en
dc.identifier.urihttp://hdl.handle.net/10754/595441en
dc.description.abstractThe complexity of ozone (O3) formation mechanisms in the troposphere make the fast and accurate modeling of ozone very challenging. In the absence of a process model, principal component analysis (PCA) has been extensively used as a data-based monitoring technique for highly correlated process variables; however conventional PCA-based detection indices often fail to detect small or moderate anomalies. In this work, we propose an innovative method for detecting small anomalies in highly correlated multivariate data. The developed method combine the multivariate exponentially weighted moving average (MEWMA) monitoring scheme with PCA modelling in order to enhance anomaly detection performance. Such a choice is mainly motivated by the greater ability of the MEWMA monitoring scheme to detect small changes in the process mean. The proposed PCA-based MEWMA monitoring scheme is successfully applied to ozone measurements data collected from Upper Normandy region, France, via the network of air quality monitoring stations. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0957582016000203en
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, 1 February 2016. DOI: 10.1016/j.psep.2016.01.015en
dc.subjectAnomaly detectionen
dc.subjectMEWMA statisticen
dc.subjectMSPCen
dc.subjectPrincipal components analysisen
dc.subjectOzone pollutionen
dc.subjectData-driven strategyen
dc.titleOzone Measurements Monitoring Using Data-Based Approachen
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.institutionPIMM Laboratory, UMR CNRS 800, Arts et Métiers ParisTech, Paris, Franceen
dc.contributor.institutionUniversity of Sharjah, Department of Electrical and Computer Engineering, Sharjah, United Arab Emiratesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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