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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorKadri, Farid
dc.contributor.authorKhadraoui, Sofiane
dc.contributor.authorSun, Ying
dc.date.accessioned2016-02-02T13:57:58Z
dc.date.available2016-02-02T13:57:58Z
dc.date.issued2016-02-01
dc.identifier.citationOzone Measurements Monitoring Using Data-Based Approach 2016 Process Safety and Environmental Protection
dc.identifier.issn09575820
dc.identifier.doi10.1016/j.psep.2016.01.015
dc.identifier.urihttp://hdl.handle.net/10754/595441
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.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0957582016000203
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.2016.01.015
dc.subjectAnomaly detection
dc.subjectMEWMA statistic
dc.subjectMSPC
dc.subjectPrincipal components analysis
dc.subjectOzone pollution
dc.subjectData-driven strategy
dc.titleOzone Measurements Monitoring Using Data-Based Approach
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalProcess Safety and Environmental Protection
dc.eprint.versionPost-print
dc.contributor.institutionPIMM Laboratory, UMR CNRS 800, Arts et Métiers ParisTech, Paris, France
dc.contributor.institutionUniversity of Sharjah, Department of Electrical and Computer Engineering, Sharjah, United Arab Emirates
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personHarrou, Fouzi
kaust.personSun, Ying
refterms.dateFOA2018-02-01T00:00:00Z
dc.date.published-online2016-02-01
dc.date.published-print2016-03


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