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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorKhadraoui, Sofiane
dc.date.accessioned2016-01-31T14:52:06Z
dc.date.available2016-01-31T14:52:06Z
dc.date.issued2016-01-29
dc.identifier.citationAmalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring 2016 Journal of Loss Prevention in the Process Industries
dc.identifier.issn09504230
dc.identifier.doi10.1016/j.jlp.2016.01.024
dc.identifier.urihttp://hdl.handle.net/10754/595322
dc.description.abstractAccurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been studied intensively and largely applied to multivariate processes with highly cross-correlated process variables; howver conventional PCA-based methods often fail to detect small or moderate anomalies. In this paper, the proposed approach integrates two popular process-monitoring detection tools, the conventional PCA-based monitoring indices Hotelling’s T2 and Q and the exponentially weighted moving average (EWMA). We develop two EWMA tools based on the Q and T2 statistics, T2-EWMA and Q-EWMA, to detect anomalies in the process mean. The performances of the proposed methods were compared with that of conventional PCA-based anomaly-detection methods by applying each method to two examples: a synthetic data set and experimental data collected from a flow heating system. The results clearly show the benefits and effectiveness of the proposed methods over conventional PCA-based methods.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0950423016300225
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Loss Prevention in the Process Industries. 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 Journal of Loss Prevention in the Process Industries, 29 January 2016. DOI: 10.1016/j.jlp.2016.01.024
dc.subjectPrincipal component analysis
dc.subjectAnomaly detection
dc.subjectData-based approach
dc.subjectControl charts
dc.titleAmalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of Loss Prevention in the Process Industries
dc.eprint.versionPost-print
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.dateFOA2018-01-29T00:00:00Z
dc.date.published-online2016-01-29
dc.date.published-print2016-03


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