Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring

Handle URI:
http://hdl.handle.net/10754/595322
Title:
Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Khadraoui, Sofiane
Abstract:
Accurate 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring 2016 Journal of Loss Prevention in the Process Industries
Publisher:
Elsevier BV
Journal:
Journal of Loss Prevention in the Process Industries
Issue Date:
29-Jan-2016
DOI:
10.1016/j.jlp.2016.01.024
Type:
Article
ISSN:
09504230
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0950423016300225
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.authorSun, Yingen
dc.contributor.authorKhadraoui, Sofianeen
dc.date.accessioned2016-01-31T14:52:06Zen
dc.date.available2016-01-31T14:52:06Zen
dc.date.issued2016-01-29en
dc.identifier.citationAmalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring 2016 Journal of Loss Prevention in the Process Industriesen
dc.identifier.issn09504230en
dc.identifier.doi10.1016/j.jlp.2016.01.024en
dc.identifier.urihttp://hdl.handle.net/10754/595322en
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.en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0950423016300225en
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.2016.01.024en
dc.subjectPrincipal component analysisen
dc.subjectAnomaly detectionen
dc.subjectData-based approachen
dc.subjectControl chartsen
dc.titleAmalgamation of Anomaly-Detection Indices for Enhanced Process Monitoringen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Loss Prevention in the Process Industriesen
dc.eprint.versionPost-printen
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
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.