Kullback-Leibler distance-based enhanced detection of incipient anomalies

Handle URI:
http://hdl.handle.net/10754/622305
Title:
Kullback-Leibler distance-based enhanced detection of incipient anomalies
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Madakyaru, Muddu
Abstract:
Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters. © 2016 Elsevier Ltd
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Harrou F, Sun Y, Madakyaru M (2016) Kullback-Leibler distance-based enhanced detection of incipient anomalies. Journal of Loss Prevention in the Process Industries 44: 73–87. Available: http://dx.doi.org/10.1016/j.jlp.2016.08.020.
Publisher:
Elsevier BV
Journal:
Journal of Loss Prevention in the Process Industries
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
9-Sep-2016
DOI:
10.1016/j.jlp.2016.08.020
Type:
Article
ISSN:
0950-4230
Sponsors:
This publication is based upon work 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/S0950423016302273
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.authorMadakyaru, Mudduen
dc.date.accessioned2017-01-02T09:08:25Z-
dc.date.available2017-01-02T09:08:25Z-
dc.date.issued2016-09-09en
dc.identifier.citationHarrou F, Sun Y, Madakyaru M (2016) Kullback-Leibler distance-based enhanced detection of incipient anomalies. Journal of Loss Prevention in the Process Industries 44: 73–87. Available: http://dx.doi.org/10.1016/j.jlp.2016.08.020.en
dc.identifier.issn0950-4230en
dc.identifier.doi10.1016/j.jlp.2016.08.020en
dc.identifier.urihttp://hdl.handle.net/10754/622305-
dc.description.abstractAccurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters. © 2016 Elsevier Ltden
dc.description.sponsorshipThis publication is based upon work 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/S0950423016302273en
dc.subjectAnomaly detectionen
dc.subjectKullback-Leibler distanceen
dc.subjectPartial least squareen
dc.subjectStatistical process controlen
dc.titleKullback-Leibler distance-based enhanced detection of incipient anomaliesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Loss Prevention in the Process Industriesen
dc.contributor.institutionManipal Institute of Technology, Department of Chemical Engineering, Manipal University, Manipal, Indiaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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