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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorMadakyaru, Muddu
dc.contributor.authorSun, Ying
dc.contributor.authorKhadraoui, Sofiane
dc.date.accessioned2016-11-03T08:30:45Z
dc.date.available2016-11-03T08:30:45Z
dc.date.issued2016-08-11
dc.identifier.citationHarrou F, Madakyaru M, Sun Y, Khadraoui S (2016) Improved detection of incipient anomalies via multivariate memory monitoring charts: Application to an air flow heating system. Applied Thermal Engineering 109: 65–74. Available: http://dx.doi.org/10.1016/j.applthermaleng.2016.08.047.
dc.identifier.issn1359-4311
dc.identifier.doi10.1016/j.applthermaleng.2016.08.047
dc.identifier.urihttp://hdl.handle.net/10754/621495
dc.description.abstractDetecting anomalies is important for reliable operation of several engineering systems. Multivariate statistical monitoring charts are an efficient tool for checking the quality of a process by identifying abnormalities. Principal component analysis (PCA) was shown effective in monitoring processes with highly correlated data. Traditional PCA-based methods, nevertheless, often are relatively inefficient at detecting incipient anomalies. Here, we propose a statistical approach that exploits the advantages of PCA and those of multivariate memory monitoring schemes, like the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes to better detect incipient anomalies. Memory monitoring charts are sensitive to incipient anomalies in process mean, which significantly improve the performance of PCA method and enlarge its profitability, and to utilize these improvements in various applications. The performance of PCA-based MEWMA and MCUSUM control techniques are demonstrated and compared with traditional PCA-based monitoring methods. Using practical data gathered from a heating air-flow system, we demonstrate the greater sensitivity and efficiency of the developed method over the traditional PCA-based methods. Results indicate that the proposed techniques have potential for detecting incipient anomalies in multivariate data. © 2016 Elsevier Ltd
dc.description.sponsorshipKing Abdullah University of Science and Technology
dc.description.sponsorshipOffice of Sponsored Research[OSR-2015-CRG4-2582]
dc.publisherElsevier BV
dc.subjectAnomaly detection
dc.subjectIncipient anomaly
dc.subjectMemory monitoring charts
dc.subjectMultivariate control chart
dc.titleImproved detection of incipient anomalies via multivariate memory monitoring charts: Application to an air flow heating system
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalApplied Thermal Engineering
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal University, Manipal, India
dc.contributor.institutionUniversity of Sharjah, Department of Electrical and Computer Engineering, Sharjah, United Arab Emirates
kaust.personHarrou, Fouzi
kaust.personSun, Ying


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