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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorMadakyaru, Muddu
dc.date.accessioned2017-07-19T10:45:00Z
dc.date.available2017-07-19T10:45:00Z
dc.date.issued2017-07-05
dc.identifier.citationHarrou F, Sun Y, Madakyaru M (2017) An Improved Wavelet-Based Multivariable Fault Detection Scheme. Uncertainty Quantification and Model Calibration. Available: http://dx.doi.org/10.5772/intechopen.68947.
dc.identifier.doi10.5772/intechopen.68947
dc.identifier.urihttp://hdl.handle.net/10754/625210
dc.description.abstractData observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)-based Q and EWMA methods and MSPLS-based Q method.
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.
dc.publisherIntechOpen
dc.relation.urlhttps://www.intechopen.com/books/uncertainty-quantification-and-model-calibration/an-improved-wavelet-based-multivariable-fault-detection-scheme
dc.rightsThis chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/3.0
dc.subjectdata uncertainty
dc.subjectmultiscale representation
dc.subjectfault detection
dc.subjectdata-driven approaches
dc.subjectstatistical monitoring schemes
dc.titleAn Improved Wavelet-Based Multivariable Fault Detection Scheme
dc.typeBook Chapter
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalUncertainty Quantification and Model Calibration
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal University, Manipal, India
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2018-06-14T05:18:32Z


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This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.