Canonical variate dissimilarity analysis for process incipient fault detection

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dc.contributor.authorSalgado Pilario, Karl Ezra
dc.contributor.authorCao, Yi
dc.date.accessioned2018-03-07T16:34:19Z
dc.date.available2018-03-07T16:34:19Z
dc.date.issued2018-02-28
dc.description.abstractEarly detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate statistical process monitoring methods are currently established for abrupt fault detection. Among these, canonical variate analysis (CVA) was proven to be effective for dynamic process monitoring. However, the traditional CVA indices may not be sensitive enough for incipient faults. In this work, an extension of CVA, called the canonical variate dissimilarity analysis (CVDA), is proposed for process incipient fault detection in nonlinear dynamic processes under varying operating conditions. To handle non-Gaussian distributed data, kernel density estimation was used for computing detection limits. A CVA dissimilarity-based index has been demonstrated to outperform traditional CVA indices and other dissimilarity-based indices, namely DISSIM, RDTCSA, and GCCA, in terms of sensitivity when tested on slowly developing multiplicative and additive faults in a CSTR under closed-loop control and varying operating conditions.en_UK
dc.identifier.citationPilario KES, Cao Y, Canonical variate dissimilarity analysis for process incipient fault detection, IEEE Transactions on Industrial Informatics, Volume 14, no. 12, 2018, pp. 5308-5315.en_UK
dc.identifier.issn1551-3203
dc.identifier.urihttp://dx.doi.org/10.1109/TII.2018.2810822
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13055
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMonitoringen_UK
dc.subjectProcess controlen_UK
dc.subjectIndexesen_UK
dc.subjectPrincipal component analysisen_UK
dc.subjectFault detectionen_UK
dc.subjectNonlinear dynamical systemsen_UK
dc.subjectCanonical variate analysisen_UK
dc.subjectKernal density estimationen_UK
dc.subjectDissimilarity analysisen_UK
dc.subjectKernalen_UK
dc.titleCanonical variate dissimilarity analysis for process incipient fault detectionen_UK
dc.typeArticleen_UK

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