Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

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dc.contributor.authorSamuel, Raphael
dc.contributor.authorCao, Yi
dc.date.accessioned2016-07-08T11:01:16Z
dc.date.available2016-07-08T11:01:16Z
dc.date.issued2016-08-28
dc.description.abstractKernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA–KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA–KDE is more robust and provide better overall performance than KPCA with Gaussian assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed.en_UK
dc.identifier.citationSamuel, R., Cao, Y. (2016) Nonlinear process fault detection and identification using kernel PCA and kernel density estimation, Systems Science and Control Engineering, Vol. 4, Iss. 1, pp. 165-174en_UK
dc.identifier.issn2164-2583
dc.identifier.urihttp://dx.doi.org/10.1080/21642583.2016.1198940
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/10107
dc.language.isoenen_UK
dc.publisherTaylor & Francisen_UK
dc.rightsAttribution 4.0 Internationalen_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFault detection and identificationen_UK
dc.subjectProcess monitoringen_UK
dc.subjectNonlinear systemsen_UK
dc.subjectMultivariate statisticsen_UK
dc.subjectKernel density estimationen_UK
dc.titleNonlinear process fault detection and identification using kernel PCA and kernel density estimationen_UK
dc.typeArticleen_UK

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