dc.creatorGodoy, José Luis
dc.creatorZumoffen, David Alejandro Ramon
dc.creatorVega, Jorge Ruben
dc.creatorMarchetti, Jacinto Luis
dc.date.accessioned2018-02-27T18:50:03Z
dc.date.accessioned2018-11-06T14:03:54Z
dc.date.available2018-02-27T18:50:03Z
dc.date.available2018-11-06T14:03:54Z
dc.date.created2018-02-27T18:50:03Z
dc.date.issued2014-07
dc.identifierGodoy, José Luis; Zumoffen, David Alejandro Ramon; Vega, Jorge Ruben; Marchetti, Jacinto Luis; New contributions to non-linear process monitoring through kernel partial least squares; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 135; 7-2014; 76-89
dc.identifier0169-7439
dc.identifierhttp://hdl.handle.net/11336/37300
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1882459
dc.description.abstractThe kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to non-linear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a non-linear process. The effectiveness of the proposed methods is confirmed by using simulation examples.
dc.languageeng
dc.publisherElsevier Science
dc.relationinfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743914000707
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.chemolab.2014.04.001
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectFAULT DETECTION
dc.subjectFAULT DIAGNOSIS
dc.subjectKPLS MODELING
dc.subjectNON-LINEAR PROCESSES
dc.subjectPREDICTION RISK ASSESSMENT
dc.titleNew contributions to non-linear process monitoring through kernel partial least squares
dc.typeArtículos de revistas
dc.typeArtículos de revistas
dc.typeArtículos de revistas


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