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dc.contributor.authorNikzad-Langerodi, R.
dc.contributor.authorLughofer, E.
dc.contributor.authorCernuda, C.
dc.contributor.authorReischer, T.
dc.contributor.authorKantner, W.
dc.contributor.authorPawliczek, M.
dc.contributor.authorBrandstetter, M.
dc.date2018-11-08en_US
dc.date.accessioned2018-11-28T14:28:14Z
dc.date.available2018-11-28T14:28:14Z
dc.date.issued2018
dc.identifier.issn0003-2670
dc.identifier.urihttp://hdl.handle.net/20.500.11824/891
dc.description.abstractThe physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect hanges in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling’s $T^2$ and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling’s $T^2$ and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectensembled PLS, drift detection, committee disagreement, active learning, calibration model adaptation, weighted learning, Melamine formaldehyde resinen_US
dc.titleCalibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptationen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.aca.2018.02.003
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0003267018301752en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDES/1PE/TIN2017-82626-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleAnalytica Chimica Actaen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España