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A kernel-based approach for fault diagnosis in batch processes

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A kernel-based approach for fault diagnosis in batch processes

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dc.contributor.author Vitale, R. es_ES
dc.contributor.author de Noord, O. E. es_ES
dc.contributor.author Ferrer, Alberto es_ES
dc.date.accessioned 2016-02-11T13:31:00Z
dc.date.available 2016-02-11T13:31:00Z
dc.date.issued 2014-08
dc.identifier.issn 0886-9383
dc.identifier.uri http://hdl.handle.net/10251/60811
dc.description.abstract This article explores the potential of kernel-based techniques for discriminating on-specification and off-specification batch runs, combining kernel-partial least squares discriminant analysis and three common approaches to analyze batch data by means of bilinear models: landmark features extraction, batchwise unfolding, and variablewise unfolding. Gower s idea of pseudo-sample projection is exploited to recover the contribution of the initial variables to the final model and visualize those having the highest discriminant power. The results show that the proposed approach provides an efficient fault discrimination and enables a correct identification of the discriminant variables in the considered case studies. es_ES
dc.language Inglés es_ES
dc.publisher Wiley es_ES
dc.relation.ispartof Journal of Chemometrics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Kernel-based methods es_ES
dc.subject Pseudo-sample projection es_ES
dc.subject Batch processes es_ES
dc.subject Fault discrimination es_ES
dc.subject Fault diagnosis es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title A kernel-based approach for fault diagnosis in batch processes es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/cem.2629
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Vitale, R.; De Noord, OE.; Ferrer, A. (2014). A kernel-based approach for fault diagnosis in batch processes. Journal of Chemometrics. 28(8):697-707. doi:10.1002/cem.2629 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1002/cem.2629 es_ES
dc.description.upvformatpinicio 697 es_ES
dc.description.upvformatpfin 707 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 28 es_ES
dc.description.issue 8 es_ES
dc.relation.senia 269305 es_ES
dc.identifier.eissn 1099-128X
dc.description.references Cao, D.-S., Liang, Y.-Z., Xu, Q.-S., Hu, Q.-N., Zhang, L.-X., & Fu, G.-H. (2011). Exploring nonlinear relationships in chemical data using kernel-based methods. Chemometrics and Intelligent Laboratory Systems, 107(1), 106-115. doi:10.1016/j.chemolab.2011.02.004 es_ES
dc.description.references Walczak, B., & Massart, D. L. (1996). The Radial Basis Functions — Partial Least Squares approach as a flexible non-linear regression technique. Analytica Chimica Acta, 331(3), 177-185. doi:10.1016/0003-2670(96)00202-4 es_ES
dc.description.references Walczak, B., & Massart, D. L. (1996). Application of Radial Basis Functions — Partial Least Squares to non-linear pattern recognition problems: diagnosis of process faults. Analytica Chimica Acta, 331(3), 187-193. doi:10.1016/0003-2670(96)00206-1 es_ES
dc.description.references Gasteiger, J., & Zupan, J. (1993). Neural Networks in Chemistry. Angewandte Chemie International Edition in English, 32(4), 503-527. doi:10.1002/anie.199305031 es_ES
dc.description.references Li, H., Liang, Y., & Xu, Q. (2009). Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, 95(2), 188-198. doi:10.1016/j.chemolab.2008.10.007 es_ES
dc.description.references Williams, P. (2009). Influence of Water on Prediction of Composition and Quality Factors: The Aquaphotomics of Low Moisture Agricultural Materials. Journal of Near Infrared Spectroscopy, 17(6), 315-328. doi:10.1255/jnirs.862 es_ES
dc.description.references Tan, C., & Li, M. (2008). Mutual information-induced interval selection combined with kernel partial least squares for near-infrared spectral calibration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 71(4), 1266-1273. doi:10.1016/j.saa.2008.03.033 es_ES
dc.description.references Embrechts, M. J., & Ekins, S. (2006). Classification of Metabolites with Kernel-Partial Least Squares (K-PLS). Drug Metabolism and Disposition, 35(3), 325-327. doi:10.1124/dmd.106.013185 es_ES
dc.description.references Arenas-Garcia, J., & Camps-Valls, G. (2008). Efficient Kernel Orthonormalized PLS for Remote Sensing Applications. IEEE Transactions on Geoscience and Remote Sensing, 46(10), 2872-2881. doi:10.1109/tgrs.2008.918765 es_ES
dc.description.references Sun, R., & Tsung, F. (2003). A kernel-distance-based multivariate control chart using support vector methods. International Journal of Production Research, 41(13), 2975-2989. doi:10.1080/1352816031000075224 es_ES
dc.description.references Lee, J.-M., Yoo, C., Choi, S. W., Vanrolleghem, P. A., & Lee, I.-B. (2004). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59(1), 223-234. doi:10.1016/j.ces.2003.09.012 es_ES
dc.description.references Kewley, R. H., Embrechts, M. J., & Breneman, C. (2000). Data strip mining for the virtual design of pharmaceuticals with neural networks. IEEE Transactions on Neural Networks, 11(3), 668-679. doi:10.1109/72.846738 es_ES
dc.description.references Üstün, B., Melssen, W. J., & Buydens, L. M. C. (2007). Visualisation and interpretation of Support Vector Regression models. Analytica Chimica Acta, 595(1-2), 299-309. doi:10.1016/j.aca.2007.03.023 es_ES
dc.description.references Krooshof, P. W. T., Üstün, B., Postma, G. J., & Buydens, L. M. C. (2010). Visualization and Recovery of the (Bio)chemical Interesting Variables in Data Analysis with Support Vector Machine Classification. Analytical Chemistry, 82(16), 7000-7007. doi:10.1021/ac101338y es_ES
dc.description.references GOWER, J. C., & HARDING, S. A. (1988). Nonlinear biplots. Biometrika, 75(3), 445-455. doi:10.1093/biomet/75.3.445 es_ES
dc.description.references Postma, G. J., Krooshof, P. W. T., & Buydens, L. M. C. (2011). Opening the kernel of kernel partial least squares and support vector machines. Analytica Chimica Acta, 705(1-2), 123-134. doi:10.1016/j.aca.2011.04.025 es_ES
dc.description.references Smolinska, A., Blanchet, L., Coulier, L., Ampt, K. A. M., Luider, T., Hintzen, R. Q., … Buydens, L. M. C. (2012). Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis. PLoS ONE, 7(6), e38163. doi:10.1371/journal.pone.0038163 es_ES
dc.description.references Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22(5), 299-308. doi:10.1002/cem.1113 es_ES
dc.description.references Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3 es_ES
dc.description.references Nomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888 es_ES
dc.description.references García-Muñoz, S., Kourti, T., MacGregor, J. F., Mateos, A. G., & Murphy, G. (2003). Troubleshooting of an Industrial Batch Process Using Multivariate Methods. Industrial & Engineering Chemistry Research, 42(15), 3592-3601. doi:10.1021/ie0300023 es_ES
dc.description.references Pérez, N. F., Ferré, J., & Boqué, R. (2009). Calculation of the reliability of classification in discriminant partial least-squares binary classification. Chemometrics and Intelligent Laboratory Systems, 95(2), 122-128. doi:10.1016/j.chemolab.2008.09.005 es_ES
dc.description.references Lindgren, F., Hansen, B., Karcher, W., Sjöström, M., & Eriksson, L. (1996). Model validation by permutation tests: Applications to variable selection. Journal of Chemometrics, 10(5-6), 521-532. doi:10.1002/(sici)1099-128x(199609)10:5/6<521::aid-cem448>3.0.co;2-j es_ES
dc.description.references Quintás, G., Portillo, N., García-Cañaveras, J. C., Castell, J. V., Ferrer, A., & Lahoz, A. (2011). Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool. Metabolomics, 8(1), 86-98. doi:10.1007/s11306-011-0292-5 es_ES
dc.description.references Courrieu, P. (2002). Straight monotonic embedding of data sets in Euclidean spaces. Neural Networks, 15(10), 1185-1196. doi:10.1016/s0893-6080(02)00091-6 es_ES


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