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Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study

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Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study

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Vidal-Puig, S.; Vitale, R.; Ferrer, A. (2019). Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study. Chemometrics and Intelligent Laboratory Systems. 187:41-52. https://doi.org/10.1016/j.chemolab.2019.02.006

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/138476

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Title: Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study
Author: Vidal-Puig, Santiago Vitale, Raffaele Ferrer, Alberto
UPV Unit: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Abstract:
[EN] A comparison among widely used multivariate latent variable-based techniques for supervised process fault diagnosis was carried out. In order to assess their overall performance several diagnosis criteria were proposed ...[+]
Subjects: Supervised process fault diagnosis , Fault reconstruction , Fault signature , Partial Least Squares Discriminant Analysis (PLS-DA) , Sensitivity , Specificity
Copyrigths: Reserva de todos los derechos
Source:
Chemometrics and Intelligent Laboratory Systems. (issn: 0169-7439 )
DOI: 10.1016/j.chemolab.2019.02.006
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.chemolab.2019.02.006
Project ID:
AEI/DPI2017-82896-C2-1-R-AR
Thanks:
This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2017-82896-C2-1-R and Shell Global Solutions International B.V. (Amsterdam, The Netherlands).
Type: Artículo

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