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Artículo

A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space

Godoy, José LuisIcon ; Vega, Jorge RubenIcon ; Marchetti, Jacinto LuisIcon
Fecha de publicación: 10/2013
Editorial: Elsevier Science
Revista: Chemometrics And Intelligent Laboratory Systems
ISSN: 0169-7439
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Otras Ingenierías y Tecnologías

Resumen

A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis inmultivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLSdecomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems
Palabras clave: Pls , Process Monitoring , Fault Detection , Fault Diagnosis , Fault Isolation , Contribution Analysis
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/8877
DOI: http://dx.doi.org/10.1016/j.chemolab.2013.07.006
URL: http://www.sciencedirect.com/science/article/pii/S016974391300138X
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Articulos(INTEC)
Articulos de INST.DE DES.TECNOL.PARA LA IND.QUIMICA (I)
Citación
Godoy, José Luis; Vega, Jorge Ruben; Marchetti, Jacinto Luis; A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 128; 10-2013; 25-36
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