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dc.contributor.author
Rodriguez Aguilar, Leandro Pedro Faustino  
dc.contributor.author
Cedeño Viteri, Marco Vinicio  
dc.contributor.author
Sanchez, Mabel Cristina  
dc.date.available
2017-10-23T18:53:50Z  
dc.date.issued
2016-02-11  
dc.identifier.citation
Rodriguez Aguilar, Leandro Pedro Faustino; Cedeño Viteri, Marco Vinicio; Sanchez, Mabel Cristina; Optimal sensor network upgrade for fault detection using principal component analysis; American Chemical Society; Industrial & Engineering Chemical Research; 55; 8; 11-2-2016; 2359-2370  
dc.identifier.issn
0888-5885  
dc.identifier.uri
http://hdl.handle.net/11336/26927  
dc.description.abstract
The efficiency of a fault monitoring system critically depends on the structure of the plant instrumentation system. For processes monitored using principal component analysis, the multivariate statistical technique most used for fault diagnosis in industry, an existing strategy aims at selecting the set of instruments that satisfies the detection of a given set of faults at minimum cost. It is based on the minimum fault magnitude concept. Because that procedure discards lower-cost feasible solutions, in this work, a new optimal selection methodology is proposed whose constraints are straightaway defined in terms of the principal component analysis’s statistics. To solve the optimization problem, a level traversal search with cutting criteria is enhanced taking into account that the fault observability is a necessary condition for fault detection when statistical monitoring techniques are applied. Furthermore, observability and detection degree concepts are defined and considered as constraints of the optimization problems to devise robust sensor structures, which are able to detect a set of key faults under the presence of failed sensors or outliers. Application results of the new strategy to a case study taken from the literature are provided.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
American Chemical Society  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Fault Diagnosis  
dc.subject
Multivariate Statistical Process Control  
dc.subject.classification
Otras Ingeniería Química  
dc.subject.classification
Ingeniería Química  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Optimal sensor network upgrade for fault detection using principal component analysis  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2017-10-09T15:33:58Z  
dc.journal.volume
55  
dc.journal.number
8  
dc.journal.pagination
2359-2370  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Washington D. C.  
dc.description.fil
Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina  
dc.description.fil
Fil: Cedeño Viteri, Marco Vinicio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina  
dc.description.fil
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina  
dc.journal.title
Industrial & Engineering Chemical Research  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/acs.iecr.5b02599  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acs.iecr.5b02599