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Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

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Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

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dc.contributor.author Debón Aucejo, Ana María es_ES
dc.contributor.author García-Díaz, J. Carlos es_ES
dc.date.accessioned 2016-02-16T15:40:26Z
dc.date.available 2016-02-16T15:40:26Z
dc.date.issued 2012-04
dc.identifier.issn 0951-8320
dc.identifier.uri http://hdl.handle.net/10251/60940
dc.description.abstract [EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures. es_ES
dc.description.sponsorship The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Reliability Engineering and System Safety es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Generalized additive models es_ES
dc.subject ROC curve es_ES
dc.subject Bootstrapping es_ES
dc.subject Generalized linear models es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.ress.2011.12.022
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-06-08/ es_ES
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 Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.ress.2011.12.022 es_ES
dc.description.upvformatpinicio 102 es_ES
dc.description.upvformatpfin 114 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 100 es_ES
dc.relation.senia 233632 es_ES
dc.contributor.funder Universitat Politècnica de València


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