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dc.contributor.authorArdakani, Mohammadhamed
dc.contributor.authorAskarian, Mahdieh
dc.contributor.authorShokry Abdel-aleem, Ahmed
dc.contributor.authorEscudero Bakx, Gerard
dc.contributor.authorGraells Sobré, Moisès
dc.contributor.authorEspuña Camarasa, Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.identifier.citationArdakani, M., Askarian, M., Shokry , A., Escudero, G., Graells, M., Espuña, A. Optimal features selection for designing a fault diagnosis system. A: European Symposium on Computer Aided Process Engineering. "26th Symposium on Computer Aided Process Engineering". Portorož: Elsevier, 2016, p. 1111-1116.
dc.description.abstractFault diagnosis (FD) using data-driven methods is essential for monitoring complex process systems, but its performance is severely affected by the quality of the used information. Additionally, processing huge amounts of data recorded by modern monitoring systems may be complex and time consuming if no data mining and/or preprocessing methods are employed. Thus, features selection for FD is advisable in order to determine the optimal subset of features/variables for conducting statistical analyses or building a machine-learning model. In this work, features selection are formulated as an optimization problem. Several relevancy indices, such as Maximum Relevance (MR), Value Difference Metric (VDM), and Fit Criterion (FC), and redundancy indices such as Minimum Redundancy (mR), Redundancy VDM (RVDM), and Redundancy Fit Criterion (RFC) are combined to determine the optimal subset of features. Another approach of features selection is based on the optimal performance of the classifier, which is achieved by a classifier wrapped with genetic algorithm. Efficiency of this strategy is explored considering different classifiers, namely Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN) Classifier and Gaussian Naïve Bayes (GNB). A Genetic algorithm (GA), as a Derivative Free Optimization (DFO) technique, has been used due to the robustness to deal with different kinds of problems. The optimal subset of obtained features has been tested with SVM, DT, KNN, and GNB for the Tennessee-Eastman process benchmark with 19 classes. Results show that, when the performance of the classifier is used as the objective function the wrapper method obtains the best features set.
dc.format.extent6 p.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Enginyeria química
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshAutomatic control
dc.subject.otherFault Diagnosis
dc.subject.otherFeatures Selection
dc.titleOptimal features selection for designing a fault diagnosis system
dc.typeConference report
dc.subject.lemacControl automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor.groupUniversitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (author's final draft)
local.citation.authorArdakani, M.; Askarian, M.; Shokry, A.; Escudero, G.; Graells, M.; Espuña, A.
local.citation.contributorEuropean Symposium on Computer Aided Process Engineering
local.citation.publicationName26th Symposium on Computer Aided Process Engineering

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