Fault diagnosis using the incremental learning algorithm with support vector machine

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/84295

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.contributor	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor	Universitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering
dc.contributor	Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor.author	Ardakani, Mohammad Hamed
dc.contributor.author	Graells Sobré, Moisès
dc.contributor.author	Escudero Bakx, Gerard
dc.date	2015
dc.identifier.citation	Ardakani, M., Graells, M., Escudero, G. Fault diagnosis using the incremental learning algorithm with support vector machine. A: European Congress of Chemical Engineering. "ECCE 10+ECAB 3+EPIC 5: 10th European Congress of Chemical Engineering: Nice, France: proceedings book". Niza: 2015.
dc.identifier.uri	http://hdl.handle.net/2117/84295
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Enginyeria química
dc.subject	Fault location (Engineering)
dc.subject	Chemical process control
dc.subject	Chemical reactor
dc.subject	Energy activation
dc.subject	Identification
dc.subject	Process system engineering
dc.subject	Errors de sistemes (Enginyeria) -- Localització
dc.subject	Control de processos químics
dc.title	Fault diagnosis using the incremental learning algorithm with support vector machine
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	To prevent process interruption and eventual losses, the need for a reliable fault detection and diagnosis system (FDD) is completely acknowledged. Besides the capability to recognize known faults automatically, a further requirement for a FDD is adaptability. If th e model cannot be adapted to deal with changes, variations due to external changes, decaying performance, Poisoning of catalyst etc. the FDD system could perform misleadingly. This paper presents an advantageous of incremental learning algorithm for fault d iagnosis, when a support vector machine algorithm are implemented as a classifier. The method which is followed in order to use the incremental learning algorithm is based on hyperplane - distance (HD) [1] . In the continues reactor which is studied, two cases are compared in order to clarify the role and importance of incremental learning algorithm. Result show t he effectiveness of this method

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