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dc.contributor.authorCariño Corrales, Jesús Adolfo
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorZurita Millán, Daniel
dc.contributor.authorMillan, Marta
dc.contributor.authorOrtega Redondo, Juan Antonio
dc.contributor.authorRomero Troncoso, Rene De Jesus
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2017-03-02T16:02:27Z
dc.date.available2017-03-02T16:02:27Z
dc.date.issued2016-10-19
dc.identifier.citationCariño , J.A., Delgado Prieto, M., Zurita, D., Millan, M., Ortega, J.A., Romero-Troncoso, R. Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis. "IEEE access", 19 Octubre 2016, vol. 4, p. 7594-7604.
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2117/101874
dc.description.abstractThis paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.
dc.format.extent11 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica::Processos de fabricació mecànica
dc.subject.lcshMachine learning
dc.subject.otherCondition Monitoring
dc.subject.otherFault Detection
dc.subject.otherMachine Learning
dc.subject.otherNovelty Detection.
dc.titleEnhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis
dc.typeArticle
dc.subject.lemacMaquinària
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMàquines, Teoria de
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ACCESS.2016.2619382
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7600383/
dc.rights.accessOpen Access
local.identifier.drac19727632
dc.description.versionPostprint (published version)
local.citation.authorCariño, J.A.; Delgado Prieto, M.; Zurita, D.; Millan, M.; Ortega, J.A.; Romero-Troncoso, R.J.
local.citation.publicationNameIEEE access
local.citation.volume4
local.citation.startingPage7594
local.citation.endingPage7604


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