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dc.contributor.authorAcho Zuppa, Leonardo
dc.contributor.authorBuenestado Caballero, Pablo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2019-09-03T08:40:25Z
dc.date.available2019-09-03T08:40:25Z
dc.date.issued2019-06-20
dc.identifier.citationAcho, L.; Buenestado, P. An experimental platform for statistical fault diagnosis in propelled mechanical systems: an academic exercise. "Cybernetics and physics", 20 Juny 2019, vol. 8, núm. 1, p. 5-11.
dc.identifier.issn2226-4116
dc.identifier.urihttp://hdl.handle.net/2117/167870
dc.description.abstractStatistical data analysis for fault diagnosis in mechanical systems is a fundamental tool, for instance, in applied mechanical engineering. In order to capture a feasible data set, a well designed electronic instrumentation and excitation system signal stages are mandatory. Hence, one objective of this paper is to develop a low cost vibration sensor based on an inductive LC-tank oscillator (a resonant inductive-capacitive electronic circuit carefully designed to produce an harmonic electrical signal), and then to tune an effective excitation system signal to our experimental platform. This platform uses a propelled drone motor mounted on a beam structure to emulate a propelled rotating machine. Essentially, two data set were acquired. One for the healthy behaviour of the developed system, and the other for a programmed faulty scenario. This defective case was realized by introducing a small mechanical fault in one blade extreme of the mechanical propelled system. To note, this faulty scenario is almost impossible to deduce by just seen the raw data. The other objective of this paper is to analyze the obtained data sets by utilizing a statistical data analysis tool. Then, by employing box-plot diagrams, the healthy and faulty cases become evidenced. Finally, and due to we are proposing a low-cost academic experimental platform for fault diagnosis based on data analysis, our platform’s toll was around 120 euros. Hence, this platform results applicable to teach data analysis from dynamical systems
dc.format.extent7 p.
dc.language.isoeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
dc.subject.lcshFault location (Engineering)
dc.subject.otherPropelled rotating machines
dc.subject.otherStatistics
dc.subject.otherFault diagnosis
dc.subject.otherElectronic instrumentation
dc.subject.otherSignal processing
dc.titleAn experimental platform for statistical fault diagnosis in propelled mechanical systems: an academic exercise
dc.typeArticle
dc.subject.lemacErrors de sistemes (Enginyeria) -- Localització
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.contributor.groupUniversitat Politècnica de Catalunya. STH - Sostenibilitat, Tecnologia i Humanisme
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://lib.physcon.ru/doc?id=dfcff44b46f8
dc.rights.accessOpen Access
drac.iddocument25256202
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/DPI2015-64170-R
upcommons.citation.authorAcho, L.; Buenestado, P.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameCybernetics and physics
upcommons.citation.volume8
upcommons.citation.number1
upcommons.citation.startingPage5
upcommons.citation.endingPage11


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