A fractional Fourier transform-based method to detect impacts between the bogie and the car body of a railway vehicle: A data-driven approach

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dc.citation.journalTitlePROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT
dc.contributor.authorGutierrez-Carvajal, R. E.
dc.contributor.authorBetancur, German R.
dc.contributor.authorCastaneda, Leonel F.
dc.contributor.authorZajac, G.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.contributor.researchgroupEstudios en Mantenimiento (GEMI)spa
dc.date.accessioned2021-04-12T19:12:48Z
dc.date.available2021-04-12T19:12:48Z
dc.date.issued2018-01-01
dc.description.abstractStructural railway transport elements are typically designed to work for at least 30 years without undergoing major maintenance. However, real-life operational conditions present behaviors different to the model predicted during the initial design phase, which affects the lifetime of the elements in question. This is the case of first-generation railway vehicles which operates in the city of Medellín, Colombia, as the bolster beam presented cracks after 12 years of operation, possibly due to undesired impacts between the bogie and the pivot of the bolster beam. Monitoring vibrational signals would give some sort of an insight into impact phenomena; however, herein lies the problem, as they are difficult to identify using only vibration signals, occurring during time events that take place in a speed-varying system. In this article, the authors present a technique that automatically detects impacts using multiple in-between time/frequency representations, ranking them according to their capacity to discriminate between impact events. Our results show that the best representation for this data was the Fractional Cepstrum Transform at order 0.5 (auROC = 0.961), which outperformed the best pure domain descriptor by least 4%. © 2016, © IMechE 2016.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=7842
dc.identifier.doi10.1177/0954409716675187
dc.identifier.issn9544097
dc.identifier.issn2041301
dc.identifier.otherWOS;000419833100021
dc.identifier.otherSCOPUS;2-s2.0-85040344939
dc.identifier.urihttp://hdl.handle.net/10784/28340
dc.language.isoeng
dc.publisherSAGE PUBLICATIONS LTD
dc.relationDOI;10.1177/0954409716675187
dc.relationWOS;000419833100021
dc.relationSCOPUS;2-s2.0-85040344939
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85040344939&doi=10.1177%2f0954409716675187&partnerID=40&md5=16a654aa753cd2c8195c597c7fc3d16a
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/0954-4097
dc.sourcePROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT
dc.subjectBogies (railroad rolling stock)eng
dc.subjectLearning systemseng
dc.subjectLocomotiveseng
dc.subjectRailroad rolling stockeng
dc.subjectRailroadseng
dc.subjectTransportationeng
dc.subjectVehicleseng
dc.subjectFractional Fourier transformseng
dc.subjectRailway vehicleseng
dc.subjectResearch and developmenteng
dc.subjectsuperstructureeng
dc.subjectVehicle structureseng
dc.subjectvibrationeng
dc.subjectVibrations (mechanical)eng
dc.titleA fractional Fourier transform-based method to detect impacts between the bogie and the car body of a railway vehicle: A data-driven approacheng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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