Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

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dc.contributor.author Gómez García, María Jesús
dc.contributor.author Castejón Sisamón, Cristina
dc.contributor.author Corral Abad, Eduardo
dc.contributor.author García Prada, Juan Carlos
dc.date.accessioned 2021-07-01T09:19:31Z
dc.date.available 2021-07-01T09:19:31Z
dc.date.issued 2020-06-02
dc.identifier.bibliographicCitation Gómez, M. J., Castejón, C., Corral, E. & García-Prada, J. C. (2020). Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines. Sensors, 20(12), 3575.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/32969
dc.description.abstract Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.
dc.description.sponsorship This research was funded by the Spanish Government through the project MAQSTATUS with grantnumber DPI2015-69325-C2-1-R.
dc.format.extent 18
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2020 by the authors.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Bogie testing
dc.subject.other Condition monitoring
dc.subject.other Railway axles
dc.subject.other Support vector machines
dc.subject.other Wavelet packet transform
dc.title Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines
dc.type article
dc.subject.eciencia Ingeniería Mecánica
dc.identifier.doi https://doi.org/10.3390/s20123575
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. DPI2015-69325-C2-1-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 12
dc.identifier.publicationlastpage 18
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 20
dc.identifier.uxxi AR/0000026715
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Ingeniería Mecánica
dc.affiliation.grupoinv UC3M. Grupo de Investigación: MAQLAB: Laboratorio de Máquinas
dc.affiliation.area UC3M. Área de Ingeniería Mecánica
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