Optimal selection of the mother wavelet in WPT analysis and its influence in cracked railway axles detection

dc.affiliation.areaUC3M. Área de Ingeniería Mecánicaes
dc.affiliation.dptoUC3M. Departamento de Ingeniería Mecánicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: MAQLAB: Laboratorio de Máquinases
dc.contributor.authorZamorano Garzón, Marta
dc.contributor.authorGómez García, María Jesús
dc.contributor.authorCastejón Sisamón, Cristina
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.descriptionThis article belongs to the Special Issue Railway Vehicle Maintenance 4.0.en
dc.description.abstractThe detection of cracked railway axles by processing vibratory signals measured during operation is the focus of this study. The rotodynamic theory is applied to this specific purpose but, in practice and for real systems, there is no consensus on applying the results obtained from theory. Finding reliable patterns that change during operation would have advantages over other currently applied methods, such as non-destructive testing (NDT) techniques, because data between inspections would be obtained during operation. Vibratory signal processing techniques in the time-frequency domain, such as wavelet packet transform (WPT), have proved to be reliable to obtain patterns. The aim of this work is to develop a methodology to select the optimal function associated with the WPT, the mother wavelet (MW), and to find diagnostic patterns for cracked railway axle detection. In previous related works, the Daubechies 6 MW was commonly used for all speed/load conditions and defects. In this work, it was found that the Symlet 9 MW works better, so a comparative study was carried out with both functions, and it was observed that the success rates obtained with Daubechies 6 are improved when using Symlet 9. Specifically, defects above 16.6% of the shaft diameter were reliably detected, with no false alarms. To validate the proposed methodology, experimental vibratory signals of a healthy scaled railway axle were obtained and then the same axle was tested with a transverse crack located close to a section change (where this type of defect typically appears) for nine different crack depths.en
dc.description.sponsorshipThis research was funded by the MCIN/AEI/10.13039/501100011033, grant number MC4.0 PID2020-116984RB-C22.en
dc.identifier.bibliographicCitationZamorano, M., García, M. J. G., & Castejón, C. (2023). Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection. Machines, 11(4), 493.en
dc.identifier.publicationissue4, 493
dc.relation.projectIDGobierno de España. PID2020-116984RB-C22es
dc.rights© 2023 by the authors.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherMother waveleten
dc.subject.otherWavelet packet transformen
dc.subject.otherVibration analysisen
dc.subject.otherCondition monitoringen
dc.subject.otherRailway axlesen
dc.subject.otherCrack detectionen
dc.subject.otherCrack diagnosisen
dc.titleOptimal selection of the mother wavelet in WPT analysis and its influence in cracked railway axles detectionen
dc.typeresearch article*
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