Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles

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.authorSánchez, René Vinicio
dc.contributor.authorLucero, Pablo
dc.contributor.authorMacancela, Jean Carlo
dc.contributor.authorRubio Alonso, Higinio
dc.contributor.authorCerrada, Mariela
dc.contributor.authorCabrera, Diego
dc.contributor.authorCastejón Sisamón, Cristina
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.description.abstractRailway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features by applying the Mean Decrease Accuracy method of Random Forest. Finally, we test the best indicators with a K-Nearest Neighbor classifier. For the data collection, a real bogie test bench has been used to simulate crack faults on the railway axles, and vibration signals from both the left and right sides of the axle were measured. The results not only show the performance of condition indicators in different domains, but also show that the fusion of condition indicators works well together to detect a crack fault in railway axles.en
dc.description.sponsorshipAuthors would like to thank the support provided by the Spanish Government, through the MAQ-STATUS DPI2015-69325-C2-1-R project, and Universidad Politécnica Salesiana through the research group GIDTEC.en
dc.identifier.bibliographicCitationSánchez, R.-V., Lucero, P., Macancela, J.-C., Rubio Alonso, H., Cerrada, M., Cabrera, D., & Castejón, C. (2020). Evaluation of time and frequency condition indicators from vibration signals for crack detection in railway axles. Applied Sciences (Basel, Switzerland), 10(12), 4367.en
dc.identifier.publicationtitleApplied Sciences (MDPI)en
dc.relation.projectIDGobierno de España. DPI2015-69325-C2-1-Res
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( / 4.0/).en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherRailway axlesen
dc.subject.otherCrack detectionen
dc.subject.otherCondition monitoringen
dc.subject.otherTime-domain featuresen
dc.subject.otherFrequency-domain featuresen
dc.subject.otherRandom forest classifieren
dc.subject.otherFeature extractionen
dc.subject.otherFeature selectionen
dc.titleEvaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axlesen
dc.typeresearch article*
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