Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods

dc.contributor.authorSysyn, Mykola P.en
dc.contributor.authorGerber, Ulfen
dc.contributor.authorNabochenko, Olga S.en
dc.contributor.authorGruen, Dmitrien
dc.contributor.authorKluge, Franziskaen
dc.date.accessioned2019-12-19T12:12:58Z
dc.date.available2019-12-19T12:12:58Z
dc.date.issued2019
dc.descriptionM. Sysyn: ORCID 0000-0001-6893-0018; O. Nabochenko: ORCID: 0000-0001-6048-2556
dc.description.abstractEN: Abstract In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to preprocess the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.en
dc.identifierDOI: 10.1007/s40864-019-0105-0
dc.identifier.citationPrediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods / Sysyn M., Gerber U., Nabochenko O. [et al.] // Urban Rail Transit. – 2019. – Vol. 5, iss. 2. – P. 123–132. – DOI: 10.1007/s40864-019-0105-0.en
dc.identifier.urihttp://eadnurt.diit.edu.ua/jspui/handle/123456789/11678
dc.language.isoen
dc.publisherSpringer Verlag, Germanyen
dc.subjectrailway turnouten
dc.subjectcommon crossingen
dc.subjectimage processingen
dc.subjectrolling contact fatigueen
dc.subjectmachine learningen
dc.subjectfeature detection and selectionen
dc.subjectКРС (ЛФ)uk_UA
dc.titlePrediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methodsen
dc.typeArticleen
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