Evaluation of Flange Grease on Revenue Service Tracks Using Laser-Based Systems and Machine Learning

dc.contributor.authorRahalkar, Adityaen
dc.contributor.authorMirzaei, S. Mortezaen
dc.contributor.authorChen, Yangen
dc.contributor.authorHolton, Carvelen
dc.contributor.authorAhmadian, Mehdien
dc.date.accessioned2025-04-28T17:19:39Zen
dc.date.available2025-04-28T17:19:39Zen
dc.date.issued2025-03-31en
dc.date.updated2025-04-25T13:46:12Zen
dc.description.abstractThis study presents a machine learning approach for estimating the presence and extent of flange-face lubrication on a rail. It offers an alternative to the current empirical and subjective methods for lubrication assessment, in which track engineers’ periodic visual inspections are used to evaluate the condition of the rail. This alternative approach uses a laser-based optical sensing system developed by the Railway Technologies Laboratory (RTL) located at Virginia Tech in Blacksburg, VA, combined with a machine learning calibration model. The optical sensing system can capture the fluorescence emitted by the grease to identify its presence, while the machine learning model classifies the extent of grease present into four thickness indices (TIs), from 0 to 3, representing heavy (3), medium (2), light (1) and low/no (0) lubrication. Both laboratory and field tests are conducted, with the results demonstrating the ability of the system to differentiate lubrication levels and measure the presence or absence of grease and TI with an accuracy of 90%.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRahalkar, A.; Mirzaei, S.M.; Chen, Y.; Holton, C.; Ahmadian, M. Evaluation of Flange Grease on Revenue Service Tracks Using Laser-Based Systems and Machine Learning. Infrastructures 2025, 10, 80.en
dc.identifier.doihttps://doi.org/10.3390/infrastructures10040080en
dc.identifier.urihttps://hdl.handle.net/10919/126253en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleEvaluation of Flange Grease on Revenue Service Tracks Using Laser-Based Systems and Machine Learningen
dc.title.serialInfrastructuresen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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