Automatic Detection of Track Length Defects

Kanis Juaraj () and Zitrický Vladislav ()
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Kanis Juaraj: SMARTRONIC s.r.o., Černyševského 10, 851 01 Bratislava, Slovenská republika
Zitrický Vladislav: Univerzity of Zilina, Faculty of Operation and Economics of Transport and Communications, Univerzitná 8215/1, 010 26 Žilina, Slovenská republika

LOGI – Scientific Journal on Transport and Logistics, 2022, vol. 13, issue 1, 13-24

Abstract: Ensuring the safety of railway transport operation requires constant monitoring of the technical condition of individual elements of railway infrastructure. The necessary activities that contribute to maintaining good operational condition of the railway transport line also include the diagnostics of track length. Diagnostics of railway tracks is most often performed by means of regular visual inspection (in the conditions of the infrastructure manager – ŽSR). The objective of the article is to provide information on the application of a new approach to diagnostics of the technical condition of railway infrastructure. The new approach to defect identification on railway infrastructure uses non-invasive diagnostic methods based on the latest knowledge in the field of information and communication technologies. These facts resulted in investigating the possibilities of automatic detection of the technical condition of the track length using neural networks. The article is part of the following scientific research task: ‘Research into new knowledge and observational experience of a new generation of diagnostic systems in industrial production and transport industry – research into the physical nature of an automated track length video inspection system’, supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic.

Keywords: Railway transport; diagnostics; track length; neural networks (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:logitl:v:13:y:2022:i:1:p:13-24:n:2

DOI: 10.2478/logi-2022-0002

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