Automatic Detection of Track Length Defects
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 o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2022-01-01
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| Series: | Logi |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/logi-2022-0002 |
| _version_ | 1797208441766281216 |
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| author | Kanis Juaraj Zitrický Vladislav |
| author_facet | Kanis Juaraj Zitrický Vladislav |
| author_sort | Kanis Juaraj |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:38:52Z |
| format | Article |
| id | doaj.art-7e12176a0ae845e9a6e54c482ebbf78a |
| institution | Directory Open Access Journal |
| issn | 2336-3037 |
| language | English |
| last_indexed | 2024-04-24T09:38:52Z |
| publishDate | 2022-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Logi |
| spelling | doaj.art-7e12176a0ae845e9a6e54c482ebbf78a2024-04-15T07:43:26ZengSciendoLogi2336-30372022-01-01131132410.2478/logi-2022-0002Automatic Detection of Track Length DefectsKanis Juaraj0Zitrický Vladislav1SMARTRONIC s.r.o., Černyševského 10, 851 01 Bratislava, Slovenská republikaUniverzity of Zilina, Faculty of Operation and Economics of Transport and Communications, Univerzitná 8215/1, 010 26 Žilina, Slovenská republikaEnsuring 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.https://doi.org/10.2478/logi-2022-0002railway transportdiagnosticstrack lengthneural networks |
| spellingShingle | Kanis Juaraj Zitrický Vladislav Automatic Detection of Track Length Defects Logi railway transport diagnostics track length neural networks |
| title | Automatic Detection of Track Length Defects |
| title_full | Automatic Detection of Track Length Defects |
| title_fullStr | Automatic Detection of Track Length Defects |
| title_full_unstemmed | Automatic Detection of Track Length Defects |
| title_short | Automatic Detection of Track Length Defects |
| title_sort | automatic detection of track length defects |
| topic | railway transport diagnostics track length neural networks |
| url | https://doi.org/10.2478/logi-2022-0002 |
| work_keys_str_mv | AT kanisjuaraj automaticdetectionoftracklengthdefects AT zitrickyvladislav automaticdetectionoftracklengthdefects |