Train Type Identification at S&C
The presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicl...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8849734 |
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| _version_ | 1850109672573370368 |
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| author | Martina Kratochvílová Jan Podroužek Jiří Apeltauer Ivan Vukušič Otto Plášek |
| author_facet | Martina Kratochvílová Jan Podroužek Jiří Apeltauer Ivan Vukušič Otto Plášek |
| author_sort | Martina Kratochvílová |
| collection | DOAJ |
| description | The presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicle track interaction, the machine learning was chosen as a suitable tool. For design and validation of the system, real on-site acceleration data were used. The resulting theoretical and practical challenges are discussed. |
| format | Article |
| id | doaj-art-485022a7d48b49a698bf9d5fbf34c5be |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-485022a7d48b49a698bf9d5fbf34c5be2025-08-20T02:38:01ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88497348849734Train Type Identification at S&CMartina Kratochvílová0Jan Podroužek1Jiří Apeltauer2Ivan Vukušič3Otto Plášek4Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, Veveří 331/95, 602 00 Brno, Czech RepublicInstitute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, Veveří 331/95, 602 00 Brno, Czech RepublicInstitute of Road Structures, Faculty of Civil Engineering, Brno University of Technology, Brno, Czech RepublicInstitute of Railway Structures and Constructions, Faculty of Civil Engineering, Brno University of Technology, Brno, Czech RepublicInstitute of Railway Structures and Constructions, Faculty of Civil Engineering, Brno University of Technology, Brno, Czech RepublicThe presented paper concerns the development of condition monitoring system for railroad switches and crossings that utilizes vibration data. Successful utilization of such system requires a robust and efficient train type identification. Given the complex and unique dynamical response of any vehicle track interaction, the machine learning was chosen as a suitable tool. For design and validation of the system, real on-site acceleration data were used. The resulting theoretical and practical challenges are discussed.http://dx.doi.org/10.1155/2020/8849734 |
| spellingShingle | Martina Kratochvílová Jan Podroužek Jiří Apeltauer Ivan Vukušič Otto Plášek Train Type Identification at S&C Journal of Advanced Transportation |
| title | Train Type Identification at S&C |
| title_full | Train Type Identification at S&C |
| title_fullStr | Train Type Identification at S&C |
| title_full_unstemmed | Train Type Identification at S&C |
| title_short | Train Type Identification at S&C |
| title_sort | train type identification at s c |
| url | http://dx.doi.org/10.1155/2020/8849734 |
| work_keys_str_mv | AT martinakratochvilova traintypeidentificationatsc AT janpodrouzek traintypeidentificationatsc AT jiriapeltauer traintypeidentificationatsc AT ivanvukusic traintypeidentificationatsc AT ottoplasek traintypeidentificationatsc |