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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Bibliographic Details
Published in:Journal of advanced transportation 2020-11, Vol.2020 (2020), p.1-12
Main Authors: Vukušič, Ivan, Apeltauer, Jiří, Podroužek, Jan, Kratochvílová, Martina, Plášek, Otto
Format: Article
Language:English
Subjects:
Condition monitoring
Kurtosis
Learning algorithms
Machine learning
Railroad crossings
Railroad track switches
Railroads
Sensors
Skewness
Standard deviation
Transportation
Velocity
Vibration
Vibration monitoring
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Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/8849734