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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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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cited_by cdi_FETCH-LOGICAL-c474t-560611268237f739045ffda7c83370e04081d72399d7af78c68db540e2e456723
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container_issue 2020
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creator Vukušič, Ivan
Apeltauer, Jiří
Podroužek, Jan
Kratochvílová, Martina
Plášek, Otto
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.
doi_str_mv 10.1155/2020/8849734
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2042-3195
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subjects Condition monitoring
Kurtosis
Learning algorithms
Machine learning
Railroad crossings
Railroad track switches
Railroads
Sensors
Skewness
Standard deviation
Transportation
Velocity
Vibration
Vibration monitoring
title Train Type Identification at S&C
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