Train Type Identification at S&C

Abstract

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.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.1155/2020/8849734 under the license https://creativecommons.org/licenses/by/4.0/

• http://downloads.hindawi.com/journals/jat/2020/8849734.pdf,

http://downloads.hindawi.com/journals/jat/2020/8849734.xml,

http://dx.doi.org/10.1155/2020/8849734

under the license https://creativecommons.org/licenses/by/4.0/