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Latest revision as of 12:33, 15 February 2021
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/
under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2020
Volume 2020, 2020
DOI: 10.1155/2020/8849734
Licence: Other
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