The paper presents a measurement setup capable of collecting wheel/rail contact noise and vibration signals from a passenger train. A data analysis method based on machine learning is developed for detecting events from the acquired data and classifying them according to relevant railway track components and noise phenomena. A classification rate higher than 84 % is achieved.


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    Title :

    Monitoring Rail Condition Based on Sound and Vibration Sensors Installed on an Operational Train


    Additional title:

    Notes Numerical Fluid Mech.




    Publication date :

    2015-01-01


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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