Abstract Technology associated with acoustic data capture has advanced significantly, with commercially available Sound Level Meters allowing engineers and consultants to capture large amounts of multi-channel data relating to train noise. Whilst this extended dataset can provide vital information, manually scrutinizing large amounts of data to isolate individual train pass-bys can be time consuming and problematic. This paper investigates the implementation of automated, remote (un-manned) systems that can be installed on-site, allowing train pass-by noise levels to be recorded with minimal user guidance. The efficacy of acoustic and ground vibration sensors to accurately identify train noise levels and train direction is investigated.


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    Titel :

    Identifying Noise Levels of Individual Rail Pass by Events


    Beteiligte:
    Ottley, Matthew (Autor:in) / Stoker, Alex (Autor:in) / Dobson, Stephen (Autor:in) / Lynar, Nicholas (Autor:in)


    Erscheinungsdatum :

    2018-01-01


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Identifying Noise Levels of Individual Rail Pass by Events

    Ottley, Matthew / Stoker, Alex / Dobson, Stephen et al. | British Library Conference Proceedings | 2018





    Localizing Noise Sources on a Rail Vehicle during Pass-By

    Gomes, J. / Hald, J. / Ginn, B. | Springer Verlag | 2015