For monitoring the conditions of railway infrastructures, axle box acceleration (ABA) measurements on board of trains is used. In this paper, the focus is on the early detection of short surface defects called squats. Different classes of squats are classified based on the response in the frequency domain of the ABA signal, using the wavelet power spectrum. For the investigated Dutch tracks, the power spectrum in the frequencies between 1060–1160Hz and around 300Hz indicate existence of a squat and also provide information of whether a squat is light, moderate or severe. The detection procedure is then validated relying on real-life measurements of ABA signals from measuring trains, and data of severity and location of squats obtained via a visual inspection of the tracks. Based on the real-life tests in the Netherlands, the hit rate of the system for light squats is higher than 78%, with a false alarm rate of 15%. In the case of severe squats the hit rate was 100% and zero false alarms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Monitoring the railway infrastructure: Detection of surface defects using wavelets


    Contributors:


    Publication date :

    2013-10-01


    Size :

    1108397 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SYSTEM FOR MONITORING RAILWAY INFRASTRUCTURE

    VASILEV VLADIMIR PETROVICH / ZHEREBTSOV VLADIMIR MIKHAJLOVICH | European Patent Office | 2016

    Free access

    Application of Wavelets and Fuzzy Sets to the Detection of Head-Checking Defects in Railway Rails

    Lesiak, P. / Bojarczak, P. | British Library Conference Proceedings | 2010



    Railway infrastructure monitoring by drones

    Flammini, Francesco / Pragliola, Concetta / Smarra, Giovanni | IEEE | 2016


    MONITORING SYSTEM FOR A RAILWAY INFRASTRUCTURE

    DONADIO PASQUALE | European Patent Office | 2018

    Free access