Wheel polygon is one of the most common faults in railway industry threatening health of bogie components. Efforts have been made for years, to explore reason-solution and fault detection techniques for wheel ploygon. In China, 6A systems are widely applied for monitoring locomotive running safety. Among all 6A systems, an advanced truck defect diagnosis and record system (ATDR) focuses on fault detections of bogie components, including bearings, gears, and wheel trends based on analyses of sensor-collected bogie impacts. This paper tends to explore a wheel-polygon detection method based on sensor network of ATDR systems. A vehicle dynamic model was developed for a certain typical locomotive in China. Various typical wheel-polygon conditions were considered during simulations. Virtual sensors were placed on each axles and hugging-bearing box body (HBBB) for acquiring vehicle responses (acceleration) during simulations. Besides, impact features of axle-boxes and HBBBs were analysed via applying virtual demodulated resonance technique. Both time-domain and frequency-domain analyses were based between the normal conditions and wheel-polygon conditions. Simulations results were compared with field data. Finally, a wheel-polygon detection method was proposed.


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    Titel :

    Wheel Polygon Detection Based on Vibration-Impact Analyses of Bogie Components


    Beteiligte:
    Li, Chunsheng (Autor:in)

    Kongress:

    Second International Conference on Rail Transportation ; 2021 ; Chengdu, Sichuan Province, China


    Erschienen in:

    ICRT 2021 ; 267-275


    Erscheinungsdatum :

    08.02.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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