The development of algorithms for detecting failures in railway catenary support components has, among others, one major challenge: data about healthy components are much more abundant than data about defective components. In this paper, virtual reality technology is employed to control the learning environment of convolutional neural networks (CNNs) for the automatic multicamera-based monitoring of catenary support components. First, 3D image data based on drawings and real-life video images are developed. Then, a virtual reality environment for monitoring the catenary support system is created, emulating real-life conditions such as measurement noise and a multicamera train simulation to resemble state-of-the-art monitoring systems. Then, CNNs are used to extract and fuse the features of multicamera images. Experiments are conducted for monitoring the cantilever support connection, both down (CSC-D) and up (CSC-U), and registration arm support connection, both down (RASC-D) and up (RASC-U). Experimental results show that the CNNs trained in the virtual reality environment can capture the most relevant spatial information of the catenary support components. Multicamera image detection based on CNNs detects screw loss for all four components. For CSC-D and RASC-U, normal and pin-loss images are also fully detected. A challenge remains in increasing the pin-loss detection for both CSC-U and RASC-D.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Virtual Reality and Convolutional Neural Networks for Railway Catenary Support Components Monitoring


    Beteiligte:
    Liu, Wenqiang (Autor:in) / Liu, Zhigang (Autor:in) / Nunez, Alfredo (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    886509 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    railway catenary support assembly

    KANG JI HYEON / CHOI JI YEONG / SEO DO SEOK | Europäisches Patentamt | 2023

    Freier Zugriff

    Visualizing Convolutional Neural Networks with Virtual Reality

    Meissler, Nadine / Wohlan, Annika / Hochgeschwender, Nico | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2019

    Freier Zugriff


    Swin Transformer-Based Positioning Methodology for Catenary Support Components in High-Speed Railway

    Wang, Hui / Guo, Weiping / Zhong, Junping et al. | British Library Conference Proceedings | 2022


    Swin Transformer-Based Positioning Methodology for Catenary Support Components in High-Speed Railway

    Wang, Hui / Guo, Weiping / Zhong, Junping et al. | Springer Verlag | 2022