This work introduces an improvised, cost effective system with renewable based electric vehicle that detects major faults in the railway track with better accuracy. Unlike the manually pulling cart used conventionally, this vehicle is automated and controlled by Raspberry Pi. Image processing is introduced to detect faults like vertical broken rail, horizontal split head and broken base. For the detection of crushed head and misalignment of tracks, ultrasonic sensor are employed. In addition to this, vibration sensors are used to find the faulty areas in the track. The information regarding the faults can be used to do Job scheduling in order to make the rectifying process easier. This is implemented using GSM module and Raspberry Pi. A periodic communication is done between the vehicle and the control room for efficient working of the system. The vehicle is eco-friendly as it uses the renewable source solar energy to power the vehicle. A number of test cases are tested for each fault and its values are recorded.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automated Railway Track Fault Detection Using Solar Powered Electric Vehicle


    Contributors:


    Publication date :

    2019-04-01


    Size :

    728773 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Solar powered railway track monitoring system

    Gajanur, Nanditha R / Singh, Apurva / Jain, Aishwarya | IEEE | 2016


    Automated Railway Track Fault Detection using ResNet50V2 Deep Learning Architecture

    Kaur, Gurjot / Sharma, Neha / Malhotra, Sonal et al. | IEEE | 2024


    Railway track detection vehicle

    CHEN JIAN / LIU JICHENG / HAN LIANFU et al. | European Patent Office | 2025

    Free access

    Railway track detection vehicle

    ZHU XUEJIN / YANG YANG | European Patent Office | 2025

    Free access

    Railway Track Fasteners Fault Detection using Deep Learning

    Lin, Ya-Wen / Hsieh, Chen-Chiung / Huang, Wei-Hsin et al. | IEEE | 2019