As a critical component of railway infrastructure, the railway point machine (RPM) is vital for the operational efficiency and safety of the entire railway system. The intelligent diagnosis of RPM faults is thus of considerable significance. Traditional methods have often neglected the fractional-order components in fault signals, focusing instead on integer-order signals during feature extraction. This study presents a novel fault diagnosis approach that leverages the vibration signals of the RPM. Utilizing the Fractional Fourier Transform (FRFT) at its optimal order to extract fractional-order features, the subsequent deep learning process employs Convolutional Neural Networks (CNN) to further enhance these extracted features, and finally classifying the fault data with SVM, the method addresses the shortcomings of existing techniques. The effectiveness of this approach is confirmed through experimental results, which demonstrate its ability to accurately identify RPM faults. This advancement in fault diagnosis is expected to significantly enhance the predictive maintenance of railway systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Railway Point Machines Fault Diagnosis Based on Fractional Fourier Transform


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Ou, Dongxiu (editor) / Liu, Hui (editor) / Zong, Fang (editor) / Wang, Pangwei (editor) / Zhang, Mingfang (editor) / Yang, Wentao (author) / Wen, Tao (author) / Cai, Baigen (author) / Clive, Roberts (author)

    Conference:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Publication date :

    2025-03-31


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Expert system based fault diagnosis for railway point machines

    Reetz, Susanne / Neumann, Thorsten / Schrijver, Gerrit et al. | SAGE Publications | 2024

    Free access


    A hybrid fault diagnosis scheme for railway point machines by motor current signal analysis

    Narges, Khadem Hossaini / Ahmad, Mirabadi / Fereydoun, Gholami Manesh | SAGE Publications | 2022


    Bearing Fault Diagnosis Method Based on Graph Fourier Transform and C4.5 Decision Tree

    Wang, Yuze / Qin, Yong / Zhao, Xuejun et al. | British Library Conference Proceedings | 2020


    Bearing Fault Diagnosis Method Based on Graph Fourier Transform and C4.5 Decision Tree

    Wang, Yuze / Qin, Yong / Zhao, Xuejun et al. | Springer Verlag | 2020