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Variational mode decomposition-based abnormal wheel-rail relationship detection in distributed acoustic sensing.

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    This study introduces a novel method using distributed acoustic sensing to detect abnormal train wheel-rail interactions. The system analyzes vibration signals to identify and locate faulty bogies, enhancing railway safety.

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    Area of Science:

    • Railway Engineering
    • Signal Processing
    • Condition Monitoring

    Background:

    • Effective monitoring of wheel-rail interaction is crucial for railway safety and operational efficiency.
    • Existing methods may lack the precision or real-time capabilities needed for early detection of anomalies.

    Purpose of the Study:

    • To develop and validate a novel scheme for identifying abnormal wheel-rail relationships using distributed acoustic sensing (DAS).
    • To accurately locate the specific bogie exhibiting abnormal conditions.

    Main Methods:

    • Utilizing DAS to capture train-induced vibration signals.
    • Employing variational mode decomposition (VMD) for signal analysis and extraction of intrinsic mode functions (IMFs).
    • Calculating kurtosis values of IMFs to identify abnormal fluctuations and comparing them against a predefined threshold.

    Main Results:

    • The proposed scheme successfully identified trains with abnormal wheel-rail relationships.
    • Abnormal IMFs were detected, and their extreme points were used to pinpoint the faulty bogie.
    • Experimental validation confirmed the effectiveness of the identification and localization method.

    Conclusions:

    • The DAS-based approach offers a reliable method for detecting and locating abnormal wheel-rail conditions.
    • This technique can significantly contribute to predictive maintenance and enhance overall railway safety.