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Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing
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Surface intrusion event identification for subway tunnels using ultra-weak FBG array based fiber sensing.

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    This study introduces a new method using fiber Bragg gratings to detect surface intrusion events in subway tunnels. The advanced signal processing achieved a high 96.57% recognition rate for various events.

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    Area of Science:

    • Fiber optic sensing
    • Signal processing
    • Machine learning for event detection

    Background:

    • Monitoring subway tunnels for security and safety is crucial.
    • Existing methods may struggle to differentiate various surface activities from actual intrusions.
    • Ultra-weak fiber Bragg gratings offer sensitive detection capabilities.

    Purpose of the Study:

    • To develop and validate a robust scheme for identifying surface intrusion events using fiber Bragg grating sensor arrays.
    • To enhance the accuracy of event recognition by employing multi-scale analysis techniques.
    • To differentiate between intrusion and non-intrusion events in a subway environment.

    Main Methods:

    • Utilizing an ultra-weak fiber Bragg grating array for signal acquisition in a subway tunnel.
    • Applying spectral subtraction and power spectral density root mean square for initial event signal extraction.
    • Employing local characteristics-scale decomposition and multi-scale permutation entropy for advanced feature extraction.
    • Conducting experimental validation with four distinct event types.

    Main Results:

    • The proposed scheme successfully identified four common events: discrete pulse construction, continuous pulse construction, subway train travel, and lorry passage.
    • Intrusion events (discrete and continuous pulse construction) were distinguished from non-intrusion events (train and lorry passage).
    • An average recognition rate of 96.57% was achieved, demonstrating high accuracy.

    Conclusions:

    • The developed scheme effectively identifies surface intrusion events in subway tunnels.
    • The combination of signal processing and multi-scale entropy analysis significantly improves event recognition.
    • The high accuracy achieved meets practical application requirements for enhanced security.