Researchers in China have demonstrated a distributed acoustic sensing (DAS) system that repurposes existing underground optical fiber cables alongside railway tracks for continuous safety monitoring. By sending pulsed light through the cables and analyzing scattered light propagation, the system detects vibrations associated with safety hazards. Machine learning models were trained on over 13,000 samples to identify faulty train wheels (98.75% accuracy), broken sound barriers (99.6% accuracy), and abnormal trackside events like trespassers or falling rocks (97.03% accuracy). The approach requires no new cable infrastructure — only monitoring stations at intervals connecting to existing fiber — making it cost-effective and weather-resilient compared to traditional point-based methods like radar or video surveillance.
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