Deep Learning Model for Railroad Structural Health Monitoring via Distributed Acoustic Sensing

Railway infrastructure plays a vital role in modern transportation systems, facilitating the efficient movement of people and goods. However, the integrity and performance of railroad structures are subject to various external forces and aging processes, which necessitate continuous monitoring to en...

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Veröffentlicht in:2023 26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter) S. 274 - 281
Hauptverfasser: Rahman, Md Arifur, Taheri, Hossein, Kim, Jongyeop
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 05.07.2023
Schlagworte:
Acoustics
DAS
Deep learning
Distributed databases
HTL
HTL Fiber
LSTM
Predictive models
railroad health
railroad monitoring
Sensors
Structural Health Monitoring
Time division multiplexing
Transportation
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Zusammenfassung:Railway infrastructure plays a vital role in modern transportation systems, facilitating the efficient movement of people and goods. However, the integrity and performance of railroad structures are subject to various external forces and aging processes, which necessitate continuous monitoring to ensure safety and operational efficiency. This research focused on the structural health monitoring of the railroad using Distributed Acoustic Sensing (DAS) data collected from a High Tonnage Loop (HTL). An investigation on applying a deep learning model, long-shot-term memory (LSTM), and gated recurrent Unit(GRU) is presented to identify and classify railroad conditions.
DOI:10.1109/SNPD-Winter57765.2023.10223971