Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based modelOA
Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model
Md Arifur Rahman;Suhaima Jamal;Hossein Taheri
The Laboratory for Advanced Non-Destructive Testing,In-situ Monitoring,and Evaluation(LANDTIE),Department of Manufacturing Engineering,Georgia Southern University,Statesboro 30458,GA,USAInformation Technology Department,Georgia Southern University,Statesboro 30458,GA,USAThe Laboratory for Advanced Non-Destructive Testing,In-situ Monitoring,and Evaluation(LANDTIE),Department of Manufacturing Engineering,Georgia Southern University,Statesboro 30458,GA,USA
Distributed acoustic sensing(DAS)-Fiber optic cableRailroad condition monitoring and anomaly detectionHigh tonnage load(HTL)Convolutional neural network-long short-term memory-sliding window(CNN-LSTM-SW)
Distributed acoustic sensing(DAS)-Fiber optic cableRailroad condition monitoring and anomaly detectionHigh tonnage load(HTL)Convolutional neural network-long short-term memory-sliding window(CNN-LSTM-SW)
《新能源与智能载运（英文）》 2025 (4)
51-66,16
This work has been supported by funding from The Association of American Railroads(AAR)-MxV Rail(Award number:21-0825-007538)and Impact Area Accelerator Award Grant 2023 from Georgia Southern University's Office of Research. This work is supported by the AAR/TTCI under the program:Grand Challenge Research Topic:In-motion Track Stability Assessment under award ID 21-0825-007538 and Impact Area Accelerator Grant(2023)from Georgia Southern University's Office of Research.The authors are also grateful to MxV Rail(Dr.Anish Poudel),and Dr.Hai Huang for sharing the DAS data.
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