首页|期刊导航|新能源与智能载运（英文）|Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model
新能源与智能载运（英文）2025，Vol.2025Issue(4)：51-66,16.DOI:10.1016/j.geits.2024.100178
Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model
Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model
摘要
关键词
Distributed acoustic sensing(DAS)-Fiber optic cable/Railroad condition monitoring and anomaly detection/High tonnage load(HTL)/Convolutional neural network-long short-term memory-sliding window(CNN-LSTM-SW)Key words
Distributed acoustic sensing(DAS)-Fiber optic cable/Railroad condition monitoring and anomaly detection/High tonnage load(HTL)/Convolutional neural network-long short-term memory-sliding window(CNN-LSTM-SW)引用本文复制引用
Md Arifur Rahman,Suhaima Jamal,Hossein Taheri..Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model[J].新能源与智能载运（英文）,2025,2025(4):51-66,16.基金项目
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. （AAR）
