Journal article 13 views
HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
,
Xiaopei Cai ,
Xueyang Tang ,
Shuo Pan ,
Yuqi Wang ,
Hai Yan ,
Yuheng Ren,
Yue Hou
IEEE Transactions on Intelligent Transportation Systems, Volume: 25, Issue: 12, Pages: 20793 - 20803
Swansea University Author:
Yue Hou
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1109/tits.2024.3477752
Abstract
Real-time monitoring and analysis of sensitive areas in high-speed railway (HSR) are crucial for ensuring the safe and smooth operation of high-speed trains. To address the problem of frequent missing and false alarms caused by anomaly data in HSR monitoring system, this study proposes an innovative...
| Published in: | IEEE Transactions on Intelligent Transportation Systems |
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| ISSN: | 1524-9050 1558-0016 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68355 |
| Abstract: |
Real-time monitoring and analysis of sensitive areas in high-speed railway (HSR) are crucial for ensuring the safe and smooth operation of high-speed trains. To address the problem of frequent missing and false alarms caused by anomaly data in HSR monitoring system, this study proposes an innovative network framework: Intelligent detection network of HSR anomaly monitoring data (HSRA-Net). The framework comprises of two modules: the data augmentation module and the anomaly detection module. The data augmentation module designs multiple alternative generative adversarial networks for sample augmentation. To achieve the end-to-end classification, the anomaly detection module improves the residual network by creating a deep residual shrinkage network with self-attention (DRSN-SA). An online monitoring system was installed and operated continuously for several years on a high-speed turnout of a continuous beam bridge to validate the proposed framework. The collected data includes displacement, stress, and temperature. The proposed framework has demonstrated excellent performance, generalizability, and deployability through sufficient model comparison. It can accurately and efficiently diagnose anomalies in the operation of the monitoring system. This study is of great significance for improving the anomaly detection task of the HSR monitoring system. |
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| Keywords: |
Monitoring, Anomaly detection, Data models, Noise, Generative adversarial networks, Statistical analysis, Real-time systems, Temperature distribution, Support vector machines, Stress |
| College: |
Faculty of Science and Engineering |
| Funders: |
The National Natural Science Foundation of China under Grant 52178405
The Fundamental Research Funds for the Central Universities under Grant 2022JBQY009 and Grant 2023JBZX030. |
| Issue: |
12 |
| Start Page: |
20793 |
| End Page: |
20803 |