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HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway

Yi Wang Orcid Logo, Xiaopei Cai Orcid Logo, Xueyang Tang Orcid Logo, Shuo Pan Orcid Logo, Yuqi Wang Orcid Logo, Hai Yan Orcid Logo, Yuheng Ren, Yue Hou Orcid Logo

IEEE Transactions on Intelligent Transportation Systems, Volume: 25, Issue: 12, Pages: 20793 - 20803

Swansea University Author: Yue Hou Orcid Logo

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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...

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Published in: IEEE Transactions on Intelligent Transportation Systems
ISSN: 1524-9050 1558-0016
Published: Institute of Electrical and Electronics Engineers (IEEE) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68355
first_indexed 2024-11-27T19:46:36Z
last_indexed 2025-01-13T20:34:22Z
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spelling 2025-01-13T14:43:06.0990342 v2 68355 2024-11-27 HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2024-11-27 ACEM 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. Journal Article IEEE Transactions on Intelligent Transportation Systems 25 12 20793 20803 Institute of Electrical and Electronics Engineers (IEEE) 1524-9050 1558-0016 Monitoring, Anomaly detection, Data models, Noise, Generative adversarial networks, Statistical analysis, Real-time systems, Temperature distribution, Support vector machines, Stress 1 12 2024 2024-12-01 10.1109/tits.2024.3477752 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University The National Natural Science Foundation of China under Grant 52178405 The Fundamental Research Funds for the Central Universities under Grant 2022JBQY009 and Grant 2023JBZX030. 2025-01-13T14:43:06.0990342 2024-11-27T13:48:34.6104094 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yi Wang 0009-0005-0816-8152 1 Xiaopei Cai 0000-0003-4592-4525 2 Xueyang Tang 0000-0003-2525-0316 3 Shuo Pan 0000-0001-9452-9738 4 Yuqi Wang 0009-0003-8231-6104 5 Hai Yan 0000-0003-0660-7228 6 Yuheng Ren 7 Yue Hou 0000-0002-4334-2620 8
title HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
spellingShingle HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
Yue Hou
title_short HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
title_full HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
title_fullStr HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
title_full_unstemmed HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
title_sort HSRA-Net: Intelligent Detection Network of Anomaly Monitoring Data in High-Speed Railway
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Yi Wang
Xiaopei Cai
Xueyang Tang
Shuo Pan
Yuqi Wang
Hai Yan
Yuheng Ren
Yue Hou
format Journal article
container_title IEEE Transactions on Intelligent Transportation Systems
container_volume 25
container_issue 12
container_start_page 20793
publishDate 2024
institution Swansea University
issn 1524-9050
1558-0016
doi_str_mv 10.1109/tits.2024.3477752
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description 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.
published_date 2024-12-01T08:36:38Z
_version_ 1821393916761473024
score 11.52865

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