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LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation

Xueyang Tang, Xiaopei Cai, Yuqi Wang, Yue Hou Orcid Logo

Engineering Applications of Artificial Intelligence, Volume: 140, Start page: 109708

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1016/j.engappai.2024.109708

Abstract

Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision o...

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Published in: Engineering Applications of Artificial Intelligence
ISSN: 0952-1976
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68354
first_indexed 2024-11-27T19:46:36Z
last_indexed 2025-01-14T14:38:27Z
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spelling 2025-01-14T09:42:11.1620689 v2 68354 2024-11-27 LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2024-11-27 ACEM Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision or axle box acceleration face challenges such as high costs, complex equipment installation and maintenance, as well as difficulties in achieving real-time performance. To address these challenges, this study proposes an innovative low-cost real-time recognition network (LCRTR-Net), which utilizes accelerometers installed on the underside of the train body and combines wavelet packet decomposition with dilated causal convolution in a residual neural network. Specifically, the approach first extracts the latent features of train body acceleration caused by rail corrugation through wavelet packet decomposition and reconstruction. Next, dilated causal convolution is employed to capture the temporal causal relationships and long-term dependencies of these latent features. Finally, the integration of residual connections further enhances the feature extraction performance and computational efficiency of LCRTR-Net. Experimental results demonstrate that LCRTR-Net exhibits significant generalization ability and real-time performance, achieving an average recognition accuracy exceeding 97.0%, with a recognition time of only 0.17 ms per rail corrugation sample, significantly outperforming existing rail corrugation recognition methods. This indicates that LCRTR-Net has broad application potential in practical railway operations. Future research directions will focus on unsupervised or few-shot learning algorithms and multi-sensor integration to further improve recognition accuracy and real-time performance, promoting the practical application of this technology. Journal Article Engineering Applications of Artificial Intelligence 140 109708 Elsevier BV 0952-1976 High-speed railway; Rail corrugation recognition; Car body acceleration; Wavelet packet decomposition and reconstruction; Dilated causal convolution 15 1 2025 2025-01-15 10.1016/j.engappai.2024.109708 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University The work was supported by the National Key R&D Program of China (No. 2022YFB2602901) The Project of Science and Technology Research and Development Program of China State Railway Group Co. Ltd. (No. SY2022T002) The National Natural Science Foundation of China (No. 52178405). 2025-01-14T09:42:11.1620689 2024-11-27T13:43:40.7990866 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Xueyang Tang 1 Xiaopei Cai 2 Yuqi Wang 3 Yue Hou 0000-0002-4334-2620 4
title LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
spellingShingle LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
Yue Hou
title_short LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
title_full LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
title_fullStr LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
title_full_unstemmed LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
title_sort LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Xueyang Tang
Xiaopei Cai
Yuqi Wang
Yue Hou
format Journal article
container_title Engineering Applications of Artificial Intelligence
container_volume 140
container_start_page 109708
publishDate 2025
institution Swansea University
issn 0952-1976
doi_str_mv 10.1016/j.engappai.2024.109708
publisher Elsevier BV
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 Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision or axle box acceleration face challenges such as high costs, complex equipment installation and maintenance, as well as difficulties in achieving real-time performance. To address these challenges, this study proposes an innovative low-cost real-time recognition network (LCRTR-Net), which utilizes accelerometers installed on the underside of the train body and combines wavelet packet decomposition with dilated causal convolution in a residual neural network. Specifically, the approach first extracts the latent features of train body acceleration caused by rail corrugation through wavelet packet decomposition and reconstruction. Next, dilated causal convolution is employed to capture the temporal causal relationships and long-term dependencies of these latent features. Finally, the integration of residual connections further enhances the feature extraction performance and computational efficiency of LCRTR-Net. Experimental results demonstrate that LCRTR-Net exhibits significant generalization ability and real-time performance, achieving an average recognition accuracy exceeding 97.0%, with a recognition time of only 0.17 ms per rail corrugation sample, significantly outperforming existing rail corrugation recognition methods. This indicates that LCRTR-Net has broad application potential in practical railway operations. Future research directions will focus on unsupervised or few-shot learning algorithms and multi-sensor integration to further improve recognition accuracy and real-time performance, promoting the practical application of this technology.
published_date 2025-01-15T08:36:38Z
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score 11.544631

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