TEDS EMU Fault Image Dataset

TEDS EMU Fault Image Dataset | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Engineering Reports This is a preprint and has not been peer reviewed. Data may be preliminary. 4 April 2025 V1 Latest version Share on TEDS EMU Fault Image Dataset Authors : Mengxi Gao 0009-0001-0044-773X , Kai Yang , Miaomiao Qi [email protected] , Anni Meng , and Xiaoyu Guo Authors Info & Affiliations https://doi.org/10.22541/au.174377906.67961025/v1 Published Engineering Reports Version of record Peer review timeline 273 views 185 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The application of deep learning methods in the research on intelligent recognition of fault images in Electric Multiple Units (EMUs) represents a crucial approach to alleviating the heavy workload of Trouble of moving EMU Detection System (TEDS) inspections and safeguarding train safety. In light of the current TEDS image data’s problems such as irregular formats, substantial quality disparities, and uneven sample distribution, which pose challenges for deep learning, we proposes a method for constructing an intelligent recognition dataset of TEDS EMU fault images. By integrating the characteristics of TEDS images with the prior knowledge of EMU operations, a unified data acquisition protocol and standardized dataset construction techniques are devised. Consequently, an intelligent recognition dataset of TEDS EMU fault images is established. This dataset accomplishes effective data integration and standardization and has been successfully implemented in the research on automatic Recognition Algorithms for TEDS EMU faults. It accelerates the rapid progress of intelligent recognition technology for TEDS fault images and lays a robust technical foundation for ensuring the safety of EMU operations. Supplementary Material File (teds emu fault image dataset.docx) Download 867.33 KB Information & Authors Information Version history V1 Version 1 04 April 2025 Peer review timeline Published Engineering Reports Version of Record 2 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Engineering Reports Keywords data acquisition standards dataset intelligent recognition teds Authors Affiliations Mengxi Gao 0009-0001-0044-773X China Academy of Railway Sciences Corporation Limited View all articles by this author Kai Yang China Academy of Railway Sciences Corporation Limited View all articles by this author Miaomiao Qi [email protected] China Academy of Railway Sciences Corporation Limited View all articles by this author Anni Meng China Academy of Railway Sciences Corporation Limited View all articles by this author Xiaoyu Guo China Academy of Railway Sciences Corporation Limited View all articles by this author Metrics & Citations Metrics Article Usage 273 views 185 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mengxi Gao, Kai Yang, Miaomiao Qi, et al. TEDS EMU Fault Image Dataset. Authorea . 04 April 2025. DOI: https://doi.org/10.22541/au.174377906.67961025/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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