Current-Driven Autoencoder Framework based Thermal Field Prediction of Railway Electrical Devices

Current-Driven Autoencoder Framework based Thermal Field Prediction of Railway Electrical Devices | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 December 2025 V1 Latest version Share on Current-Driven Autoencoder Framework based Thermal Field Prediction of Railway Electrical Devices Authors : Jiancheng Liu , Yayun Liu , and Faye Zhang 0000-0001-6239-3231 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176674521.19060434/v1 129 views 57 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To improve the temperature prediction efficiency of typical electronic control devices in rail transit under multi current conditions, an intelligent prediction method for the thermal response of electronic control devices is proposed. The method utilizes an improved encoding decoding deep neural network, takes the thermal imaging image of the device surface as input, extracts its structural feature tensor, and combines current scalar information to achieve temperature field distribution prediction that decouples structure and operating conditions. During the training process, the structural encoder learns and solidifies the static thermal response characteristics of different devices, enabling the model to efficiently reuse and quickly adapt to multiple current inputs during the inference phase. The experiment conducted model training and validation based on measured data, and the results showed that the proposed method can accurately predict the temperature field under any current. This method has good universality, accuracy, and deployment efficiency, providing new ideas for thermal performance evaluation and intelligent thermal management of rail transit electronic control systems. Supplementary Material File (bare_jrnl_new_sample4.pdf) Download 4.73 MB File (ieee-transactions-latex2e-templates-and-instructions.rar) Download 10.19 MB Information & Authors Information Version history V1 Version 1 26 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords learning (artificial intelligence) network Authors Affiliations Jiancheng Liu CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd View all articles by this author Yayun Liu CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd View all articles by this author Faye Zhang 0000-0001-6239-3231 [email protected] Shandong University School of Control Science and Engineering View all articles by this author Metrics & Citations Metrics Article Usage 129 views 57 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jiancheng Liu, Yayun Liu, Faye Zhang. Current-Driven Autoencoder Framework based Thermal Field Prediction of Railway Electrical Devices. Authorea . 26 December 2025. DOI: https://doi.org/10.22541/au.176674521.19060434/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 . 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