[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124104-en":3,"doc-seo-124104-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124104,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning-based spatiotemporal fusion method for non-intrusive charging pile fault identification","Charging pile fault detection is essential to support reliable electric-vehicle charging, yet accurate fault identification is constrained by insufficient fault data and the need for highly distinctive fault features. The work builds a simulated charging pile system and collects multi-power-level fault data through manually introduced faults. A machine-learning algorithm performs spatiotemporal feature fusion by deriving temporal frequency-domain features via Fourier analysis on sliding windows and combining them with spatial current amplitude information for robust, accurate non-intrusive identification.","TYPE Original Research PUBLISHED 20 December 2024 DOI 10.3389/felec.2024.1490939  \nOPEN ACCESS  \nEDITED BY  \nFanfan Lin,  \nThe Zhejiang University-University of Illinois at Urbana-Champaign Institute, United States  \nREVIEWED BY  \nMuhammad Zain Yousaf,  \nHubei University of Automotive Technology, China  \nMohamed Ali Zdiri,  \nZhejiang University, China  \n*CORRESPONDENCE  \nHao Tian,  \n [haotian@email.sdu.edu.cn](haotian@email.sdu.edu.cn)  \nRECEIVED 04 September 2024  \nACCEPTED 02 December 2024  \nPUBLISHED 20 December 2024  \nCITATION  \nDuan Y, Shu S, Zhao Y, Mo H, Wu H, Hou C and Tian H (2024) Machine learning-based spatiotemporal fusion method for non-intrusive charging pile fault identification.  \nFront. Electron. 5:1490939 .  \ndoi: 10.3389/felec.2024.1490939  \nCOPYRIGHT  \n© 2024 Duan, Shu, Zhao, Mo, Wu, Hou and Tian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based spatiotemporal fusion method for non-intrusive charging pile fault identification  \nYoujun Duan 1, Shi Shu 1, Yange Zhao 1, Hengfeng Mo 2, Haitao Wu 2, Chengzhi Hou 3 and Hao Tian 4*  \n1Guilin Power Supply Bureau, China Southern Power Grid, Guilin, China, 2Guilin Yangshuo Power Supply Bureau, China Southern Power Grid, Guilin, China, 3Guilin Lingui Power Supply Bureau, China Southern Power Grid, Guilin, China, 4School of Control Science and Engineering, Shandong University, Jinan, China  \nFault detection in charging piles is crucial for the widespread adoption of electric vehicles and the reliability of charging infrastructure. Currently, due to the lack of sufficient fault data for charging piles, achieving stable and accurate fault identification is challenging. Moreover, distinctive fault features are key to accurate fault recognition. To address this, we designed a simulated charging pile system and collected fault data at multiple power levels by manually introducing faults. Furthermore, we proposed a fault identification algorithm based on spatiotemporal feature fusion using machine learning. This algorithm first collects fault data through a sliding window and utilizes Fourier transform to extract frequency domain information to construct temporal features. These features are then fused with spatial current amplitude information to form a distinctive feature set, enabling fault identification based on a machine learning model. Extensive experiments conducted on the constructed dataset show that this method can accurately identify charging pile faults. Compared with random forest and gradient boosted decision tree, the proposed method improves the macro-average score by 2 . 99% and 7 . 28%, respectively. We also explored the importance of each feature for fault identification results and the impact of window length on identification outcomes, demonstrating the necessity of the extracted features and the robustness of the proposed method to data resolution.  \nKEYWORDS  \ncharging pile, fault identification, machine learning, spatiotemporal information fusion, fault detection  \nHighlights  \n• In order to deal with the problem of insufficient fault detection data of current charging piles, charging pile simulation systems of various capacity levels are constructed to obtain rich fault data.  \n• The spatiotemporal information of current data is considered to obtain key features associated with faults.  \n• A method for fault detection in charging based on spatiotemporal fusion of machine learning is proposed, which realizes accurate and stable non-invasive fault identification.  \nFrontiers in Electronics 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  ","cbCaijACTGZLVEtk","https://ap.wps.com/l/cbCaijACTGZLVEtk","pdf",2598100,1,9,"English","en",105,"# Introduction\n## Problem background and motivation\n## Limitations of traditional fault identification\n# Method\n## Data collection with simulated charging pile systems\n## Spatiotemporal feature extraction and fusion\n## Machine learning-based fault identification\n# Experiments and results\n## Comparative performance evaluation\n## Feature importance analysis\n## Sliding-window length impact","[{\"question\":\"Why is fault identification for charging piles difficult in practice?\",\"answer\":\"It is challenging because available fault data are insufficient, and accurate recognition depends on extracting distinctive fault features.\"},{\"question\":\"How does the proposed method extract temporal and spatial information?\",\"answer\":\"Temporal features are obtained by applying Fourier transform on sliding-window fault data, while spatial features come from current amplitude information.\"},{\"question\":\"What performance improvement does the method achieve compared with other models?\",\"answer\":\"Experiments on the constructed dataset show higher macro-average scores than random forest and gradient boosted decision tree, improving by 2.99% and 7.28% respectively.\"}]","Machine learning-based spatiotemporal fusion method for non-intrusive charging pile fault identification | 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is fault identification for charging piles difficult in practice?","Question",{"text":75,"@type":76},"It is challenging because available fault data are insufficient, and accurate recognition depends on extracting distinctive fault features.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method extract temporal and spatial information?",{"text":80,"@type":76},"Temporal features are obtained by applying Fourier transform on sliding-window fault data, while spatial features come from current amplitude information.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance improvement does the method achieve compared with other models?",{"text":84,"@type":76},"Experiments on the constructed dataset show higher macro-average scores than random forest and gradient boosted decision tree, improving by 2.99% and 7.28% 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