{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T14:43:04Z","timestamp":1772203384476,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Project of Henan Province","award":["251111211600"],"award-info":[{"award-number":["251111211600"]}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["252300420397"],"award-info":[{"award-number":["252300420397"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henan Province Science and Technology R&D projects","award":["242102320215"],"award-info":[{"award-number":["242102320215"]}]},{"name":"Joint Fund Project of the National Natural Science Foundation of China","award":["U2368201"],"award-info":[{"award-number":["U2368201"]}]},{"name":"High-end Foreign Expert Program of Henan Province","award":["HNGD2024032"],"award-info":[{"award-number":["HNGD2024032"]}]},{"name":"Subject Strength Enhancement Plan Project of Zhongyuan University of Technology","award":["GG202412"],"award-info":[{"award-number":["GG202412"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Symmetry"],"abstract":"<jats:p>As a critical component of rail travel, the train communication network (TCN) is an integrated central platform that is used to realize the train control, condition monitoring, and data transmission, whose failure will disrupt the symmetry of TCN topology and endanger the security of rail trains. To enhance the reliability of TCN, an intelligent fault diagnosis method is proposed based on active learning (AL) and a stacked consistent autoencoder (SCAE), which is capable of building a competitive classifier with a limited amount of labeled training samples. SCAE can learn better feature presentations from electrical multifunction vehicle bus (MVB) signals by reconstructing the same raw input data layer by layer in the unsupervised feature learning phase. In the supervised fine-tuning phase, a deep AL-based fault diagnosis framework is proposed, and a dynamic fusion AL method is presented. The most valuable unlabeled samples are selected for labeling and training by considering uncertainty and similarity simultaneously, and the fusion weight is dynamically adjusted at the different training stages. A TCN experimental platform is constructed, and experimental results show that the proposed method achieves better performance under three different metrics with fewer labeled samples compared to the state-of-the-art methods; it is also symmetrically valid in class-imbalanced data.<\/jats:p>","DOI":"10.3390\/sym17101622","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T07:58:13Z","timestamp":1759305493000},"page":"1622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Fault Diagnosis Method for the Train Communication Network Based on Active Learning and Stacked Consistent Autoencoder"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5966-8169","authenticated-orcid":false,"given":"Yueyi","family":"Yang","sequence":"first","affiliation":[{"name":"Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou 471700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiquan","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou 471700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobo","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjun","family":"Wen","sequence":"additional","affiliation":[{"name":"Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou 471700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guolong","family":"Li","sequence":"additional","affiliation":[{"name":"Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou 471700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/MCOM.001.1800957","article-title":"Train Communication Networks and Prospects","volume":"57","author":"Luedicke","year":"2019","journal-title":"IEEE Commun. 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