{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T11:29:04Z","timestamp":1786447744391,"version":"build-2736575974"},"reference-count":20,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52574210"],"award-info":[{"award-number":["52574210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["52574210"],"award-info":[{"award-number":["52574210"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"the project \u201cResearch and Application of Intelligent Mining and Transportation Equipment for Mining Robotization\u201d","award":["IMRI23002"],"award-info":[{"award-number":["IMRI23002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned mining trucks operate in harsh environments such as those in open-pit mines, where online fault diagnosis of critical drivetrain bearings faces severe challenges including slow response, high precision requirements, and strong interference from realistic on-site noise. To address the insufficient generalization capability of existing diagnostic methods in real-world noisy scenarios, this paper proposes a multimodal adversarial transfer learning framework for bearing fault diagnosis in unmanned mining trucks. First, to bridge the domain shift gap between laboratory data and on-site truck data, an augmented multimodal dataset is constructed based on real-vehicle noise grafting. This approach fuses authentic background noise collected from the field with clean laboratory fault signals, thereby simulating graded on-site interference. Second, a deep feature extraction network integrating CNN, ViT, and CBAM attention mechanisms is designed. Building upon this backbone, an adversarial training scheme combined with a hierarchical adaptive fine-tuning strategy is introduced to formulate a domain-adversarial transfer learning model. This model is capable of extracting robust features that are both fault-discriminative and domain-invariant from multimodal signals (vibration and current). Experimental results on the constructed noise-augmented dataset demonstrate that the proposed method maintains high diagnostic accuracy in cross-domain scenarios with strong noise and limited samples, significantly outperforming conventional approaches. This study provides an effective technical pathway for real-time and highly reliable \u201cedge-terminal\u201d fault diagnosis of unmanned mining trucks operating in realistic noisy environments.<\/jats:p>","DOI":"10.3390\/s26154890","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T11:50:21Z","timestamp":1785757821000},"page":"4890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Adversarial Transfer Learning for Bearing Fault Diagnosis of Unmanned Mining Trucks in Realistic Noisy Environments"],"prefix":"10.3390","volume":"26","author":[{"given":"Haifeng","family":"Han","sequence":"first","affiliation":[{"name":"Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017010, China"},{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017010, China"},{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6202-3617","authenticated-orcid":false,"given":"Jianjian","family":"Yang","sequence":"additional","affiliation":[{"name":"Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017010, China"},{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017010, China"},{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111112","DOI":"10.1016\/j.ymssp.2024.111112","article-title":"Frequency-chirprate synchro squeezing-based scaling chirplet transform for wind turbine nonstationary fault feature time-frequency representation","volume":"209","author":"Zhao","year":"2024","journal-title":"Mech. 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