{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T11:51:15Z","timestamp":1792324275888,"version":"4.4.0"},"reference-count":25,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences of Jiangxi Province","award":["20224ACB204017"],"award-info":[{"award-number":["20224ACB204017"]}]},{"name":"Natural Sciences of Jiangxi Province","award":["N2023J042"],"award-info":[{"award-number":["N2023J042"]}]},{"name":"Natural Sciences of Jiangxi Province","award":["HJGZ2022202"],"award-info":[{"award-number":["HJGZ2022202"]}]},{"name":"Natural Sciences of Jiangxi Province","award":["HJGZ2022208"],"award-info":[{"award-number":["HJGZ2022208"]}]},{"name":"Science and Technology Project of China State Railway Group Co, Ltd.","award":["20224ACB204017"],"award-info":[{"award-number":["20224ACB204017"]}]},{"name":"Science and Technology Project of China State Railway Group Co, Ltd.","award":["N2023J042"],"award-info":[{"award-number":["N2023J042"]}]},{"name":"Science and Technology Project of China State Railway Group Co, Ltd.","award":["HJGZ2022202"],"award-info":[{"award-number":["HJGZ2022202"]}]},{"name":"Science and Technology Project of China State Railway Group Co, Ltd.","award":["HJGZ2022208"],"award-info":[{"award-number":["HJGZ2022208"]}]},{"name":"National Key Laboratory Autonomous Projects","award":["20224ACB204017"],"award-info":[{"award-number":["20224ACB204017"]}]},{"name":"National Key Laboratory Autonomous Projects","award":["N2023J042"],"award-info":[{"award-number":["N2023J042"]}]},{"name":"National Key Laboratory Autonomous Projects","award":["HJGZ2022202"],"award-info":[{"award-number":["HJGZ2022202"]}]},{"name":"National Key Laboratory Autonomous Projects","award":["HJGZ2022208"],"award-info":[{"award-number":["HJGZ2022208"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Data imbalances present a serious problem for intelligent fault diagnosis. They can lead to reduced diagnostic precision, which can jeopardize equipment reliability and safety. Based on that, this paper proposes a novel fault diagnosis method combining the denoising diffusion implicit model (DDIM) with a new convolutional neural network framework. First, the Gramian angular difference field (GADF) is used to generate 2D images, which are then augmented using DDIM. Next, by utilizing the weight-sharing properties of a convolutional neural network and the self-attention mechanism along with the global data processing capabilities of Transformers, a TransNet model is constructed. The augmented data are input into the model for training to establish a fault diagnosis framework. Finally, the method is validated and analyzed using the CWRU bearing dataset and the Nanchang Railway Bureau dataset. The results show that the proposed method achieves over 99% recognition accuracy on the two datasets. Meanwhile, the proposed model provides better generalization performance and recognition accuracy than existing fault diagnosis methods.<\/jats:p>","DOI":"10.3390\/s24248009","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T05:26:02Z","timestamp":1734413162000},"page":"8009","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Denoising Diffusion Implicit Model Combined with TransNet for Rolling Bearing Fault Diagnosis Under Imbalanced Data"],"prefix":"10.3390","volume":"24","author":[{"given":"Chaobing","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China"},{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5957-6269","authenticated-orcid":false,"given":"Cong","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2976-1412","authenticated-orcid":false,"given":"Long","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China"},{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongshuai","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-6284","authenticated-orcid":false,"given":"Jiayang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.cja.2019.07.011","article-title":"A new bearing fault diagnosis method based on modified convolutional neural networks","volume":"33","author":"Zhang","year":"2020","journal-title":"Chin. 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