{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:06:21Z","timestamp":1782835581772,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62072220"],"award-info":[{"award-number":["62072220"]}]},{"name":"National Natural Science Foundation of China","award":["2022-KF-13-06"],"award-info":[{"award-number":["2022-KF-13-06"]}]},{"name":"National Natural Science Foundation of China","award":["XLYC2203003"],"award-info":[{"award-number":["XLYC2203003"]}]},{"name":"Natural Science Foundation of Liaoning Province","award":["62072220"],"award-info":[{"award-number":["62072220"]}]},{"name":"Natural Science Foundation of Liaoning Province","award":["2022-KF-13-06"],"award-info":[{"award-number":["2022-KF-13-06"]}]},{"name":"Natural Science Foundation of Liaoning Province","award":["XLYC2203003"],"award-info":[{"award-number":["XLYC2203003"]}]},{"name":"Xing Liao Talent Program","award":["62072220"],"award-info":[{"award-number":["62072220"]}]},{"name":"Xing Liao Talent Program","award":["2022-KF-13-06"],"award-info":[{"award-number":["2022-KF-13-06"]}]},{"name":"Xing Liao Talent Program","award":["XLYC2203003"],"award-info":[{"award-number":["XLYC2203003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In response to the challenge of small and imbalanced Datasets, where the total Sample size is limited and healthy Samples significantly outweigh faulty ones, we propose a diagnostic framework designed to tackle Class imbalance, denoted as the Dual-Stream Adaptive Deep Residual Shrinkage Vision Transformer with Interclass\u2013Intraclass Rebalancing Loss (DSADRSViT-IIRL). Firstly, to address the issue of limited Sample quantity, we incorporated the Dual-Stream Adaptive Deep Residual Shrinkage Block (DSA-DRSB) into the Vision Transformer (ViT) architecture, creating a DSA-DRSB that adaptively removes redundant signal information based on the input data characteristics. This enhancement enables the model to focus on the Global receptive field while capturing crucial local fault discrimination features from the extremely limited Samples. Furthermore, to tackle the problem of a significant Class imbalance in long-tailed Datasets, we designed an Interclass\u2013Intraclass Rebalancing Loss (IIRL), which decouples the contributions of the Intraclass and Interclass Samples during training, thus promoting the stable convergence of the model. Finally, we conducted experiments on the Laboratory and CWRU bearing Datasets, validating the superiority of the DSADRSViT-IIRL algorithm in handling Class imbalance within mixed-load Datasets.<\/jats:p>","DOI":"10.3390\/s24030890","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T12:06:58Z","timestamp":1706616418000},"page":"890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Residual Shrinkage ViT with Discriminative Rebalancing Strategy for Small and Imbalanced Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Li","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixing","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110848","DOI":"10.1016\/j.ymssp.2023.110846","article-title":"IDSN: A one-stage interpretable and differentiable STFT domain adaptation Network for traction motor of high-speed trains cross-machine diagnosis","volume":"205","author":"He","year":"2023","journal-title":"Mech. 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