{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:33:39Z","timestamp":1786980819869,"version":"3.56.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007224","name":"National Foundation for Science and Technology Development","doi-asserted-by":"publisher","award":["102.05-2021.34"],"award-info":[{"award-number":["102.05-2021.34"]}],"id":[{"id":"10.13039\/100007224","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06361-0","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T03:31:43Z","timestamp":1740627103000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Mixmamba-fewshot: mamba and attention mixer-based method with few-shot learning for bearing fault diagnosis"],"prefix":"10.1007","volume":"55","author":[{"given":"Nhu-Linh","family":"Than","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Van Quang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gia-Bao","family":"Truong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Van-Truong","family":"Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0862-1262","authenticated-orcid":false,"given":"Thi-Thao","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"6361_CR1","doi-asserted-by":"crossref","unstructured":"Smith DM (1969) Bearing development and bearing theory. J Bearings Turbomach 3\u20135. Boston, MA: Springer US","DOI":"10.1007\/978-1-4757-5623-4_1"},{"key":"6361_CR2","doi-asserted-by":"crossref","unstructured":"Samanta P, Murmu N, Khonsari M (2019) The evolution of foil bearing technology. Tribol Int 135:305\u2013323. ISSN: 0301-679X","DOI":"10.1016\/j.triboint.2019.03.021"},{"key":"6361_CR3","doi-asserted-by":"crossref","unstructured":"Yadav E, Chawla V (2022) An explicit literature review on bearing materials and their defect detection techniques. In: Materials Today: Proceedings 50. 2nd International Conference on Functional Material, Manufacturing and Performances (ICFMMP-2021), pp 1637\u20131643","DOI":"10.1016\/j.matpr.2021.09.132"},{"issue":"6","key":"6361_CR4","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1016\/j.matdes.2008.07.056","volume":"30","author":"MM Goudarzi","year":"2009","unstructured":"Goudarzi MM, Jahromi SJ, Nazarboland A (2009) Investigation of characteristics of tin-based white metals as a bearing material. Mater Des 30(6):2283\u20132288","journal-title":"Mater Des"},{"key":"6361_CR5","doi-asserted-by":"crossref","unstructured":"Wu K, Yu K, Chen C, Wu J, Liu Y (2024) Optimal transport strategy-based meta-attention network for fault diagnosis of rotating machinery with zero sample. Appl Intell 1\u201317","DOI":"10.2139\/ssrn.4420271"},{"key":"6361_CR6","doi-asserted-by":"publisher","first-page":"110895","DOI":"10.1109\/ACCESS.2019.2934233","volume":"7","author":"A Zhang","year":"2019","unstructured":"Zhang A, Li S, Cui Y, Yang W, Dong R, Hu J (2019) Limited data rolling bearing fault diagnosis with few-shot learning. Ieee Access 7:110895\u2013110904","journal-title":"Ieee Access"},{"key":"6361_CR7","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhao X, Liang H, Chen P (2024) Multiscale dilated convolution and swin-transformer for small sample gearbox fault diagnosis. Appl Intell 1\u201317","DOI":"10.1007\/s10489-024-05530-x"},{"key":"6361_CR8","doi-asserted-by":"crossref","unstructured":"Kumar H, Upadhyaya G (2023) Fault diagnosis of rolling element bearing using continuous wavelet transform and k-nearest neighbour. In: Materials today: proceedings 92, pp 56\u201360","DOI":"10.1016\/j.matpr.2023.03.618"},{"issue":"4","key":"6361_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s13748-020-00217-z","volume":"9","author":"LS Sawaqed","year":"2020","unstructured":"Sawaqed LS, Alrayes AM (2020) Bearing fault diagnostic using machine learning algorithms. Prog Artif Intell 9(4):341\u2013350","journal-title":"Prog Artif Intell"},{"issue":"2","key":"6361_CR10","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.cja.2019.07.011","volume":"33","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yi S, Liang G, Hongli G, Xin H, Hongliang S (2020) A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeronaut 33(2):439\u2013447","journal-title":"Chin J Aeronaut"},{"issue":"4","key":"6361_CR11","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/s10845-020-01600-2","volume":"32","author":"X Chen","year":"2021","unstructured":"Chen X, Zhang B, Gao D (2021) Bearing fault diagnosis base on multi-scale cnn and lstm model. J Intell Manuf 32(4):971\u2013987","journal-title":"J Intell Manuf"},{"issue":"8","key":"6361_CR12","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac66c4","volume":"33","author":"Z Yang","year":"2022","unstructured":"Yang Z, Cen J, Liu X, Xiong J, Chen H (2022) Research on bearing fault diagnosis method based on transformer neural network. Meas Sci Technol 33(8):085111","journal-title":"Meas Sci Technol"},{"key":"6361_CR13","unstructured":"Koch G, Zemel R, Salakhutdinov R et al (2015) Siamese neural networks for one-shot image recognition. In: Icml deep learning workshop, vol 2. 1. Lille. pp 1\u201330"},{"issue":"7553","key":"6361_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"6361_CR15","doi-asserted-by":"crossref","unstructured":"Vu MH, Pham VT (2023) Mixerformer-covariance metric neural network: A new few-shot learning model for bearing fault diagnosis. In: 2023 12th international conference on control, automation and information sciences (ICCAIS), pp 639\u2013644","DOI":"10.1109\/ICCAIS59597.2023.10382300"},{"key":"6361_CR16","doi-asserted-by":"crossref","unstructured":"Shen H, Zhao D, Wang L, Liu Q (2023) Bearing fault diagnosis based on prototypical network. In: International conference on mechatronics engineering and artificial intelligence (MEAI 2022), vol 12596, SPIE. pp 79\u201384","DOI":"10.1117\/12.2671906"},{"key":"6361_CR17","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2021.01.099","volume":"439","author":"C Li","year":"2021","unstructured":"Li C, Li S, Zhang A, He Q, Liao Z, Hu J (2021) Meta-learning for few-shot bearing fault diagnosis under complex working conditions. Neurocomputing 439:197\u2013211","journal-title":"Neurocomputing"},{"key":"6361_CR18","doi-asserted-by":"crossref","unstructured":"Li W, Xu J, Huo J, Wang L, Gao Y, Luo J (2019) Distribution consistency based covariance metric networks for few-shot learning. In: AAAI Conference on artificial intelligence. https:\/\/api.semanticscholar.org\/CorpusID:69672216","DOI":"10.1609\/aaai.v33i01.33018642"},{"issue":"24","key":"6361_CR19","doi-asserted-by":"publisher","first-page":"30080","DOI":"10.1007\/s10489-023-05128-9","volume":"53","author":"X Zheng","year":"2023","unstructured":"Zheng X, Yue C, Wei J, Xue A, Ge M, Kong Y (2023) Few-shot intelligent fault diagnosis based on an improved meta-relation network. Appl Intell 53(24):30080\u201330096","journal-title":"Appl Intell"},{"key":"6361_CR20","unstructured":"Gu A, Dao T (2023) Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752"},{"key":"6361_CR21","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X (2024) Vision mamba: Efficient visual representation learning with bidirectional state space model. ArXiv arXiv:2401.09417https:\/\/api.semanticscholar.org\/CorpusID:267028142"},{"key":"6361_CR22","unstructured":"Liu Y, Tian Y, Zhao Y, Yu H, Xie L, Wang Y, Ye Q, Liu Y (2024) Vmamba: Visual state space model. CoRR arXiv:2401.10166https:\/\/doi.org\/10.48550\/arXiv.2401.10166"},{"key":"6361_CR23","unstructured":"Trockman A, Kolter JZ (2022) Patches are all you need? arXiv preprint arXiv:2201.09792"},{"issue":"6","key":"6361_CR24","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1364\/OL.35.000796","volume":"35","author":"Y Park","year":"2010","unstructured":"Park Y, Aza\u00f1a J (2010) Optical signal processors based on a time-spectrum convolution. Opt Lett 35(6):796\u2013798","journal-title":"Opt Lett"},{"key":"6361_CR25","unstructured":"Yue Y, Li Z (2024) Medmamba: Vision mamba for medical image classification. arXiv:2403.03849"},{"key":"6361_CR26","doi-asserted-by":"publisher","DOI":"10.4108\/eetinis.v10i3.3562","volume":"10","author":"TV Le","year":"2023","unstructured":"Le TV, Le H-M-Q, Vu VY, Tran T-T, Pham V-T (2023) Attention convmixer model and application for fish species classification. EAI Endorsed Trans Ind Netw Intell Syst 10:e2","journal-title":"EAI Endorsed Trans Ind Netw Intell Syst"},{"key":"6361_CR27","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","volume":"64","author":"WA Smith","year":"2015","unstructured":"Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the case western reserve university data: A benchmark study. Mech Syst Signal Process 64:100\u2013131","journal-title":"Mech Syst Signal Process"},{"key":"6361_CR28","unstructured":"Paderborn University bearing data center (2014). Online. https:\/\/mb.uni-paderborn.de\/kat\/forschung\/datacenter\/bearing-datacenter Accessed Jan 2021"},{"key":"6361_CR29","doi-asserted-by":"crossref","unstructured":"Alonso-Gonz\u00e1lez M, D\u00edaz VG, P\u00e9rez BL, G-Bustelo BCP, Anzola JP, (2023) Bearing fault diagnosis with envelope analysis and machine learning approaches using cwru dataset. IEEE Access 11:57796\u201357805","DOI":"10.1109\/ACCESS.2023.3283466"},{"key":"6361_CR30","doi-asserted-by":"crossref","unstructured":"Vu M-H, Nguyen V-Q, Tran T-T, Pham V-T, Lo M-T (2024) Few-shot bearing fault diagnosis via ensembling transformer-based model with mahalanobis distance metric learning from multiscale features. IEEE Transactions on Instrumentation and Measurement","DOI":"10.1109\/TIM.2024.3381270"},{"key":"6361_CR31","doi-asserted-by":"crossref","unstructured":"Nguyen V-Q, Vu M-H, Pham V-T, Tran T-T (2023) A deep learning approach based on mlp-mixer models for bearing fault diagnosis. In: 2023 International conference on system science and engineering (ICSSE), pp 16\u201321. IEEE","DOI":"10.1109\/ICSSE58758.2023.10227218"},{"key":"6361_CR32","doi-asserted-by":"publisher","first-page":"93155","DOI":"10.1109\/ACCESS.2020.2990528","volume":"8","author":"D Neupane","year":"2020","unstructured":"Neupane D, Seok J (2020) Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. IEEE Access 8:93155\u201393178","journal-title":"IEEE Access"},{"issue":"5","key":"6361_CR33","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1080\/08839514.2020.1723868","volume":"34","author":"K Park","year":"2020","unstructured":"Park K, Hong JS, Kim W (2020) A methodology combining cosine similarity with classifier for text classification. Appl Artif Intell 34(5):396\u2013411","journal-title":"Appl Artif Intell"},{"key":"6361_CR34","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29"},{"key":"6361_CR35","unstructured":"Hou R, Chang H, Ma B, Shan S, Chen X (2019) Cross attention network for few-shot classification. Adv Neural Inf Process Syst 32"},{"key":"6361_CR36","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: 2018 IEEE\/CVF Conference on computer vision and pattern recognition, pp 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"6361_CR37","doi-asserted-by":"crossref","unstructured":"Vu M-H, Nguyen V-Q, Tran T-T, Pham V-T (2023) A new convmixer-based approach for diagnosis of fault bearing using signal spectrum. In: Conference on information technology and its applications. Springer, pp 3\u201314","DOI":"10.1007\/978-3-031-36886-8_1"},{"key":"6361_CR38","doi-asserted-by":"publisher","first-page":"1149186","DOI":"10.3389\/fmars.2023.1149186","volume":"10","author":"J Zhai","year":"2023","unstructured":"Zhai J, Han L, Xiao Y, Yan M, Wang Y, Wang X (2023) Few-shot fine-grained fish species classification via sandwich attention CovaMNet. Front Mar Sci 10:1149186","journal-title":"Front Mar Sci"},{"issue":"12","key":"6361_CR39","doi-asserted-by":"publisher","first-page":"7789","DOI":"10.1109\/TCSVT.2023.3282777","volume":"33","author":"X Wang","year":"2023","unstructured":"Wang X, Wang X, Jiang B, Luo B (2023) Few-shot learning meets transformer: Unified query-support transformers for few-shot classification. IEEE Trans Circ Syst Vid Technol 33(12):7789\u20137802","journal-title":"IEEE Trans Circ Syst Vid Technol"},{"key":"6361_CR40","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06361-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06361-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06361-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:30:46Z","timestamp":1758310246000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06361-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,27]]},"references-count":40,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6361"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06361-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,27]]},"assertion":[{"value":"10 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"484"}}