{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:27:58Z","timestamp":1774121278376,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203263"],"award-info":[{"award-number":["62203263"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62350083"],"award-info":[{"award-number":["62350083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR2022QF062"],"award-info":[{"award-number":["ZR2022QF062"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04847-5","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T18:47:36Z","timestamp":1759517256000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamics-enhanced Vision Transformer for Cross-Domain Bearing Fault Diagnosis"],"prefix":"10.1007","volume":"19","author":[{"given":"Junnan","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingtao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianrui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"4847_CR1","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.jmsy.2022.08.007","volume":"64","author":"Z Xie","year":"2022","unstructured":"Xie, Z., Chen, J., Feng, Y., He, S.: Semi-supervised multi-scale attention-aware graph convolution network for intelligent fault diagnosis of machine under extremely-limited labeled samples. J. Manuf. Syst. 64, 561\u2013577 (2022)","journal-title":"J. Manuf. Syst."},{"issue":"2","key":"4847_CR2","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1007\/s11760-023-02846-y","volume":"18","author":"D Li","year":"2024","unstructured":"Li, D., Li, M., Yang, L., Wang, X., Zhang, F., Liang, Y.: Rolling bearing fault diagnosis in strong noise background based on vibration signals. SIViP 18(2), 1295\u20131303 (2024)","journal-title":"SIViP"},{"issue":"7","key":"4847_CR3","doi-asserted-by":"publisher","first-page":"5306","DOI":"10.1007\/s10489-024-05429-7","volume":"54","author":"P Ding","year":"2024","unstructured":"Ding, P., Xu, Y., Qin, P., Sun, X.-M.: A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples. Appl. Intell. 54(7), 5306\u20135316 (2024)","journal-title":"Appl. Intell."},{"issue":"8","key":"4847_CR4","doi-asserted-by":"publisher","first-page":"8430","DOI":"10.1109\/TIE.2021.3108726","volume":"69","author":"Z Wang","year":"2022","unstructured":"Wang, Z., He, X., Yang, B., Li, N.: Subdomain adaptation transfer learning network for fault diagnosis of roller bearings. IEEE Trans. Ind. Electron. 69(8), 8430\u20138439 (2022)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"18","key":"4847_CR5","doi-asserted-by":"publisher","first-page":"21211","DOI":"10.1007\/s10489-023-04665-7","volume":"53","author":"J Feng","year":"2023","unstructured":"Feng, J., Bao, S., Xu, X., Zhang, Z., Hou, P., Steyskal, F., Dustdar, S.: Rotating machinery fault diagnosis based on feature extraction via an unsupervised graph neural network. Appl. Intell. 53(18), 21211\u201321226 (2023)","journal-title":"Appl. Intell."},{"issue":"5","key":"4847_CR6","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/41.873208","volume":"47","author":"KA Loparo","year":"2000","unstructured":"Loparo, K.A., Adams, M.L., Lin, W., Abdel-Magied, M.F., Afshari, N.: Fault detection and diagnosis of rotating machinery. IEEE Trans. Ind. Electron. 47(5), 1005\u20131014 (2000)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"1","key":"4847_CR7","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TMECH.2017.2728371","volume":"23","author":"M Xia","year":"2018","unstructured":"Xia, M., Li, T., Xu, L., Liu, L., Silva, C.W.: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE\/ASME Trans. Mechatron. 23(1), 101\u2013110 (2018)","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"4847_CR8","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","volume":"77","author":"H Liu","year":"2018","unstructured":"Liu, H., Zhou, J., Zheng, Y., Jiang, W., Zhang, Y.: Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans. 77, 167\u2013178 (2018)","journal-title":"ISA Trans."},{"issue":"7","key":"4847_CR9","doi-asserted-by":"publisher","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","volume":"65","author":"L Wen","year":"2018","unstructured":"Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans. Ind. Electron. 65(7), 5990\u20135998 (2018)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"4847_CR10","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.isatra.2021.11.024","volume":"128","author":"S Gao","year":"2022","unstructured":"Gao, S., Xu, L., Zhang, Y., Pei, Z.: Rolling bearing fault diagnosis based on ssa optimized self-adaptive dbn. ISA Trans. 128, 485\u2013502 (2022)","journal-title":"ISA Trans."},{"key":"4847_CR11","first-page":"1","volume":"70","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Dong, Y., Zhou, H., Tang, G.: Deep dynamic adaptive transfer network for rolling bearing fault diagnosis with considering cross-machine instance. IEEE Trans. Instrum. Meas. 70, 1\u201311 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4847_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104932","volume":"113","author":"Q Yao","year":"2022","unstructured":"Yao, Q., Qian, Q., Qin, Y., Guo, L., Wu, F.: Adversarial domain adaptation network with pseudo-siamese feature extractors for cross-bearing fault transfer diagnosis. Eng. Appl. Artif. Intell. 113, 104932 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4847_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2023.3234142","volume-title":"Cross-machine transfer fault diagnosis by ensemble weighting subdomain adaptation network","author":"Q Qian","year":"2023","unstructured":"Qian, Q., Qin, Y., Luo, J., Xiao, D.: Cross-machine transfer fault diagnosis by ensemble weighting subdomain adaptation network. IEEE Trans. Ind, Electron (2023)"},{"issue":"2","key":"4847_CR14","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1007\/s10489-021-02504-1","volume":"52","author":"Z Tang","year":"2022","unstructured":"Tang, Z., Bo, L., Liu, X., Wei, D.: A semi-supervised transferable lstm with feature evaluation for fault diagnosis of rotating machinery. Appl. Intell. 52(2), 1703\u20131717 (2022)","journal-title":"Appl. Intell."},{"key":"4847_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102033","volume":"57","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Xia, J., Li, J., Chen, J., Huang, R., Jin, G., Li, W.: Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network. Adv. Eng. Inform. 57, 102033 (2023)","journal-title":"Adv. Eng. Inform."},{"key":"4847_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102703","volume":"62","author":"C Jian","year":"2024","unstructured":"Jian, C., Peng, Y., Mo, G., Chen, H.: Open-set domain generalization for fault diagnosis through data augmentation and a dual-level weighted mechanism. Adv. Eng. Inform. 62, 102703 (2024)","journal-title":"Adv. Eng. Inform."},{"issue":"6","key":"4847_CR17","doi-asserted-by":"publisher","first-page":"3904","DOI":"10.1177\/14759217241230129","volume":"23","author":"C Jian","year":"2024","unstructured":"Jian, C., Chen, H., Zhong, C., Ao, Y., Mo, G.: Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions. Struct. Health Monit. 23(6), 3904\u20133920 (2024)","journal-title":"Struct. Health Monit."},{"key":"4847_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102878","volume":"62","author":"C Jian","year":"2024","unstructured":"Jian, C., Chen, H., Ao, Y., Zhang, X.: A two-stage learning framework for imbalanced semi-supervised domain generalization fault diagnosis under unknown operating conditions. Adv. Eng. Inform. 62, 102878 (2024)","journal-title":"Adv. Eng. Inform."},{"issue":"1","key":"4847_CR19","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TNN.2005.860843","volume":"17","author":"C Wang","year":"2006","unstructured":"Wang, C., Hill, D.J.: Learning from neural control. IEEE Trans. Neural Netw. 17(1), 130\u2013146 (2006)","journal-title":"IEEE Trans. Neural Netw."},{"key":"4847_CR20","doi-asserted-by":"crossref","unstructured":"Wang, C., Hill, D.J.: Deterministic Learning Theory for Identification, Recognition, and Control. CRC Press, (2018)","DOI":"10.1201\/9781315221755"},{"key":"4847_CR21","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.neucom.2019.05.044","volume":"358","author":"W Wu","year":"2019","unstructured":"Wu, W., Wang, C., Yuan, C.: Deterministic learning from sampling data. Neurocomputing 358, 456\u2013466 (2019)","journal-title":"Neurocomputing"},{"key":"4847_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-019-2878-y","volume":"64","author":"W Wu","year":"2021","unstructured":"Wu, W., Wang, Q., Yuan, C., Wang, C.: Rapid dynamical pattern recognition for sampling sequences. Sci. China Inf. Sci. 64, 1\u201318 (2021)","journal-title":"Sci. China Inf. Sci."},{"key":"4847_CR23","volume-title":"New results on rapid dynamical pattern recognition via deterministic learning from sampling sequences","author":"W Wu","year":"2023","unstructured":"Wu, W., Hu, J., Zhang, F., Wang, C.: New results on rapid dynamical pattern recognition via deterministic learning from sampling sequences. IEEE Trans. Neural Netw. Learn, Syst (2023)"},{"key":"4847_CR24","volume-title":"Human gait recognition based on frontal-view sequences using gait dynamics and deep learning","author":"M Deng","year":"2023","unstructured":"Deng, M., Fan, Z., Lin, P., Feng, X.: Human gait recognition based on frontal-view sequences using gait dynamics and deep learning. IEEE Trans, Multimedia (2023)"},{"key":"4847_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2017.02.056","volume":"240","author":"X Dong","year":"2017","unstructured":"Dong, X., Wang, C., Si, W.: Ecg beat classification via deterministic learning. Neurocomputing 240, 1\u201312 (2017)","journal-title":"Neurocomputing"},{"issue":"3\u20135","key":"4847_CR26","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1002\/acs.2326","volume":"28","author":"T Chen","year":"2014","unstructured":"Chen, T., Wang, C.: Rapid isolation of small oscillation faults via deterministic learning. Int. J. Adapt. Control Signal Process. 28(3\u20135), 366\u2013385 (2014)","journal-title":"Int. J. Adapt. Control Signal Process."},{"key":"4847_CR27","volume-title":"Matching pursuit network: An interpretable sparse time-frequency representation method toward mechanical fault diagnosis","author":"H Lin","year":"2024","unstructured":"Lin, H., Huang, X., Chen, Z., He, G., Xi, C., Li, W.: Matching pursuit network: An interpretable sparse time-frequency representation method toward mechanical fault diagnosis. IEEE Trans. Neural Netw. Learn, Syst (2024)"},{"issue":"10","key":"4847_CR28","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"4847_CR29","doi-asserted-by":"crossref","unstructured":"Guo, J., Wu, W., Wang, C.: A novel bearing fault diagnosis method based on the dlm-cnn framework. In: Proc. 2023 38th Youth Acad. Annu. Conf. Chin. Assoc. Autom. (YAC), pp. 370\u2013374 (2023). IEEE","DOI":"10.1109\/YAC59482.2023.10401580"},{"issue":"59","key":"4847_CR30","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"4847_CR31","unstructured":"Loparo, K.A.: Bearings vibration data set Case Western Reserve University. https:\/\/engineering.case.edu\/bearingdatacenter"},{"issue":"4\u20135","key":"4847_CR32","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.jsv.2005.03.007","volume":"289","author":"H Qiu","year":"2006","unstructured":"Qiu, H., Lee, J., Lin, J., Yu, G.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J. Sound Vib. 289(4\u20135), 1066\u20131090 (2006)","journal-title":"J. Sound Vib."},{"key":"4847_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107539","volume":"156","author":"T Hu","year":"2020","unstructured":"Hu, T., Tang, T., Lin, R., Chen, M., Han, S., Wu, J.: A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions. Measurement 156, 107539 (2020)","journal-title":"Measurement"},{"issue":"2","key":"4847_CR34","doi-asserted-by":"publisher","first-page":"425","DOI":"10.3390\/s17020425","volume":"17","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Peng, G., Li, C., Chen, Y., Zhang, Z.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 425 (2017)","journal-title":"Sensors"},{"key":"4847_CR35","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","volume":"100","author":"W Zhang","year":"2018","unstructured":"Zhang, W., Li, C., Peng, G., Chen, Y., Zhang, Z.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439\u2013453 (2018)","journal-title":"Mech. Syst. Signal Process."},{"key":"4847_CR36","volume-title":"Deep multi-adversarial conditional domain adaptation networks for fault diagnostics of industrial equipment","author":"B Wang","year":"2022","unstructured":"Wang, B., Baraldi, P., Zio, E.: Deep multi-adversarial conditional domain adaptation networks for fault diagnostics of industrial equipment. IEEE Trans. Ind, Inform (2022)"},{"key":"4847_CR37","doi-asserted-by":"crossref","unstructured":"Shen, J., Qu, Y., Zhang, W., Yu, Y.: Wasserstein distance guided representation learning for domain adaptation. In: Proc. AAAI Conf. Artif. Intell., vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"4847_CR38","unstructured":"Chen, X., Wang, S., Long, M., Wang, J.: Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In: Proc. Int. Conf. Mach. Learn., pp. 1081\u20131090 (2019). PMLR"},{"key":"4847_CR39","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Proc. 32nd Int. Conf. Neural Inf. Process. Syst., pp. 1647\u20131657 (2018)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04847-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04847-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04847-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T18:59:10Z","timestamp":1761418750000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04847-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,3]]},"references-count":39,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4847"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04847-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,3]]},"assertion":[{"value":"14 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"There is no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1248"}}