{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:36:38Z","timestamp":1776144998341,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 51775452"],"award-info":[{"award-number":["No. 51775452"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11760-025-03847-9","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T10:52:29Z","timestamp":1739443949000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Non-local adaptive network for cross-domain intelligent fault diagnosis leveraging multi-source IOT data"],"prefix":"10.1007","volume":"19","author":[{"given":"Hanshu","family":"Shao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingbo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hengkai","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiying","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongli","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"issue":"1","key":"3847_CR1","doi-asserted-by":"crossref","first-page":"21996","DOI":"10.1038\/s41598-022-26316-6","volume":"12","author":"J Zhao","year":"2022","unstructured":"Zhao, J., et al.: Research on an intelligent diagnosis method of mechanical faults for small sample data sets. Sci. Rep. 12(1), 21996 (2022)","journal-title":"Sci. Rep."},{"key":"3847_CR2","doi-asserted-by":"crossref","unstructured":"Meng, W., et al.: Intelligent fault diagnosis of mechanical engineering using NLF-LSTM optimized deep learning model. Optim. Eng. 1\u201322 (2024)","DOI":"10.1007\/s11081-024-09904-5"},{"issue":"3","key":"3847_CR3","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s11063-021-10719-z","volume":"54","author":"C Qian","year":"2022","unstructured":"Qian, C., et al.: Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge. Neural Process. Lett. 54(3), 2509\u20132531 (2022)","journal-title":"Neural Process. Lett."},{"key":"3847_CR4","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-99-8850-1","volume":"133","author":"T Feng","year":"2024","unstructured":"Feng, T., et al.: A new nonlinear ensemble framework based on dynamic-matched weights for tool remaining useful life prediction. Eng. Appl. Artif. Intell. 133, 108002 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3847_CR5","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102206","volume":"58","author":"A Kumar","year":"2023","unstructured":"Kumar, A., et al.: Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (RUL) of bearing. Adv. Eng. Inform. 58, 102206 (2023)","journal-title":"Adv. Eng. Inform."},{"key":"3847_CR6","volume":"183","author":"Y Tan","year":"2021","unstructured":"Tan, Y., et al.: MiDAN: a framework for cross-domain intelligent fault diagnosis with imbalanced datasets. Measurement 183, 109834 (2021)","journal-title":"Measurement"},{"issue":"2","key":"3847_CR7","volume":"32","author":"J Ma","year":"2020","unstructured":"Ma, J., et al.: An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction. Meas. Sci. Technol. 32(2), 025103 (2020)","journal-title":"Meas. Sci. Technol."},{"issue":"5","key":"3847_CR8","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1016\/S0888-3270(03)00077-3","volume":"18","author":"X Lou","year":"2004","unstructured":"Lou, X., Loparo, K.A.: Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. Signal Process. 18(5), 1077\u20131095 (2004)","journal-title":"Mech. Syst. Signal Process."},{"key":"3847_CR9","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.101877","volume":"55","author":"D Ruan","year":"2023","unstructured":"Ruan, D., et al.: CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv. Eng. Inform. 55, 101877 (2023)","journal-title":"Adv. Eng. Inform."},{"issue":"5","key":"3847_CR10","volume":"31","author":"S Gao","year":"2020","unstructured":"Gao, S., et al.: Rolling bearing fault diagnosis based on intelligent optimized self-adaptive deep belief network. Meas. Sci. Technol. 31(5), 055009 (2020)","journal-title":"Meas. Sci. Technol."},{"issue":"1","key":"3847_CR11","volume":"36","author":"L Wan","year":"2024","unstructured":"Wan, L., et al.: Efficient cross-domain fault diagnosis via distributed multi-source domain deep transfer learning. Meas. Sci. Technol. 36(1), 016165 (2024)","journal-title":"Meas. Sci. Technol."},{"key":"3847_CR12","volume":"299","author":"L Wan","year":"2024","unstructured":"Wan, L., et al.: A novel meta-transfer learning approach via convolutional multi-head self-attention network for few-shot fault diagnosis. Knowl.-Based Syst. 299, 112113 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"3847_CR13","doi-asserted-by":"crossref","unstructured":"Long, M., et al.: Transfer joint matching for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410\u20131417 (2014)","DOI":"10.1109\/CVPR.2014.183"},{"key":"3847_CR14","first-page":"1","volume":"70","author":"Y Tan","year":"2020","unstructured":"Tan, Y., et al.: Deep coupled joint distribution adaptation network: a method for intelligent fault diagnosis between artificial and real damages. IEEE Trans. Instrum. Meas. 70, 1\u201312 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3847_CR15","doi-asserted-by":"crossref","unstructured":"Long, M., et al.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200\u20132207 (2013)","DOI":"10.1109\/ICCV.2013.274"},{"key":"3847_CR16","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2015.05.140","volume":"195","author":"J Yang","year":"2016","unstructured":"Yang, J., et al.: Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. Neurocomputing 195, 88\u201395 (2016)","journal-title":"Neurocomputing"},{"key":"3847_CR17","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"issue":"3","key":"3847_CR18","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TCDS.2022.3232569","volume":"15","author":"J Gou","year":"2022","unstructured":"Gou, J., et al.: Channel-correlation-based selective knowledge distillation. IEEE Trans. Cogn. Dev. Syst. 15(3), 1574\u20131585 (2022)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"3847_CR19","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isatra.2019.08.012","volume":"97","author":"T Han","year":"2020","unstructured":"Han, T., et al.: Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application. ISA Trans. 97, 269\u2013281 (2020)","journal-title":"ISA Trans."},{"key":"3847_CR20","volume":"156","author":"Q Li","year":"2020","unstructured":"Li, Q., et al.: Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data. Measurement 156, 107570 (2020)","journal-title":"Measurement"},{"issue":"11","key":"3847_CR21","doi-asserted-by":"crossref","first-page":"8702","DOI":"10.1109\/TIM.2020.2995441","volume":"69","author":"Z Chen","year":"2020","unstructured":"Chen, Z., et al.: Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery. IEEE Trans. Instrum. Meas. 69(11), 8702\u20138712 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3847_CR22","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106236","volume":"205","author":"J Jiao","year":"2020","unstructured":"Jiao, J., et al.: Double-level adversarial domain adaptation network for intelligent fault diagnosis. Knowl.-Based Syst. 205, 106236 (2020)","journal-title":"Knowl.-Based Syst."},{"issue":"9","key":"3847_CR23","doi-asserted-by":"crossref","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","volume":"66","author":"L Guo","year":"2018","unstructured":"Guo, L., et al.: Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Trans. Ind. Electron. 66(9), 7316\u20137325 (2018)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"3","key":"3847_CR24","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TCDS.2022.3232569","volume":"15","author":"J Gou","year":"2022","unstructured":"Gou, J., et al.: Channel-correlation-based selective knowledge distillation. IEEE Trans. Cogn. Dev. Syst. 15(3), 1574\u20131585 (2022)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"3847_CR25","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sigpro.2018.12.005","volume":"157","author":"X Li","year":"2019","unstructured":"Li, X., et al.: Multi-layer domain adaptation method for rolling bearing fault diagnosis. Signal Process. 157, 180\u2013197 (2019)","journal-title":"Signal Process."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03847-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-03847-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03847-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T01:06:14Z","timestamp":1743555974000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-03847-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":25,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["3847"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-03847-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]},"assertion":[{"value":"1 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 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":"Conflict of interest"}}],"article-number":"288"}}