{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:52:09Z","timestamp":1777499529420,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076215"],"award-info":[{"award-number":["62076215"]}],"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":["61502411"],"award-info":[{"award-number":["61502411"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"New Generation Information Technology Innovation Project of the Ministry of Education of China","award":["2020ITA02057"],"award-info":[{"award-number":["2020ITA02057"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious. Besides, parameters of target domain feature extractor gradually approach that of source domain feature extractor to cheat discriminator which results in similar feature distribution of source domain and target domain. These issues make it difficult for the domain adaptation-based bearing fault diagnosis methods to achieve satisfactory performance. An unsupervised domain adaptive bearing fault diagnosis method based on maximum domain discrepancy (UDA-BFD-MDD) is proposed in this paper. In UDA-BFD-MDD, maximum domain discrepancy is exploited to maximize the feature difference between the source domain and target domain, while the output feature of target domain feature extractor can cheat the discriminator. The performance of UDA-BFD-MDD is verified through comprehensive experiments using the bearing dataset of Case Western Reserve University. The experimental results demonstrate that UDA-BFD-MDD is more stable during training process and can achieve higher accuracy rate.<\/jats:p>","DOI":"10.1186\/s13634-023-01107-x","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T16:02:28Z","timestamp":1704988948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy"],"prefix":"10.1186","volume":"2024","author":[{"given":"Cuixiang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Shengkai","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Shao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"issue":"2","key":"1107_CR1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCOM.2018.1700629","volume":"56","author":"J Wan","year":"2018","unstructured":"J. Wan, B. Chen, M. Imran, F. Tao, D. Li, C. Liu, S. Ahmad, Toward dynamic resources management for IoT-based manufacturing. IEEE Commun. Mag. 56(2), 52\u201359 (2018)","journal-title":"IEEE Commun. Mag."},{"issue":"6","key":"1107_CR2","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.eng.2020.07.017","volume":"7","author":"B Wang","year":"2021","unstructured":"B. Wang, F. Tao, X. Fang, C. Liu, Y. Liu, T. Freiheit, Smart manufacturing and intelligent manufacturing: a comparative review. Engineering 7(6), 738\u2013757 (2021)","journal-title":"Engineering"},{"issue":"4","key":"1107_CR3","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/TEC.2005.847955","volume":"20","author":"S Nandi","year":"2005","unstructured":"S. Nandi, H.A. Toliyat, X. Li, Condition monitoring and fault diagnosis of electrical motors: a review. IEEE Trans. Energy Convers. 20(4), 719\u2013729 (2005)","journal-title":"IEEE Trans. Energy Convers."},{"key":"1107_CR4","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cirpj.2022.11.004","volume":"40","author":"P Nunes","year":"2023","unstructured":"P. Nunes, J. Santos, E. Rocha, Challenges in predictive maintenance: a review. CIRP J. Manuf. Sci. Technol. 40, 53\u201367 (2023)","journal-title":"CIRP J. Manuf. Sci. Technol."},{"issue":"2","key":"1107_CR5","doi-asserted-by":"publisher","first-page":"2602","DOI":"10.1109\/JSYST.2022.3193200","volume":"17","author":"H Wang","year":"2023","unstructured":"H. Wang, W. Zhang, D. Yang, Y. Xiang, Deep-learning-enabled predictive maintenance in industrial internet of things: methods, applications, and challenges. IEEE Syst. J. 17(2), 2602\u20132615 (2023)","journal-title":"IEEE Syst. J."},{"issue":"10","key":"1107_CR6","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.comcom.2022.07.034","volume":"194","author":"J Jiang","year":"2022","unstructured":"J. Jiang, F. Liu, Y. Liu, Q. Tang, B. Wang, G. Zhong, W. Wang, A dynamic ensemble algorithm for anomaly detection in IoT imbalanced data streams. Comput. Commun. 194(10), 250\u2013257 (2022)","journal-title":"Comput. Commun."},{"issue":"6","key":"1107_CR7","doi-asserted-by":"publisher","first-page":"e17584","DOI":"10.1016\/j.heliyon.2023.e17584","volume":"9","author":"O Das","year":"2023","unstructured":"O. Das, D.B. Das, D. Birant, Machine learning for fault analysis in rotating machinery: a comprehensive review. Heliyon 9(6), e17584 (2023)","journal-title":"Heliyon"},{"issue":"3","key":"1107_CR8","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1109\/TGCN.2022.3151716","volume":"6","author":"J Jiang","year":"2022","unstructured":"J. Jiang, F. Liu, W.W.Y. Ng, Q. Tang, W. Wang, Q.-V. Pham, Dynamic incremental ensemble fuzzy classifier for data streams in green internet of things. IEEE Trans. Green Commun. Netw. 6(3), 1316\u20131329 (2022)","journal-title":"IEEE Trans. Green Commun. Netw."},{"issue":"2","key":"1107_CR9","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.future.2020.09.019","volume":"115","author":"Y Ren","year":"2021","unstructured":"Y. Ren, Y. Leng, J. Qi, P.K. Sharma, J. Wang, Z. Almakhadmeh, A. Tolba, Multiple cloud storage mechanism based on blockchain in smart homes. Future Gener. Comput. Syst. 115(2), 304\u2013313 (2021)","journal-title":"Future Gener. Comput. Syst."},{"issue":"2019","key":"1107_CR10","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.neucom.2018.06.078","volume":"335","author":"D-T Hoang","year":"2019","unstructured":"D.-T. Hoang, H.-J. Kang, A survey on deep learning based bearing fault diagnosis. Neurocomputing 335(2019), 327\u2013335 (2019)","journal-title":"Neurocomputing"},{"key":"1107_CR11","doi-asserted-by":"publisher","first-page":"112346","DOI":"10.1016\/j.measurement.2022.112346","volume":"206","author":"Z Zhu","year":"2023","unstructured":"Z. Zhu, Y. Lei, G. Qi, Y. Chai, N. Mazur, Y. An, X. Huang, A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement 206, 112346 (2023)","journal-title":"Measurement"},{"issue":"9","key":"1107_CR12","first-page":"1","volume":"217","author":"L Zhang","year":"2022","unstructured":"L. Zhang, J. Wang, W. Wang, Z. Jin, Su. Yansen, H. Chen, Smart contract vulnerability detection combined with multi-objective detection. Comput. Netw. 217(9), 1\u201313 (2022)","journal-title":"Comput. Netw."},{"issue":"4","key":"1107_CR13","doi-asserted-by":"publisher","first-page":"101945","DOI":"10.1016\/j.asej.2022.101945","volume":"14","author":"M Hakim","year":"2023","unstructured":"M. Hakim, A.A. Borhana Omran, A.N. Ahmed, M. Al-Waily, A. Abdellatif, A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: taxonomy, overview, application, open challenges, weaknesses and recommendations. Ain Shams Eng. J. 14(4), 101945 (2023)","journal-title":"Ain Shams Eng. J."},{"issue":"10","key":"1107_CR14","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1107_CR15","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","volume":"122","author":"B Yang","year":"2019","unstructured":"B. Yang, Y. Lei, F. Jia, S. Xing, An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech. Syst. Signal Process. 122, 692\u2013706 (2019)","journal-title":"Mech. Syst. Signal Process."},{"key":"1107_CR16","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.sigpro.2018.12.005","volume":"157","author":"X Li","year":"2019","unstructured":"X. Li, W. Zhang, Q. Ding, J.-Q. Sun, Multi-layer domain adaptation method for rolling bearing fault diagnosis. Signal Process. 157, 180\u2013197 (2019)","journal-title":"Signal Process."},{"issue":"1\u20132","key":"1107_CR17","first-page":"1","volume":"2020","author":"BR Yang","year":"2020","unstructured":"B.R. Yang, Q. Li, L. Chen, C.Q. Shen, Bearing fault diagnosis based on multilayer domain adaptation. Shock. Vib. 2020(1\u20132), 1\u201311 (2020)","journal-title":"Shock. Vib."},{"issue":"7","key":"1107_CR18","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TSMC.2019.2932000","volume":"51","author":"ZH Liu","year":"2021","unstructured":"Z.H. Liu, B.L. Lu, H.L. Wei, L. Chen, X.H. Li, M. R\u00e4tsch, Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Trans. Syst. Man Cybern. Syst. 51(7), 4217\u20134226 (2021)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"1","key":"1107_CR19","doi-asserted-by":"publisher","first-page":"320","DOI":"10.3390\/s20010320","volume":"20","author":"X Wang","year":"2020","unstructured":"X. Wang, F. Liu, Triplet loss guided adversarial domain adaptation for bearing fault diagnosis. Sensors 20(1), 320 (2020)","journal-title":"Sensors"},{"issue":"3","key":"1107_CR20","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","volume":"32","author":"VM Patel","year":"2015","unstructured":"V.M. Patel, R. Gopalan, R. Li, R. Chellappa, Visual domain adaptation: a survey of recent advances. IEEE Signal Process. Mag. 32(3), 53\u201369 (2015)","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"1107_CR21","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"S.J. Pan, I.W. Tsang, J.T. Kwok, Q. Yang, Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199\u2013210 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"59","key":"1107_CR22","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky, Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"1107_CR23","unstructured":"H. Zhao, R.T.D. Combes, K. Zhang, G.J. Gordon, On learning invariant representations for domain adaptation, in: Proceedings of International Conference on Machine Learning (PMLR 2019), pp. 7523\u20137532 (2019)."},{"issue":"3","key":"1107_CR24","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"A. Gretton, K.M. Borgwardt, M.J. Rasch, B. Sch\u00f6lkopf, A. Smola, A kernel two-sample test. J. Mach. Learn. Res. 13(3), 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"1107_CR25","unstructured":"M. Long, Y. Cao, J. Wang, M.I. Jordan, Learning transferable features with deep adaptation networks, in: Proceedings of International Conference on Machine Learning (PMLR, 2015), pp. 97\u2013105 (2015)."},{"issue":"9","key":"1107_CR26","doi-asserted-by":"publisher","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","volume":"66","author":"L Guo","year":"2018","unstructured":"L. Guo, Y. Lei, S. Xing, T. Yan, N. Li, 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."},{"key":"1107_CR27","doi-asserted-by":"crossref","unstructured":"B. Sun, K. Saenko, Deep coral: correlation alignment for deep domain adaptation, in: Proceedings of European Conference on Computer Vision, (2016), pp. 443\u2013450.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"1107_CR28","unstructured":"I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Proceedings of the 27th International Conference on Neural Information Processing (NIPS 2014), (2014), pp. 2672\u20132680."},{"key":"1107_CR29","unstructured":"H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)."},{"key":"1107_CR30","doi-asserted-by":"crossref","unstructured":"E. Tzeng, J. Hoffman, K. Saenko, T. Darrell, Adversarial discriminative domain adaptation, in: The Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 2962\u20132971 2017.","DOI":"10.1109\/CVPR.2017.316"},{"key":"1107_CR31","unstructured":"E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, T. Darrell, Deep domain confusion: maximizing for domain invariance. arXiv 2014, arXiv:1412.3474."},{"key":"1107_CR32","unstructured":"G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, arXiv preprint arXiv:1207.0580, 2012."}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-023-01107-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-023-01107-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-023-01107-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T16:09:08Z","timestamp":1704989348000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-023-01107-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,11]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1107"],"URL":"https:\/\/doi.org\/10.1186\/s13634-023-01107-x","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,11]]},"assertion":[{"value":"18 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11"}}