{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T20:27:17Z","timestamp":1775248037253,"version":"3.50.1"},"reference-count":138,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2939876","type":"journal-article","created":{"date-parts":[[2019,9,6]],"date-time":"2019-09-06T15:57:31Z","timestamp":1567785451000},"page":"129260-129290","source":"Crossref","is-referenced-by-count":185,"title":["Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0391-4679","authenticated-orcid":false,"given":"Huailiang","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Rixin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuantao","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0844-4418","authenticated-orcid":false,"given":"Jiancheng","family":"Yin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2699-9951","authenticated-orcid":false,"given":"Yongbo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuqing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Minqiang","family":"Xu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0696-6"},{"key":"ref38","first-page":"2168","article-title":"Robust visual domain adaptation with low-rank reconstruction","author":"jhuo","year":"2012","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.24"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.421"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2479456"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33765-9_45"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"ref34","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"gong","year":"2012","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1155"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273521"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557130"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2347059"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_1"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"mei","year":"2018","journal-title":"Neurocomputing"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2018.2840738"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890566"},{"key":"ref26","first-page":"264","article-title":"Instance weighting for domain adaptation in NLP","author":"jiang","year":"2007","journal-title":"Proc ACL"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2752151"},{"key":"ref25","first-page":"601","article-title":"Correcting sample selection bias by unlabeled data","author":"huang","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref50","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868685"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref58","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00145"},{"key":"ref56","article-title":"Unsupervised cross-domain image generation","author":"taigman","year":"2016","journal-title":"arXiv 1611 02200"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref54","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref52","first-page":"4119","article-title":"Supervised representation learning: Transfer learning with deep autoencoders","author":"zhuang","year":"2015","journal-title":"Proc Int Conf Artif Intell"},{"key":"ref40","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2005.09.012"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2308133"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.050"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.02.016"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.04.069"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2612821"},{"key":"ref7","article-title":"Machine learning and deep learning algorithms for bearing fault diagnostics—A comprehensive review","author":"zhang","year":"2019","journal-title":"arXiv 1901 08247"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2017.2726011"},{"key":"ref46","first-page":"1831","article-title":"Kernelized Bayesian transfer learning","author":"g\u00f6nen","year":"2014","journal-title":"Proc AAAI Nat Conf Artificial Intell"},{"key":"ref45","first-page":"827","article-title":"Information-theoretic transfer learning framework for Bayesian optimisation","author":"ramachandran","year":"2018","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299016"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.114"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/1291233.1291276"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.111"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.57"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916935"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169536"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.03.005"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Chongqing.2018.00180"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.3390\/s17020425"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CCDC.2017.7978168"},{"key":"ref129","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3901\/JME.2019.07.001","article-title":"Deep transfer diagnosis method for machinery in big data era","volume":"55","author":"lei","year":"2019","journal-title":"J Mech Eng"},{"key":"ref71","first-page":"534","article-title":"Application of transfer factor analysis in gearbox fault diagnosis under various working condition","volume":"30","author":"xie","year":"2016","journal-title":"J Electron Meas Instrum"},{"key":"ref128","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.02.075"},{"key":"ref76","article-title":"Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition","author":"zhang","year":"2018","journal-title":"arXiv 1805 00778"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-018-00048-y"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6714520"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.06.022"},{"key":"ref75","article-title":"Bearing fault diagnosis under varying working condition based on domain adaptation","author":"zhang","year":"2017","journal-title":"arXiv 1707 09890"},{"key":"ref133","first-page":"1","article-title":"Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification","author":"lessmeier","year":"2016","journal-title":"Proc Eur Conf Prognostics Health Manage Soc"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.10.010"},{"key":"ref131","year":"2019","journal-title":"CWRU Dataset"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2878491"},{"key":"ref132","year":"2019","journal-title":"MFPT Dataset"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05090-0_16"},{"key":"ref136","first-page":"1","article-title":"PRONOSTIA: An experimental platform for bearings accelerated degradation tests","author":"nectoux","year":"2012","journal-title":"Proc IEEE Int Conf Prognostics Health Manage"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2004.08.001"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2881543"},{"key":"ref137","year":"2019","journal-title":"PHM09 Gearbox Dataset"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2016.7542845"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2883078"},{"key":"ref61","first-page":"104","article-title":"Gearbox diagnosis based on auxiliary monitoring datasets of different working conditions","volume":"36","author":"duan","year":"2017","journal-title":"Shock Vib"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2880770"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.02.073"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.04.010"},{"key":"ref66","article-title":"Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis","author":"cheng","year":"2019","journal-title":"arXiv 1903 06753"},{"key":"ref67","first-page":"764","article-title":"Fault diagnosis method of a rolling bearing under variable working conditions based on feature transfer learning","volume":"39","author":"kang","year":"2019","journal-title":"Proc CSEE"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1177\/0954406219840381"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/793161"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.3390\/app8122357"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2013.06.004"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2019.03.017"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.02.013"},{"key":"ref108","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.measurement.2019.01.022","article-title":"A novel gas turbine fault diagnosis method based on transfer learning with CNN","volume":"137","author":"shi-sheng","year":"2019","journal-title":"Measurement"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2877090"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.3390\/s19081826"},{"key":"ref93","first-page":"13","article-title":"1D CNN-based transfer learning model for bearing fault diagnosis under variable working conditions","author":"hasan","year":"2018","journal-title":"Proc CIS"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.3390\/designs2040056"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2906273"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04097-w"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAIS.2018.8570702"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2864759"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2720965"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2837621"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.06.025"},{"key":"ref111","article-title":"Domain adaptation in robot fault diagnostic systems","author":"mahyari","year":"2018","journal-title":"arXiv 1809 08626"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.021"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/CAC.2018.8623158"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.12.051"},{"key":"ref99","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neucom.2018.03.014","article-title":"ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis","volume":"294","author":"yuanhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2898619"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/SDPC.2018.8664814"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2013.10.041"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.025"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2017.03.017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2016.05.027"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2774777"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2013.01.017"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2903295"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2301773"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2868023"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2896370"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref81","first-page":"1","article-title":"Bearing fault diagnosis based on SVD feature extraction and transfer learning classification","author":"shen","year":"2015","journal-title":"Proc IEEE Int Conf Prognostics Health Manage"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3291124"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2018.12.005"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.07.048"},{"key":"ref19","article-title":"Transfer adaptation learning: A decade survey","author":"zhang","year":"2019","journal-title":"arXiv 1903 0468"},{"key":"ref83","first-page":"118","article-title":"Application of SVD and transfer learning strategy on motor fault diagnosis","volume":"30","author":"shen","year":"2017","journal-title":"J Vib Eng"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/CAC.2018.8623346"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2899118"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.050"},{"key":"ref80","first-page":"33","article-title":"Enhanced least squares support vector machine-based transfer learning strategy for bearing fault diagnosis","volume":"38","author":"chen","year":"2017","journal-title":"Chin J Sci Instrum"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909267"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.12.019"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2627020"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.02.080"},{"key":"ref122","first-page":"67","article-title":"Convolutional neural networks for fault diagnosis using rotating speed normalized vibration","author":"wei","year":"2018","journal-title":"Proc 3rd Int Conf Condition Monit Mach Non-Stationary Oper"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905264"},{"key":"ref85","article-title":"Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application","author":"han","year":"2018","journal-title":"arXiv 1804 07265"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Paris.2019.00054"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2902003"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2754287"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08826442.pdf?arnumber=8826442","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:31:12Z","timestamp":1641987072000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8826442\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":138,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2939876","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}