{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:36:05Z","timestamp":1775838965335,"version":"3.50.1"},"reference-count":164,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10462-022-10230-4","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T08:03:50Z","timestamp":1660637030000},"page":"2871-2922","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["A survey of transfer learning for machinery diagnostics and prognostics"],"prefix":"10.1007","volume":"56","author":[{"given":"Siya","family":"Yao","sequence":"first","affiliation":[]},{"given":"Qi","family":"Kang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5408-8752","authenticated-orcid":false,"given":"MengChu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Muhyaddin J.","family":"Rawa","sequence":"additional","affiliation":[]},{"given":"Abdullah","family":"Abusorrah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"issue":"1","key":"10230_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3390\/data6010005","volume":"6","author":"M Arias Chao","year":"2021","unstructured":"Arias Chao M, Kulkarni C, Goebel K, Fink O (2021) Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Data 6(1):5","journal-title":"Data"},{"key":"10230_CR2","doi-asserted-by":"publisher","first-page":"103932","DOI":"10.1016\/j.mechmachtheory.2020.103932","volume":"151","author":"M Azamfar","year":"2020","unstructured":"Azamfar M, Li X, Lee J (2020) Intelligent ball screw fault diagnosis using a deep domain adaptation methodology. Mech Mach Theory 151:103932","journal-title":"Mech Mach Theory"},{"key":"10230_CR3","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M (2013) Unsupervised domain adaptation by domain invariant projection. In: Proceedings of the IEEE International conference on computer vision, pp 769\u2013776","DOI":"10.1109\/ICCV.2013.100"},{"key":"10230_CR4","doi-asserted-by":"crossref","unstructured":"Bole B, Kulkarni CS, Daigle M (2014) Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use. Inc., Moffett Field United States, Technical report, SGT","DOI":"10.36001\/phmconf.2014.v6i1.2490"},{"issue":"14","key":"10230_CR5","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt KM, Gretton A, Rasch MJ, Kriegel H-P, Sch\u00f6lkopf B, Smola AJ (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):49\u201357","journal-title":"Bioinformatics"},{"issue":"8","key":"10230_CR6","doi-asserted-by":"publisher","first-page":"3073","DOI":"10.1109\/TNNLS.2019.2935384","volume":"31","author":"G Cai","year":"2019","unstructured":"Cai G, Wang Y, He L, Zhou M (2019) Unsupervised domain adaptation with adversarial residual transform networks. IEEE Trans Neural Netw Learn Syst 31(8):3073\u20133086","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10230_CR7","unstructured":"Case Western Reserve University Bearing Data Center, CWRU Dataset. https:\/\/csegroups.case.edu\/bearingdatacenter"},{"key":"10230_CR8","doi-asserted-by":"crossref","unstructured":"Chai Z, Zhao C, Huang B (2021) Multisource-refined transfer network for industrial fault diagnosis under domain and category inconsistencies. IEEE Trans Cybernet","DOI":"10.1109\/TCYB.2021.3067786"},{"issue":"1","key":"10230_CR9","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/TII.2019.2917233","volume":"16","author":"Z Chen","year":"2019","unstructured":"Chen Z, Gryllias K, Li W (2019) Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network. IEEE Trans Ind Inform 16(1):339\u2013349","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR10","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1016\/j.renene.2020.10.121","volume":"163","author":"W Chen","year":"2021","unstructured":"Chen W, Qiu Y, Feng Y, Li Y, Kusiak A (2021) Diagnosis of wind turbine faults with transfer learning algorithms. Renew Energy 163:2053\u20132067","journal-title":"Renew Energy"},{"issue":"1","key":"10230_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s10033-020-00520-9","volume":"34","author":"C Chen","year":"2021","unstructured":"Chen C, Shen F, Xu J, Yan R (2021) Model parameter transfer for gear fault diagnosis under varying working conditions. Chin J Mech Eng 34(1):1\u201313","journal-title":"Chin J Mech Eng"},{"key":"10230_CR12","doi-asserted-by":"crossref","unstructured":"Chen Q, Liu Y, Wang Z, Wassell I, Chetty K (2018) Re-weighted adversarial adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7976\u20137985","DOI":"10.1109\/CVPR.2018.00832"},{"key":"10230_CR165","doi-asserted-by":"publisher","unstructured":"Chen C, Lu N, Jiang B, Wang C (2021) A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance. IEEE\/CAA J Autom Sin 8(2):412\u2013422. https:\/\/doi.org\/10.1109\/JAS.2021.1003835","DOI":"10.1109\/JAS.2021.1003835"},{"key":"10230_CR13","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neucom.2020.05.040","volume":"409","author":"C Cheng","year":"2020","unstructured":"Cheng C, Zhou B, Ma G, Wu D, Yuan Y (2020) Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data. Neurocomputing 409:35\u201345","journal-title":"Neurocomputing"},{"key":"10230_CR14","doi-asserted-by":"publisher","first-page":"106682","DOI":"10.1016\/j.ress.2019.106682","volume":"195","author":"PRDO da Costa","year":"2020","unstructured":"da Costa PRDO, Ak\u00e7ay A, Zhang Y, Kaymak U (2020) Remaining useful lifetime prediction via deep domain adaptation. Reliab Eng System Saf 195:106682","journal-title":"Reliab Eng System Saf"},{"key":"10230_CR15","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.ymssp.2018.10.010","volume":"120","author":"AP Daga","year":"2019","unstructured":"Daga AP, Fasana A, Marchesiello S, Garibaldi L (2019) The politecnico di torino rolling bearing test rig: Description and analysis of open access data. Mech Syst Signal Process 120:252\u2013273","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR16","doi-asserted-by":"crossref","unstructured":"Deebak B, Al-Turjman F (2021) Digital-twin assisted: fault diagnosis using deep transfer learning for machining tool condition. Int J Intell Syst","DOI":"10.1002\/int.22493"},{"key":"10230_CR17","doi-asserted-by":"publisher","first-page":"108601","DOI":"10.1016\/j.measurement.2020.108601","volume":"173","author":"M Deng","year":"2021","unstructured":"Deng M, Deng A, Zhu J, Shi Y, Liu Y (2021) Intelligent fault diagnosis of rotating components in the absence of fault data: a transfer-based approach. Measurement 173:108601","journal-title":"Measurement"},{"key":"10230_CR163","doi-asserted-by":"publisher","unstructured":"Deng Q, Kang Q, Zhang L, Zhou M, An J (2022) Objective Space-based Population Generation to Accelerate Evolutionary Algorithms for Large-scale Many-objective Optimization. IEEE Trans Evol Comput 1\u20131:9762228. https:\/\/doi.org\/10.1109\/TEVC.2022.3166815","DOI":"10.1109\/TEVC.2022.3166815"},{"key":"10230_CR18","first-page":"1","volume":"70","author":"Y Ding","year":"2021","unstructured":"Ding Y, Ding P, Jia M (2021) A novel remaining useful life prediction method of rolling bearings based on deep transfer auto-encoder. IEEE Trans Instrum Measure 70:1\u201312","journal-title":"IEEE Trans Instrum Measure"},{"key":"10230_CR19","first-page":"1","volume":"70","author":"Y Ding","year":"2021","unstructured":"Ding Y, Jia M, Cao Y (2021) Remaining useful life estimation under multiple operating conditions via deep subdomain adaptation. IEEE Trans Instrum Measure 70:1\u201311","journal-title":"IEEE Trans Instrum Measure"},{"key":"10230_CR20","doi-asserted-by":"publisher","first-page":"107583","DOI":"10.1016\/j.ress.2021.107583","volume":"212","author":"Y Ding","year":"2021","unstructured":"Ding Y, Jia M, Miao Q, Huang P (2021) Remaining useful life estimation using deep metric transfer learning for kernel regression. Reliab Eng Syst Saf 212:107583","journal-title":"Reliab Eng Syst Saf"},{"key":"10230_CR21","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.isatra.2021.03.042","volume":"121","author":"Y Dong","year":"2022","unstructured":"Dong Y, Li Y, Zheng H, Wang R, Xu M (2022) A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem. ISA Trans 121:327\u2013348","journal-title":"ISA Trans"},{"key":"10230_CR22","unstructured":"FEMTO-ST Institute, FEMTO Dataset. https:\/\/ti.arc.nasa.gov\/tech\/dash\/groups\/pcoe\/prognostic-data-repository\/#femto"},{"key":"10230_CR23","doi-asserted-by":"crossref","unstructured":"Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of the IEEE international conference on computer vision, pp 2960\u20132967","DOI":"10.1109\/ICCV.2013.368"},{"issue":"1","key":"10230_CR24","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2030\u20132096","journal-title":"J Mach Learn Res"},{"key":"10230_CR25","doi-asserted-by":"crossref","unstructured":"Ghifary M, Kleijn WB, Zhang M (2014) Domain adaptive neural networks for object recognition. In: Pacific Rim International Conference on Artificial Intelligence, pp 898\u2013904. Springer","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"10230_CR26","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on computer vision and pattern recognition, pp. 2066\u20132073. IEEE"},{"key":"10230_CR28","doi-asserted-by":"crossref","unstructured":"Gopalan R, Li R, Chellappa R (2011) Domain adaptation for object recognition: an unsupervised approach. In: 2011 International conference on computer vision, pp. 999\u20131006. IEEE","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"10230_CR29","first-page":"513","volume":"19","author":"A Gretton","year":"2006","unstructured":"Gretton A, Borgwardt K, Rasch M, Sch\u00f6lkopf B, Smola A (2006) A kernel method for the two-sample-problem. Adv Neural Inform Process Syst 19:513\u2013520","journal-title":"Adv Neural Inform Process Syst"},{"issue":"9","key":"10230_CR30","doi-asserted-by":"publisher","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","volume":"66","author":"L Guo","year":"2018","unstructured":"Guo L, Lei Y, Xing S, Yan T, Li N (2018) 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","journal-title":"IEEE Trans Ind Electron"},{"key":"10230_CR31","doi-asserted-by":"publisher","first-page":"107150","DOI":"10.1016\/j.asoc.2021.107150","volume":"103","author":"T Han","year":"2021","unstructured":"Han T, Liu C, Wu R, Jiang D (2021) Deep transfer learning with limited data for machinery fault diagnosis. Appl Soft Comput 103:107150","journal-title":"Appl Soft Comput"},{"key":"10230_CR32","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.knosys.2018.12.019","volume":"165","author":"T Han","year":"2019","unstructured":"Han T, Liu C, Yang W, Jiang D (2019) A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowl-Based Syst 165:474\u2013487","journal-title":"Knowl-Based Syst"},{"key":"10230_CR33","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.isatra.2019.08.012","volume":"97","author":"T Han","year":"2020","unstructured":"Han T, Liu C, Yang W, Jiang D (2020) Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application. ISA Trans 97:269\u2013281","journal-title":"ISA Trans"},{"issue":"4","key":"10230_CR34","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.1109\/JIOT.2020.3024287","volume":"8","author":"H Han","year":"2020","unstructured":"Han H, Ma W, Zhou M, Guo Q, Abusorrah A (2020) A novel semi-supervised learning approach to pedestrian reidentification. IEEE Internet Things J 8(4):3042\u20133052","journal-title":"IEEE Internet Things J"},{"key":"10230_CR35","doi-asserted-by":"publisher","unstructured":"Han S, Zhu K, Zhou M, Liu X (2022) Evolutionary weighted broad learning and its application to fault diagnosis in self-organizing cellular networks. IEEE transactions on cybernetics, 1\u201313. https:\/\/doi.org\/10.1109\/TCYB.2021.3126711","DOI":"10.1109\/TCYB.2021.3126711"},{"key":"10230_CR36","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/j.measurement.2019.02.075","volume":"138","author":"MJ Hasan","year":"2019","unstructured":"Hasan MJ, Islam MM, Kim J-M (2019) Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions. Measurement 138:620\u2013631","journal-title":"Measurement"},{"key":"10230_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10230_CR38","unstructured":"Henaff, O.: Data-efficient image recognition with contrastive predictive coding. In: International Conference on Machine Learning, pp. 4182\u20134192 (2020). PMLR"},{"key":"10230_CR39","doi-asserted-by":"publisher","first-page":"6298","DOI":"10.1109\/TIE.2021.3086707","volume":"69","author":"Z Huang","year":"2021","unstructured":"Huang Z, Lei Z, Wen G, Huang X, Zhou H, Yan R, Chen X (2021) A multi-source dense adaptation adversarial network for fault diagnosis of machinery. IEEE Trans Ind Electron 69:6298\u20136307","journal-title":"IEEE Trans Ind Electron"},{"key":"10230_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109090","volume":"176","author":"G Huang","year":"2021","unstructured":"Huang G, Zhang Y, Ou J (2021) Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network. Measurement 176:109090","journal-title":"Measurement"},{"key":"10230_CR41","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017): Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"10230_CR42","first-page":"434","volume":"15","author":"O Janssens","year":"2018","unstructured":"Janssens O, Loccufier M, Van Hoecke S (2018) Thermal imaging and vibration-based multisensor fault detection for rotating machinery. IEEE Trans Ind Electron 15(1):434\u2013444","journal-title":"IEEE Trans Ind Electron"},{"issue":"1","key":"10230_CR43","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TMECH.2017.2722479","volume":"23","author":"O Janssens","year":"2017","unstructured":"Janssens O, Van de Walle R, Loccufier M, Van Hoecke S (2017) Deep learning for infrared thermal image based machine health monitoring. IEEE\/ASME Transa MechD 23(1):151\u2013159","journal-title":"IEEE\/ASME Transa MechD"},{"key":"10230_CR44","doi-asserted-by":"publisher","first-page":"106236","DOI":"10.1016\/j.knosys.2020.106236","volume":"205","author":"J Jiao","year":"2020","unstructured":"Jiao J, Lin J, Zhao M, Liang K (2020) Double-level adversarial domain adaptation network for intelligent fault diagnosis. Knowl-Based Syst 205:106236","journal-title":"Knowl-Based Syst"},{"issue":"7","key":"10230_CR45","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/JAS.2021.1004051","volume":"8","author":"R Jiao","year":"2021","unstructured":"Jiao R, Peng K, Dong J (2021) Remaining useful life prediction for a roller in a hot strip mill based on deep recurrent neural networks. IEEE\/CAA J Autom Sin 8(7):1345\u20131354","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"10230_CR46","doi-asserted-by":"publisher","first-page":"106962","DOI":"10.1016\/j.ymssp.2020.106962","volume":"145","author":"J Jiao","year":"2020","unstructured":"Jiao J, Zhao M, Lin J, Liang K (2020) Residual joint adaptation adversarial network for intelligent transfer fault diagnosis. Mech Syst Signal Process 145:106962","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR47","first-page":"1","volume":"12","author":"T Jin","year":"2021","unstructured":"Jin T, Yan C, Chen C, Yang Z, Tian H, Guo J (2021) New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions. Int J Adv Manuf Technol 12:1\u201312","journal-title":"Int J Adv Manuf Technol"},{"issue":"4","key":"10230_CR48","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1109\/TCSS.2020.3001517","volume":"7","author":"Q Kang","year":"2020","unstructured":"Kang Q, Yao S, Zhou M, Zhang K, Abusorrah A (2020) Enhanced subspace distribution matching for fast visual domain adaptation. IEEE Trans Comput Soc Syst 7(4):1047\u20131057","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"9","key":"10230_CR49","doi-asserted-by":"publisher","first-page":"3919","DOI":"10.1109\/TNNLS.2020.3016180","volume":"32","author":"Q Kang","year":"2020","unstructured":"Kang Q, Yao S, Zhou M, Zhang K, Abusorrah A (2020) Effective visual domain adaptation via generative adversarial distribution matching. IEEE Trans Neural Netw Learn Syst 32(9):3919\u20133929","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10230_CR50","doi-asserted-by":"crossref","unstructured":"Kim M, Ko JU, Lee J, Youn BD, Jung JH, Sun KH (2021) A domain adaptation with semantic clustering (dasc) method for fault diagnosis of rotating machinery. ISA transactions","DOI":"10.1016\/j.isatra.2021.03.002"},{"issue":"4","key":"10230_CR51","doi-asserted-by":"publisher","first-page":"2868","DOI":"10.1109\/TII.2019.2941486","volume":"16","author":"T Ko","year":"2019","unstructured":"Ko T, Kim H (2019) Fault classification in high-dimensional complex processes using semi-supervised deep convolutional generative models. IEEE Trans Ind Inform 16(4):2868\u20132877","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR52","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inform Process Syst"},{"key":"10230_CR53","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","volume":"104","author":"Y Lei","year":"2018","unstructured":"Lei Y, Li N, Guo L, Li N, Yan T, Lin J (2018) Machinery health prognostics: a systematic review from data acquisition to rul prediction. Mech Syst Signal Process 104:799\u2013834","journal-title":"Mech Syst Signal Process"},{"issue":"11","key":"10230_CR54","doi-asserted-by":"publisher","first-page":"3918","DOI":"10.1109\/TPAMI.2020.2991050","volume":"43","author":"J Li","year":"2020","unstructured":"Li J, Chen E, Ding Z, Zhu L, Lu K, Shen HT (2020) Maximum density divergence for domain adaptation. IEEE Trans Pattern Anal Mach Intell 43(11):3918\u20133930","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10230_CR55","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1109\/TII.2020.2994621","volume":"17","author":"W Li","year":"2020","unstructured":"Li W, Chen Z, He G (2020) A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery. IEEE Trans Ind Inform 17(3):1753\u20131762","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR56","doi-asserted-by":"publisher","first-page":"105950","DOI":"10.1016\/j.asoc.2019.105950","volume":"86","author":"X Li","year":"2020","unstructured":"Li X, Hu Y, Li M, Zheng J (2020) Fault diagnostics between different type of components: A transfer learning approach. Appl Soft Comput 86:105950","journal-title":"Appl Soft Comput"},{"key":"10230_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TASE.2020.3048056","volume":"5","author":"H Li","year":"2021","unstructured":"Li H, Hu G, Li J, Zhou M (2021) Intelligent fault diagnosis for large-scale rotating machines using binarized deep neural networks and random forests. IEEE Trans Autom Sci Eng 5:1\u201311. https:\/\/doi.org\/10.1109\/TASE.2020.3048056","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"10230_CR58","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.neucom.2019.12.033","volume":"383","author":"X Li","year":"2020","unstructured":"Li X, Jia X-D, Zhang W, Ma H, Luo Z, Li X (2020) Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation. Neurocomputing 383:235\u2013247","journal-title":"Neurocomputing"},{"key":"10230_CR59","doi-asserted-by":"publisher","first-page":"106695","DOI":"10.1016\/j.knosys.2020.106695","volume":"213","author":"X Li","year":"2021","unstructured":"Li X, Jiang H, Wang R, Niu M (2021) Rolling bearing fault diagnosis using optimal ensemble deep transfer network. Knowl-Based Syst 213:106695","journal-title":"Knowl-Based Syst"},{"key":"10230_CR60","doi-asserted-by":"publisher","first-page":"91216","DOI":"10.1109\/ACCESS.2019.2926234","volume":"7","author":"X Li","year":"2019","unstructured":"Li X, Jiang H, Zhao K, Wang R (2019) A deep transfer nonnegativity-constraint sparse autoencoder for rolling bearing fault diagnosis with few labeled data. IEEE Access 7:91216\u201391224","journal-title":"IEEE Access"},{"key":"10230_CR61","doi-asserted-by":"publisher","first-page":"106825","DOI":"10.1016\/j.ymssp.2020.106825","volume":"143","author":"X Li","year":"2020","unstructured":"Li X, Li X, Ma H (2020) Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery. Mech Syst Signal Process 143:106825","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR62","doi-asserted-by":"publisher","first-page":"107095","DOI":"10.1016\/j.ymssp.2020.107095","volume":"147","author":"Q Li","year":"2021","unstructured":"Li Q, Shen C, Chen L, Zhu Z (2021) Knowledge mapping-based adversarial domain adaptation: a novel fault diagnosis method with high generalizability under variable working conditions. Mech Syst Signal Process 147:107095","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR63","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2018.05.021","volume":"310","author":"X Li","year":"2018","unstructured":"Li X, Zhang W, Ding Q (2018) A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning. Neurocomputing 310:77\u201395","journal-title":"Neurocomputing"},{"key":"10230_CR64","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neunet.2020.06.014","volume":"129","author":"X Li","year":"2020","unstructured":"Li X, Zhang W, Ma H, Luo Z, Li X (2020) Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks. Neural Netw 129:313\u2013322","journal-title":"Neural Netw"},{"key":"10230_CR65","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.jmsy.2020.04.017","volume":"55","author":"X Li","year":"2020","unstructured":"Li X, Zhang W, Ma H, Luo Z, Li X (2020) Deep learning-based adversarial multi-classifier optimization for cross-domain machinery fault diagnostics. J Manuf Syst 55:334\u2013347","journal-title":"J Manuf Syst"},{"key":"10230_CR66","first-page":"1","volume":"70","author":"T Li","year":"2021","unstructured":"Li T, Zhao Z, Sun C, Yan R, Chen X (2021) Domain adversarial graph convolutional network for fault diagnosis under variable working conditions. IEEE Trans Instrument Measure 70:1\u201310","journal-title":"IEEE Trans Instrument Measure"},{"key":"10230_CR67","doi-asserted-by":"crossref","unstructured":"Li H, Wang Y (2013) Rolling bearing reliability estimation based on logistic regression model. In: 2013 International conference on quality, reliability, risk, maintenance, and safety engineering (QR2MSE), pp. 1730\u20131733. IEEE","DOI":"10.1109\/QR2MSE.2013.6625910"},{"issue":"1","key":"10230_CR68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s10033-020-00524-5","volume":"34","author":"Y Liao","year":"2021","unstructured":"Liao Y, Huang R, Li J, Chen Z, Li W (2021) Dynamic distribution adaptation based transfer network for cross domain bearing fault diagnosis. Chin J Mech Eng 34(1):1\u201310","journal-title":"Chin J Mech Eng"},{"key":"10230_CR166","doi-asserted-by":"publisher","unstructured":"Lin J, Lin Z, Liao G, Yin H (2021) A Novel Product Remaining Useful Life Prediction Approach Considering Fault Effects. IEEE\/CAA J Autom Sin 8(11):1762\u20131773. https:\/\/doi.org\/10.1109\/JAS.2021.1004168","DOI":"10.1109\/JAS.2021.1004168"},{"key":"10230_CR162","doi-asserted-by":"crossref","unstructured":"Liu K, Ye Z, Guo H, Cao D, Chen L, Wang FY (2021) FISS GAN: A Generative Adversarial Network for Foggy Image Semantic Segmentation. IEEE\/CAA J Autom Sin 8(8):1428\u20131439","DOI":"10.1109\/JAS.2021.1004057"},{"issue":"3","key":"10230_CR69","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/TCAD.2021.3065919","volume":"41","author":"M Liu","year":"2022","unstructured":"Liu M, Li X, Chakrabarty K, Gu X (2022) Knowledge transfer in board-level functional fault diagnosis enabled by domain adaptation. IEEE Trans Comput-Aided Des Integr Circuits Syst 41(3):762\u2013775","journal-title":"IEEE Trans Comput-Aided Des Integr Circuits Syst"},{"key":"10230_CR70","first-page":"469","volume":"29","author":"M-Y Liu","year":"2016","unstructured":"Liu M-Y, Tuzel O (2016) Coupled generative adversarial networks. Adv Neural Inform Process Syst 29:469\u2013477","journal-title":"Adv Neural Inform Process Syst"},{"issue":"3","key":"10230_CR71","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu H, Zhou M, Liu Q (2019) An embedded feature selection method for imbalanced data classification. IEEE\/CAA J Autom Sin 6(3):703\u2013715","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"10230_CR72","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208\u20132217. PMLR"},{"key":"10230_CR73","doi-asserted-by":"crossref","unstructured":"Long M, Wang J, Ding G, Sun J, Yu PS (2013) Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200\u20132207","DOI":"10.1109\/ICCV.2013.274"},{"key":"10230_CR74","doi-asserted-by":"crossref","unstructured":"Long M, Wang J, Ding G, Sun J, Yu PS (2014) 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":"10230_CR75","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, pp 97\u2013105. PMLR"},{"issue":"3","key":"10230_CR76","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.1109\/TIE.2016.2627020","volume":"64","author":"W Lu","year":"2016","unstructured":"Lu W, Liang B, Cheng Y, Meng D, Yang J, Zhang T (2016) Deep model based domain adaptation for fault diagnosis. IEEE Trans Ind Electron 64(3):2296\u20132305","journal-title":"IEEE Trans Ind Electron"},{"key":"10230_CR77","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2020.10.039","volume":"427","author":"N Lu","year":"2021","unstructured":"Lu N, Xiao H, Sun Y, Han M, Wang Y (2021) A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation. Neurocomputing 427:96\u2013109","journal-title":"Neurocomputing"},{"key":"10230_CR78","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.isatra.2019.08.040","volume":"99","author":"P Ma","year":"2020","unstructured":"Ma P, Zhang H, Fan W, Wang C (2020) A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains. ISA Trans 99:465\u2013478","journal-title":"ISA Trans"},{"issue":"4","key":"10230_CR79","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1109\/TIM.2019.2917735","volume":"69","author":"W Mao","year":"2019","unstructured":"Mao W, He J, Zuo MJ (2019) Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Trans Instrum Measure 69(4):1594\u20131608","journal-title":"IEEE Trans Instrum Measure"},{"key":"10230_CR80","first-page":"1","volume":"70","author":"M Miao","year":"2021","unstructured":"Miao M, Yu J (2021) A deep domain adaptative network for remaining useful life prediction of machines under different working conditions and fault modes. IEEE Trans Instrum Measure 70:1\u201314","journal-title":"IEEE Trans Instrum Measure"},{"key":"10230_CR81","doi-asserted-by":"publisher","first-page":"106816","DOI":"10.1016\/j.knosys.2021.106816","volume":"216","author":"G Michau","year":"2021","unstructured":"Michau G, Fink O (2021) Unsupervised transfer learning for anomaly detection: application to complementary operating condition transfer. Knowl-Based Syst 216:106816","journal-title":"Knowl-Based Syst"},{"issue":"5\u20138","key":"10230_CR82","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1007\/s00170-013-5065-z","volume":"69","author":"A Mosallam","year":"2013","unstructured":"Mosallam A, Medjaher K, Zerhouni N (2013) Nonparametric time series modelling for industrial prognostics and health management. The Int J Adv Manuf Technol 69(5\u20138):1685\u20131699","journal-title":"The Int J Adv Manuf Technol"},{"key":"10230_CR83","unstructured":"NASA Ames Prognostics Data Repository. http:\/\/ti.arc.nasa.gov\/project\/prognostic-data-repository"},{"key":"10230_CR84","doi-asserted-by":"publisher","first-page":"114410","DOI":"10.1016\/j.applthermaleng.2019.114410","volume":"163","author":"A Nasiri","year":"2019","unstructured":"Nasiri A, Taheri-Garavand A, Omid M, Carlomagno GM (2019) Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images. Appl Thermal Engi 163:114410","journal-title":"Appl Thermal Engi"},{"key":"10230_CR85","unstructured":"Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Chebel-Morello, B., Zerhouni, N., Varnier, C.: PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In: IEEE International conference on prognostics and health management, PHM\u201912., pp. 1\u20138 (2012). IEEE Catalog Number: CPF12PHM-CDR"},{"issue":"4","key":"10230_CR86","doi-asserted-by":"publisher","first-page":"3539","DOI":"10.1109\/TIE.2017.2752151","volume":"65","author":"H Oh","year":"2017","unstructured":"Oh H, Jung JH, Jeon BC, Youn BD (2017) Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE Trans Ind Electron 65(4):3539\u20133549","journal-title":"IEEE Trans Ind Electron"},{"key":"10230_CR87","unstructured":"PHM Society, PHM09 Gearbox Datasets. https:\/\/phmsociety.org\/public-data-sets\/"},{"key":"10230_CR88","unstructured":"Paderborn University, Paderborn University Dataset. https:\/\/mb.uni-paderborn.de\/kat\/datacenter"},{"issue":"2","key":"10230_CR89","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Tsang IW, Kwok JT, Yang Q (2010) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210","journal-title":"IEEE Trans Neural Netw"},{"key":"10230_CR90","doi-asserted-by":"publisher","first-page":"106993","DOI":"10.1016\/j.patcog.2019.106993","volume":"96","author":"W Qian","year":"2019","unstructured":"Qian W, Li S, Jiang X (2019) Deep transfer network for rotating machine fault analysis. Pattern Recognit 96:106993","journal-title":"Pattern Recognit"},{"key":"10230_CR91","doi-asserted-by":"publisher","first-page":"106886","DOI":"10.1016\/j.asoc.2020.106886","volume":"99","author":"W Qian","year":"2021","unstructured":"Qian W, Li S, Yao T, Xu K (2021) Discriminative feature-based adaptive distribution alignment (dfada) for rotating machine fault diagnosis under variable working conditions. Appl Soft Comput 99:106886","journal-title":"Appl Soft Comput"},{"key":"10230_CR92","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108900","volume":"172","author":"A-S Qin","year":"2021","unstructured":"Qin A-S, Mao H-L, Hu Q (2021) Cross-domain fault diagnosis of rolling bearing using similar features-based transfer approach. Measurement 172:108900","journal-title":"Measurement"},{"issue":"4\u20135","key":"10230_CR93","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 (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J Sound Vibr 289(4\u20135):1066\u20131090","journal-title":"J Sound Vibr"},{"issue":"8","key":"10230_CR94","doi-asserted-by":"publisher","first-page":"5239","DOI":"10.1109\/TII.2020.3032690","volume":"17","author":"M Ragab","year":"2020","unstructured":"Ragab M, Chen Z, Wu M, Foo CS, Kwoh CK, Yan R, Li X (2020) Contrastive adversarial domain adaptation for machine remaining useful life prediction. IEEE Trans Ind Inform 17(8):5239\u20135249","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR95","doi-asserted-by":"crossref","unstructured":"Ragab M, Chen Z, Wu M, Kwoh CK, Li X (2020) Adversarial transfer learning for machine remaining useful life prediction. In: 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1\u20137. IEEE","DOI":"10.1109\/ICPHM49022.2020.9187053"},{"key":"10230_CR96","doi-asserted-by":"crossref","unstructured":"Renwick J, Kulkarni CS, Celaya JR (2015) Analysis of electrolytic capacitor degradation under electrical overstress for prognostic studies. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, vol. 6 (2015)","DOI":"10.36001\/phmconf.2015.v7i1.2713"},{"key":"10230_CR97","doi-asserted-by":"crossref","unstructured":"Saito K, Watanabe K, Ushiku Y, Harada T (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3723\u20133732","DOI":"10.1109\/CVPR.2018.00392"},{"key":"10230_CR98","unstructured":"Saito K, Ushiku Y, Harada T (2017) Asymmetric tri-training for unsupervised domain adaptation. In: International Conference on Machine Learning, pp. 2988\u20132997. PMLR"},{"issue":"10","key":"10230_CR99","doi-asserted-by":"publisher","first-page":"6263","DOI":"10.1109\/TII.2020.2967822","volume":"16","author":"SR Saufi","year":"2020","unstructured":"Saufi SR, Ahmad ZAB, Leong MS, Lim MH (2020) Gearbox fault diagnosis using a deep learning model with limited data sample. IEEE Trans Ind Inform 16(10):6263\u20136271","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR100","doi-asserted-by":"crossref","unstructured":"Saxena A, Goebel K, Simon D, Eklund N *008( Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International conference on prognostics and health management, pp 1\u20139 (2008). IEEE","DOI":"10.1109\/PHM.2008.4711414"},{"issue":"4","key":"10230_CR101","doi-asserted-by":"publisher","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","volume":"15","author":"S Shao","year":"2018","unstructured":"Shao S, McAleer S, Yan R, Baldi P (2018) Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inform 15(4):2446\u20132455","journal-title":"IEEE Trans Ind Inform"},{"issue":"5","key":"10230_CR102","doi-asserted-by":"publisher","first-page":"3488","DOI":"10.1109\/TII.2020.3005965","volume":"17","author":"H Shao","year":"2020","unstructured":"Shao H, Xia M, Han G, Zhang Y, Wan J (2020) Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images. IEEE Trans Ind Inform 17(5):3488\u20133496","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR103","doi-asserted-by":"publisher","first-page":"107155","DOI":"10.1016\/j.measurement.2019.107155","volume":"151","author":"F Shen","year":"2020","unstructured":"Shen F, Langari R, Yan R (2020) Transfer between multiple machine plants: a modified fast self-organizing feature map and two-order selective ensemble based fault diagnosis strategy. Measurement 151:107155","journal-title":"Measurement"},{"key":"10230_CR104","first-page":"1","volume":"70","author":"C Shen","year":"2021","unstructured":"Shen C, Wang X, Wang D, Li Y, Zhu J, Gong M (2021) Dynamic joint distribution alignment network for bearing fault diagnosis under variable working conditions. IEEE Trans Instrum Meas 70:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"10230_CR105","doi-asserted-by":"crossref","unstructured":"Shen J, Qu Y, Zhang W, Yu Y (2018) Wasserstein distance guided representation learning for domain adaptation. In: Thirty-Second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"10230_CR106","unstructured":"Shen F, Chen C, Yan R, Gao RX (2015) Bearing fault diagnosis based on svd feature extraction and transfer learning classification. In: 2015 Prognostics and System Health Management Conference (PHM), pp. 1\u20136. IEEE"},{"key":"10230_CR164","doi-asserted-by":"publisher","unstructured":"Shi X, Kang Q, An J, Zhou M (2021) Novel L1 Regularized Extreme Learning Machine for Soft-Sensing of an Industrial Process. IEEE Trans Industr Inform 18(2):1009\u20131017. https:\/\/doi.org\/10.1109\/TII.2021.3065377","DOI":"10.1109\/TII.2021.3065377"},{"key":"10230_CR107","doi-asserted-by":"publisher","first-page":"108827","DOI":"10.1016\/j.measurement.2020.108827","volume":"172","author":"J Si","year":"2021","unstructured":"Si J, Shi H, Chen J, Zheng C (2021) Unsupervised deep transfer learning with moment matching: a new intelligent fault diagnosis approach for bearings. Measurement 172:108827","journal-title":"Measurement"},{"issue":"3","key":"10230_CR108","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/JAS.2021.1003862","volume":"8","author":"L Silva","year":"2021","unstructured":"Silva L, Magaia N, Sousa B, Kobusi\u0144ska A, Casimiro A, Mavromoustakis CX, Mastorakis G, De Albuquerque VHC (2021) Computing paradigms in emerging vehicular environments: a review. IEEE\/CAA J Autom Sin 8(3):491\u2013511","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"10230_CR109","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"10230_CR110","doi-asserted-by":"crossref","unstructured":"Sloukia F, El\u00a0Aroussi M, Medromi H, Wahbi M (2013) Bearings prognostic using mixture of gaussians hidden markov model and support vector machine. In: 2013 ACS international conference on computer systems and applications (AICCSA), pp. 1\u20134. IEEE","DOI":"10.1109\/AICCSA.2013.6616438"},{"key":"10230_CR111","unstructured":"Society For Machinery Failure Prevention Technology, MFPT Dataset. https:\/\/www.mfpt.org\/fault-data-sets\/"},{"key":"10230_CR112","unstructured":"Southeast University, Gearbox Dataset. http:\/\/mlmechanics.ics.uci.edu\/"},{"issue":"4","key":"10230_CR113","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1109\/TII.2018.2881543","volume":"15","author":"C Sun","year":"2018","unstructured":"Sun C, Ma M, Zhao Z, Tian S, Yan R, Chen X (2018) Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing. IEEE Trans Ind Inform 15(4):2416\u20132425","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR114","first-page":"1","volume":"4","author":"B Sun","year":"2015","unstructured":"Sun B, Saenko K (2015) Subspace distribution alignment for unsupervised domain adaptation. BMVC 4:1\u201324","journal-title":"BMVC"},{"key":"10230_CR115","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: European conference on computer vision, pp 443\u2013450. Springer","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"10230_CR116","doi-asserted-by":"crossref","unstructured":"Sun B, Feng J, Saenko K (2017) Correlation alignment for unsupervised domain adaptation. In: Domain adaptation in computer vision applications, pp 153\u2013171. Springer, Cham","DOI":"10.1007\/978-3-319-58347-1_8"},{"key":"10230_CR700","doi-asserted-by":"crossref","unstructured":"Sun C, Yin H, Li Y, Chai Y (2021) A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning. in IEEE\/CAA J Autom Sin 8(6): 1188\u20131198","DOI":"10.1109\/JAS.2020.1003438"},{"key":"10230_CR117","doi-asserted-by":"crossref","unstructured":"Sutrisno E, Oh H, Vasan ASS, Pecht M (2012) Estimation of remaining useful life of ball bearings using data driven methodologies. In: 2012 IEEE Conference on Prognostics and Health Management, pp. 1\u20137 (2012). IEEE","DOI":"10.1109\/ICPHM.2012.6299548"},{"key":"10230_CR118","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Darrell T, Saenko K (2015) Simultaneous deep transfer across domains and tasks. In: Proceedings of the IEEE international conference on computer vision, pp 4068\u20134076","DOI":"10.1109\/ICCV.2015.463"},{"key":"10230_CR119","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474"},{"key":"10230_CR120","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"issue":"1","key":"10230_CR121","first-page":"1","volume":"11","author":"J Wang","year":"2020","unstructured":"Wang J, Chen Y, Feng W, Yu H, Huang M, Yang Q (2020) Transfer learning with dynamic distribution adaptation. ACM Trans Intell Syst Technol (TIST) 11(1):1\u201325","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"9","key":"10230_CR123","doi-asserted-by":"publisher","first-page":"5139","DOI":"10.1109\/TII.2019.2899118","volume":"15","author":"X Wang","year":"2019","unstructured":"Wang X, He H, Li L (2019) A hierarchical deep domain adaptation approach for fault diagnosis of power plant thermal system. IEEE Trans Ind Inform 15(9):5139\u20135148","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR124","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neucom.2019.10.064","volume":"379","author":"B Wang","year":"2020","unstructured":"Wang B, Lei Y, Yan T, Li N, Guo L (2020) Recurrent convolutional neural network: a new framework for remaining useful life prediction of machinery. Neurocomputing 379:117\u2013129","journal-title":"Neurocomputing"},{"key":"10230_CR125","doi-asserted-by":"publisher","first-page":"107050","DOI":"10.1016\/j.ress.2020.107050","volume":"202","author":"X Wang","year":"2020","unstructured":"Wang X, Shen C, Xia M, Wang D, Zhu J, Zhu Z (2020) Multi-scale deep intra-class transfer learning for bearing fault diagnosis. Reliab Eng Syst Saf 202:107050","journal-title":"Reliab Eng Syst Saf"},{"key":"10230_CR126","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3123218","volume":"70","author":"X Wang","year":"2021","unstructured":"Wang X, Wanga T, Ming A, Zhang W, Li A, Chu F (2021) Cross-operating-condition degradation knowledge learning for remaining useful life estimation of bearings. IEEE Trans Instrum Measure 70:1\u201311","journal-title":"IEEE Trans Instrum Measure"},{"key":"10230_CR127","doi-asserted-by":"publisher","first-page":"1106925","DOI":"10.1016\/j.knosys.2021.106925","volume":"220","author":"C Wang","year":"2021","unstructured":"Wang C, Xin C, Xu Z (2021) A novel deep metric learning model for imbalanced fault diagnosis and toward open-set classification. Knowl-Based Syst 220:1106925","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"10230_CR128","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TITS.2016.2521866","volume":"18","author":"F Wang","year":"2016","unstructured":"Wang F, Xu T, Tang T, Zhou M, Wang H (2016) Bilevel feature extraction-based text mining for fault diagnosis of railway systems. IEEE Trans Intell Trans Syst 18(1):49\u201358","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"10230_CR129","doi-asserted-by":"crossref","unstructured":"Wang J, Chen Y, Hao S, Feng W, Shen Z (2017) Balanced distribution adaptation for transfer learning. In: 2017 IEEE international conference on data mining (ICDM), pp. 1129\u20131134. IEEE","DOI":"10.1109\/ICDM.2017.150"},{"key":"10230_CR130","doi-asserted-by":"crossref","unstructured":"Wang J, Feng W, Chen Y, Yu H, Huang M, Yu PS (2018) Visual domain adaptation with manifold embedded distribution alignment. In: Proceedings of the 26th ACM international conference on multimedia, pp 402\u2013410","DOI":"10.1145\/3240508.3240512"},{"key":"10230_CR131","doi-asserted-by":"crossref","unstructured":"Wang J, Xie J, Zhang L, Duan L (2016) A factor analysis based transfer learning method for gearbox diagnosis under various operating conditions. In: 2016 International Symposium on Flexible Automation (ISFA), pp. 81\u201386. IEEE","DOI":"10.1109\/ISFA.2016.7790140"},{"issue":"1","key":"10230_CR132","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/TSMC.2017.2754287","volume":"49","author":"L Wen","year":"2017","unstructured":"Wen L, Gao L, Li X (2017) A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans Syst Man Cybernet 49(1):136\u2013144","journal-title":"IEEE Trans Syst Man Cybernet"},{"issue":"7","key":"10230_CR133","doi-asserted-by":"publisher","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","volume":"65","author":"L Wen","year":"2017","unstructured":"Wen L, Li X, Gao L, Zhang Y (2017) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Electron 65(7):5990\u20135998","journal-title":"IEEE Trans Ind Electron"},{"key":"10230_CR134","doi-asserted-by":"publisher","first-page":"105814","DOI":"10.1016\/j.knosys.2020.105814","volume":"196","author":"Z Wu","year":"2020","unstructured":"Wu Z, Jiang H, Lu T, Zhao K (2020) A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data. Knowl-Based Syst 196:105814","journal-title":"Knowl-Based Syst"},{"key":"10230_CR135","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107227","volume":"151","author":"Z Wu","year":"2020","unstructured":"Wu Z, Jiang H, Zhao K, Li X (2020) An adaptive deep transfer learning method for bearing fault diagnosis. Measurement 151:107227","journal-title":"Measurement"},{"issue":"3","key":"10230_CR136","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1109\/TII.2021.3081595","volume":"18","author":"P Xia","year":"2021","unstructured":"Xia P, Huang Y, Li P, Liu C, Shi L (2021) Fault knowledge transfer assisted ensemble method for remaining useful life prediction. IEEE Trans Ind Inform 18(3):1758\u20131769","journal-title":"IEEE Trans Ind Inform"},{"issue":"6","key":"10230_CR137","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1109\/TII.2018.2868687","volume":"15","author":"M Xia","year":"2018","unstructured":"Xia M, Li T, Shu T, Wan J, De Silva CW, Wang Z (2018) A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans Ind Inform 15(6):3703\u20133711","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR138","doi-asserted-by":"publisher","first-page":"107618","DOI":"10.1016\/j.ymssp.2021.107618","volume":"156","author":"B Yang","year":"2021","unstructured":"Yang B, Lee C-G, Lei Y, Li N, Lu N (2021) Deep partial transfer learning network: a method to selectively transfer diagnostic knowledge across related machines. Mech Syst Signal Process 156:107618","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR139","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","volume":"122","author":"B Yang","year":"2019","unstructured":"Yang B, Lei Y, Jia F, Xing S (2019) An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech Syst Signal Process 122:692\u2013706","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR701","doi-asserted-by":"crossref","unstructured":"Yang N, Zheng Z, Zhou M, Guo X, Qi L, Wang T (2021) A Domain-Guided Noise-Optimization-Based Inversion Method for Facial Image Manipulation. IEEE Trans. on Image Processing 30:6198\u20136211","DOI":"10.1109\/TIP.2021.3089905"},{"key":"10230_CR140","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? arXiv preprint arXiv:1411.1792"},{"key":"10230_CR141","doi-asserted-by":"crossref","unstructured":"Yu C, Wang J, Chen Y, Huang M (2019) Transfer learning with dynamic adversarial adaptation network. In: 2019 IEEE international conference on data mining (ICDM), pp 778\u2013786. IEEE","DOI":"10.1109\/ICDM.2019.00088"},{"key":"10230_CR142","doi-asserted-by":"crossref","unstructured":"Yu S, Wu Z, Zhu X, Pecht M (2019) A domain adaptive convolutional lstm model for prognostic remaining useful life estimation under variant conditions. In: 2019 Prognostics and System Health Management Conference (PHM-Paris), pp. 130\u2013137. IEEE","DOI":"10.1109\/PHM-Paris.2019.00030"},{"issue":"3","key":"10230_CR143","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1109\/TASE.2020.3000946","volume":"18","author":"H Yuan","year":"2020","unstructured":"Yuan H, Zhou M (2020) Profit-maximized collaborative computation offloading and resource allocation in distributed cloud and edge computing systems. IEEE Trans Autom Scid Eng 18(3):1277\u20131287","journal-title":"IEEE Trans Autom Scid Eng"},{"key":"10230_CR144","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108071","volume":"165","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Chen H, Li S, An Z (2020) Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis. Measurement 165:108071","journal-title":"Measurement"},{"key":"10230_CR145","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2019.09.081","volume":"376","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Chen H, Li S, An Z, Wang J (2020) A novel geodesic flow kernel based domain adaptation approach for intelligent fault diagnosis under varying working condition. Neurocomputing 376:54\u201364","journal-title":"Neurocomputing"},{"key":"10230_CR146","doi-asserted-by":"publisher","first-page":"106681","DOI":"10.1016\/j.ymssp.2020.106681","volume":"140","author":"L Zhang","year":"2020","unstructured":"Zhang L, Guo L, Gao H, Dong D, Fu G, Hong X (2020) Instance-based ensemble deep transfer learning network: A new intelligent degradation recognition method and its application on ball screw. Mech Syst Signal Process 140:106681","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR147","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107377","volume":"152","author":"W Zhang","year":"2020","unstructured":"Zhang W, Li X, Jia X-D, Ma H, Luo Z, Li X (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement 152:107377","journal-title":"Measurement"},{"key":"10230_CR148","doi-asserted-by":"publisher","first-page":"1075560","DOI":"10.1016\/j.ress.2021.107556","volume":"211","author":"W Zhang","year":"2021","unstructured":"Zhang W, Li X, Ma H, Luo Z, Li X (2021) Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions. Reliab Eng Syst Saf 211:1075560","journal-title":"Reliab Eng Syst Saf"},{"key":"10230_CR149","doi-asserted-by":"publisher","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","volume":"5","author":"R Zhang","year":"2017","unstructured":"Zhang R, Tao H, Wu L, Guan Y (2017) Transfer learning with neural networks for bearing fault diagnosis in changing working conditions. IEEE Access 5:14347\u201314357","journal-title":"IEEE Access"},{"key":"10230_CR150","doi-asserted-by":"publisher","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","volume":"8","author":"S Zhang","year":"2020","unstructured":"Zhang S, Zhang S, Wang B, Habetler TG (2020) Deep learning algorithms for bearing fault diagnostics-a comprehensive review. IEEE Access 8:29857\u201329881","journal-title":"IEEE Access"},{"issue":"5","key":"10230_CR153","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1007\/s13042-020-01249-6","volume":"12","author":"K Zhao","year":"2021","unstructured":"Zhao K, Jiang H, Li X, Wang R (2021) Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis. Int J Mach Learn Cybernet 12(5):1483\u20131499","journal-title":"Int J Mach Learn Cybernet"},{"key":"10230_CR154","doi-asserted-by":"publisher","first-page":"106974","DOI":"10.1016\/j.knosys.2021.106974","volume":"222","author":"K Zhao","year":"2021","unstructured":"Zhao K, Jiang H, Wang K, Pei Z (2021) Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis. Knowl-Based Syst 222:106974","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"10230_CR155","doi-asserted-by":"publisher","first-page":"2435","DOI":"10.1109\/TII.2018.2875956","volume":"15","author":"M Zhao","year":"2018","unstructured":"Zhao M, Jiao J, Lin J (2018) A data-driven monitoring scheme for rotating machinery via self-comparison approach. IEEE Trans Ind Inform 15(4):2435\u20132445","journal-title":"IEEE Trans Ind Inform"},{"key":"10230_CR156","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","volume":"115","author":"R Zhao","year":"2019","unstructured":"Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213\u2013237","journal-title":"Mech Syst Signal Process"},{"key":"10230_CR157","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2020.04.073","volume":"407","author":"B Zhao","year":"2020","unstructured":"Zhao B, Zhang X, Zhan Z, Pang S (2020) Deep multi-scale convolutional transfer learning network: a novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains. Neurocomputing 407:24\u201338","journal-title":"Neurocomputing"},{"key":"10230_CR158","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107393","volume":"152","author":"H Zhiyi","year":"2020","unstructured":"He Z, Shao H, Jing L, Cheng J, Yang Y (2020) Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder. Measurement 152:107393","journal-title":"Measurement"},{"issue":"2","key":"10230_CR159","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/JAS.2019.1911804","volume":"7","author":"K Zhong","year":"2019","unstructured":"Zhong K, Han M, Han B (2019) Data-driven based fault prognosis for industrial systems: a concise overview. IEEE\/CAA J Autom Sin 7(2):330\u2013345","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"15","key":"10230_CR160","doi-asserted-by":"publisher","first-page":"8394","DOI":"10.1109\/JSEN.2019.2936932","volume":"20","author":"J Zhu","year":"2019","unstructured":"Zhu J, Chen N, Shen C (2019) A new deep transfer learning method for bearing fault diagnosis under different working conditions. IEEE Sens J 20(15):8394\u20138402","journal-title":"IEEE Sens J"},{"key":"10230_CR161","doi-asserted-by":"publisher","first-page":"106602","DOI":"10.1016\/j.ymssp.2019.106602","volume":"139","author":"J Zhu","year":"2020","unstructured":"Zhu J, Chen N, Shen C (2020) A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mech Syst Signal Process 139:106602","journal-title":"Mech Syst Signal Process"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-022-10230-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-022-10230-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-022-10230-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T20:54:53Z","timestamp":1727816093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-022-10230-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,16]]},"references-count":164,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10230"],"URL":"https:\/\/doi.org\/10.1007\/s10462-022-10230-4","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,16]]},"assertion":[{"value":"16 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}