{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T06:49:41Z","timestamp":1780987781248,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["12062009"],"award-info":[{"award-number":["12062009"]}]},{"name":"National Natural Science Foundation of China","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"National Natural Science Foundation of China","award":["2022cyzc-24"],"award-info":[{"award-number":["2022cyzc-24"]}]},{"name":"National Natural Science Foundation of China","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"National Natural Science Foundation of China","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Gansu Provincial University Industry Support Plan Project","award":["12062009"],"award-info":[{"award-number":["12062009"]}]},{"name":"Gansu Provincial University Industry Support Plan Project","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"Gansu Provincial University Industry Support Plan Project","award":["2022cyzc-24"],"award-info":[{"award-number":["2022cyzc-24"]}]},{"name":"Gansu Provincial University Industry Support Plan Project","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Gansu Provincial University Industry Support Plan Project","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["12062009"],"award-info":[{"award-number":["12062009"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["2022cyzc-24"],"award-info":[{"award-number":["2022cyzc-24"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["12062009"],"award-info":[{"award-number":["12062009"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["U1934209"],"award-info":[{"award-number":["U1934209"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["2022cyzc-24"],"award-info":[{"award-number":["2022cyzc-24"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["BK20220502"],"award-info":[{"award-number":["BK20220502"]}]},{"name":"Suzhou Innovation and Entrepreneurship Leading Talent Plan","award":["ZXL2022488"],"award-info":[{"award-number":["ZXL2022488"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The transfer learning method, based on unsupervised domain adaptation (UDA), has been broadly utilized in research on fault diagnosis under variable working conditions with certain results. However, traditional UDA methods pay more attention to extracting information for the class labels and domain labels of data, ignoring the influence of data structure information on the extracted features. Therefore, we propose a domain-adversarial multi-graph convolutional network (DAMGCN) for UDA. A multi-graph convolutional network (MGCN), integrating three graph convolutional layers (multi-receptive field graph convolutional (MRFConv) layer, local extreme value convolutional (LEConv) layer, and graph attention convolutional (GATConv) layer) was used to mine data structure information. The domain discriminators and classifiers were utilized to model domain labels and class labels, respectively, and align the data structure differences through the correlation alignment (CORAL) index. The classification and feature extraction ability of the DAMGCN was significantly enhanced compared with other UDA algorithms by two example validation results, which can effectively achieve rolling bearing cross-domain fault diagnosis.<\/jats:p>","DOI":"10.3390\/sym14122654","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T04:25:26Z","timestamp":1671078326000},"page":"2654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Domain-Adversarial Multi-Graph Convolutional Network for Unsupervised Domain Adaptation Rolling Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3226-5719","authenticated-orcid":false,"given":"Xinran","family":"Li","sequence":"first","affiliation":[{"name":"The School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3741-3610","authenticated-orcid":false,"given":"Wuyin","family":"Jin","sequence":"additional","affiliation":[{"name":"The School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9713-0535","authenticated-orcid":false,"given":"Xiangyang","family":"Xu","sequence":"additional","affiliation":[{"name":"The School of Rail Transit, Soochow University, Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108765","DOI":"10.1016\/j.ymssp.2021.108765","article-title":"A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions","volume":"169","author":"Su","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","first-page":"8064","article-title":"Deep Semi-supervised Domain Generalization Network for Rotary Machinery Fault Diagnosis under Variable Speed","volume":"69","author":"Liao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6466","DOI":"10.1109\/TII.2020.2964117","article-title":"A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault Diagnosis","volume":"16","author":"Huang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108186","DOI":"10.1016\/j.ress.2021.108186","article-title":"Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery","volume":"218","author":"Lee","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.jsv.2005.03.007","article-title":"Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics","volume":"289","author":"Qiu","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zhao, R., He, T., Chen, P., Wei, K., and Xing, Z. (2022). Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding. ISA Trans.","DOI":"10.1016\/j.isatra.2022.05.006"},{"key":"ref_7","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.neucom.2019.12.033","article-title":"Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation","volume":"383","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1109\/TIE.2018.2838070","article-title":"Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis","volume":"66","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","article-title":"An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data","volume":"63","author":"Lei","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","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":"ref_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ymssp.2017.11.024","article-title":"A review on the application of deep learning in system health management","volume":"107","author":"Khan","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106796","DOI":"10.1016\/j.knosys.2021.106796","article-title":"Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network","volume":"216","author":"Cheng","year":"2021","journal-title":"Knowl. -Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.neucom.2019.08.010","article-title":"A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis","volume":"366","author":"Zhao","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, W., Guo, P., and Ye, L. (2019). A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis. Sensors, 19.","DOI":"10.3390\/s19143109"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108653","DOI":"10.1016\/j.measurement.2020.108653","article-title":"An LSTM-based severity evaluation method for intermittent open faults of an electrical connector under a shock test","volume":"173","author":"Shi","year":"2020","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5965","DOI":"10.1109\/TII.2019.2956294","article-title":"Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis","volume":"16","author":"Jiao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8394","DOI":"10.1109\/JSEN.2019.2936932","article-title":"A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions","volume":"20","author":"Zhu","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1109\/TPAMI.2013.249","article-title":"Unsupervised adaptation across domain shifts by generating intermediate data representations","volume":"36","author":"Gopalan","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3525828","DOI":"10.1109\/TIM.2021.3116309","article-title":"Unsupervised deep transfer learning for intelligent fault diagnosis: An open source and comparative study","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108601","DOI":"10.1016\/j.measurement.2020.108601","article-title":"Intelligent fault diagnosis of rotating components in the absence of fault data: A transfer-based approach","volume":"173","author":"Deng","year":"2020","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3044719","article-title":"Intelligent Fault Diagnosis with Deep Adversarial Domain Adaptation","volume":"70","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106427","DOI":"10.1016\/j.cie.2020.106427","article-title":"Domain adaptive deep belief network for rolling bearing fault diagnosis","volume":"143","author":"Che","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_24","first-page":"1","article-title":"Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural. Networks Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling. arXiv.","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12739","DOI":"10.1109\/TIE.2020.3040669","article-title":"Multi-receptive field graph convolutional networks for machine fault diagnosis","volume":"68","author":"Li","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4167","DOI":"10.1109\/TIE.2021.3075871","article-title":"SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis","volume":"69","author":"Yang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ranjan, E., Sanyal, S., and Talukdar, P. (2020). ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations. arXiv.","DOI":"10.1609\/aaai.v34i04.5997"},{"key":"ref_30","unstructured":"Velikovi, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (2017). Graph Attention Networks. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sun, B., and Saenko, K. (2016). Deep CORAL: Correlation Alignment for Deep Domain Adaptation, ECCV.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1214\/13-AOS1140","article-title":"Equivalence of distance-based and RKHS-based statistics in hypoth-esis testing","volume":"41","author":"Sejdinovic","year":"2013","journal-title":"Ann. Statist."},{"key":"ref_33","unstructured":"Gretton, A., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K., and Sriperumbudur, B.K. (2012). Advances in Neural Information Processing Systems 25, MIT Press."},{"key":"ref_34","first-page":"2096-2030","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","unstructured":"Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2018). Advances in Neural Information Processing Systems 31, MIT Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on graphs via spectral graph theory","volume":"30","author":"Hammond","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_37","unstructured":"(2022, August 14). The Case Western Reserve University Bearing Data Center Website. Available online: https:\/\/engineering.case.edu\/bearingdatacenter."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8013","DOI":"10.3390\/s130608013","article-title":"Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis","volume":"13","author":"Li","year":"2013","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","article-title":"Transferable Representation Learning with Deep Adaptation Networks","volume":"41","author":"Long","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M.I. (2017, January 6\u201311). Deep transfer learning with joint adaptation networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/12\/2654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:41:49Z","timestamp":1760146909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/12\/2654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["sym14122654"],"URL":"https:\/\/doi.org\/10.3390\/sym14122654","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}