{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T09:07:30Z","timestamp":1763716050472,"version":"3.45.0"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Aiming to tackle the challenge of feature transfer in cross-domain fault diagnosis for rolling bearings, an enhanced domain adaptation-based intelligent fault diagnosis method is proposed. This method systematically combines multi-layer multi-core MMD with adversarial domain classification. Specifically, we will extend alignment to multiple network layers, while previous work typically applied MMD to fewer layers or used single core variants. Initially, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract features from both the source and target domains, thereby enhancing the diagnostic model\u2019s cross-domain adaptability through shared feature learning. Subsequently, to address the distribution differences in feature extraction, the multi-layer multi-kernel maximum mean discrepancy (ML-MK MMD) method is employed to quantify the distribution disparity between the source and target domain features, with the objective of extracting domain-invariant features. Moreover, to further mitigate domain shift, a novel loss function is developed by integrating ML-MK MMD with a domain classifier loss, which optimizes the alignment of feature distributions between the two domains. Ultimately, testing on target domain samples demonstrates that the proposed method effectively extracts domain-invariant features, significantly reduces the distribution gap between the source and target domains, and thereby enhances cross-domain diagnostic performance.<\/jats:p>","DOI":"10.3390\/e27111178","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T08:59:46Z","timestamp":1763715586000},"page":"1178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Bearing Fault Transfer Diagnosis Method Based on Improved Domain Adaption"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6919-3643","authenticated-orcid":false,"given":"Jinli","family":"Che","sequence":"first","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liqing","family":"Fang","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiao","family":"Ma","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guibo","family":"Yu","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoting","family":"Sun","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiujie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.08.036","article-title":"Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals","volume":"113","author":"Glowacz","year":"2018","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115175","DOI":"10.1016\/j.jsv.2020.115175","article-title":"Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis","volume":"471","author":"Sun","year":"2020","journal-title":"J. Sound Vib."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, X., She, D., Xu, Y., and Jia, M. (2021). Deep regularized variational autoencoder for intelligent fault diagnosis of rotor\u2013bearing system within entire life-cycle process. Entropy, 23.","DOI":"10.1016\/j.knosys.2021.107142"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.measurement.2016.04.036","article-title":"Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review","volume":"90","author":"Li","year":"2016","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1109\/JSEN.2021.3131722","article-title":"Bearing fault diagnosis based on multiple transformation domain fusion and improved residual dense networks","volume":"22","author":"Sun","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107735","DOI":"10.1016\/j.measurement.2020.107735","article-title":"Latest developments in gear defect diagnosis and prognosis: A review","volume":"158","author":"Kumar","year":"2020","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xiao, Q., Li, S., Zhou, L., and Shi, W. (2022). Improved variational mode decomposition and CNN for intelligent rotating machinery fault diagnosis. Entropy, 24.","DOI":"10.3390\/e24070908"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, W., Peng, G., Li, C., Chen, Y., and Zhang, Z. (2017). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17.","DOI":"10.20944\/preprints201701.0132.v1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108752","DOI":"10.1016\/j.ymssp.2021.108752","article-title":"Vibration-based anomaly detection using LSTM\/SVM approaches","volume":"169","author":"Vos","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108603","DOI":"10.1016\/j.measurement.2020.108603","article-title":"Bearing fault diagnosis based on a multi-head graph convolutional weighting network","volume":"173","author":"Mao","year":"2021","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115054","DOI":"10.1016\/j.measurement.2024.115054","article-title":"A strong anti-noise and easily deployable bearing fault diagnosis model based on time-frequency dual-channel Transformer","volume":"236","author":"Xu","year":"2024","journal-title":"Measurement"},{"key":"ref_12","unstructured":"Mirzaeibonehkhater, M., Labbaf-Khaniki, M.A., and Manthouri, M. (2024). Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"125686","DOI":"10.1016\/j.eswa.2024.125686","article-title":"A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions","volume":"262","author":"Xiang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1007\/s11760-025-04581-y","article-title":"A Dual-Branch Dynamic Attention and Sparse Transformer Network for Noise-Robust Bearing Fault Diagnosis","volume":"19","author":"Mu","year":"2025","journal-title":"Signal Image Video Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1007\/s10489-025-06535-w","article-title":"Intelligent fault diagnosis method based on data generation and long-patch vision transformer under small samples","volume":"55","author":"Cen","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_16","unstructured":"Wang, C., Cheng, Y., Liu, W., and Wang, Y. (2024, January 8\u201310). A Novel Unbalanced Fault Diagnosis Method with Diffusion Model in Rotating Machinery. Proceedings of the 6th International Conference on Structural Health Monitoring and Integrity Management, Zhengzhou, China."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9933","DOI":"10.1109\/TII.2022.3232766","article-title":"Dual-threshold attention-guided GAN and limited infrared thermal images for rotating machinery fault diagnosis under speed fluctuation","volume":"19","author":"Shao","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103096","DOI":"10.1016\/j.aei.2024.103096","article-title":"Multi-scale Dynamic Graph Mutual Information Network for planetary bearing health monitoring under imbalanced data","volume":"64","author":"Cai","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108934","DOI":"10.1016\/j.asoc.2022.108934","article-title":"Long short term memory recurrent neural network based bearing fault diagnosis","volume":"123","author":"Kim","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112421","DOI":"10.1016\/j.measurement.2022.112421","article-title":"A review on synchronous micro-phasor measurement units and their applications","volume":"210","author":"Zhong","year":"2023","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4351","DOI":"10.1109\/TIE.2020.2984968","article-title":"Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics","volume":"68","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104383","DOI":"10.1016\/j.engappai.2021.104383","article-title":"Adaptive residual convolutional neural network for fault diagnosis of rotating machinery","volume":"104","author":"Yao","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104415","DOI":"10.1016\/j.engappai.2021.104415","article-title":"A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines","volume":"105","author":"Schwendemann","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Q., Michau, G., and Fink, O. (2019, January 2\u20135). Domain adaptive transfer learning for fault diagnosis. Proceedings of the 2019 Prognostics and System Health Management Conference (PHM-Paris), Paris, France.","DOI":"10.1109\/PHM-Paris.2019.00054"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108339","DOI":"10.1016\/j.measurement.2020.108339","article-title":"Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings","volume":"169","author":"Li","year":"2021","journal-title":"Measurement"},{"key":"ref_29","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","article-title":"Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data","volume":"66","author":"Guo","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104932","DOI":"10.1016\/j.engappai.2022.104932","article-title":"Multiscale domain adaptive transfer learning via pseudo-siamese network for intelligent fault diagnosis","volume":"113","author":"Yao","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","unstructured":"Long, M., Zhu, H., 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, PMLR, Sydney, Australia."},{"key":"ref_35","unstructured":"Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2018). Conditional adversarial domain adaptation. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sigpro.2018.12.005","article-title":"Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation","volume":"157","author":"Li","year":"2019","journal-title":"Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2010.07.017","article-title":"Rolling element bearing diagnostics\u2014A tutorial","volume":"25","author":"Randall","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study","volume":"64\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/11\/1178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T09:04:12Z","timestamp":1763715852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/11\/1178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["e27111178"],"URL":"https:\/\/doi.org\/10.3390\/e27111178","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]}}}