{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:57:57Z","timestamp":1779886677679,"version":"3.53.1"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"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>Accurate bearing fault diagnosis under various operational conditions presents significant challenges, mainly due to the limited availability of labeled data and the domain mismatches across different operating environments. In this study, an adaptive meta-learning framework (AdaMETA) is proposed, which combines dynamic task-aware model-independent meta-learning (DT-MAML) with efficient multi-scale attention (EMA) modules to enhance the model\u2019s ability to generalize and improve diagnostic performance in small-sample bearing fault diagnosis across different load scenarios. Specifically, a hierarchical encoder equipped with C-EMA is introduced to effectively capture multi-scale fault features from vibration signals, greatly improving feature extraction under constrained data conditions. Furthermore, DT-MAML dynamically adjusts the inner-loop learning rate based on task complexity, promoting efficient adaptation to diverse tasks and mitigating domain bias. Comprehensive experimental evaluations on the CWRU bearing dataset, conducted under carefully designed cross-domain scenarios, demonstrate that AdaMETA achieves superior accuracy (up to 99.26%) and robustness compared to traditional meta-learning and classical diagnostic methods. Additional ablation studies and noise interference experiments further validate the substantial contribution of the EMA module and the dynamic learning rate components.<\/jats:p>","DOI":"10.3390\/e27101063","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T10:37:14Z","timestamp":1760438234000},"page":"1063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Dynamic MAML with Efficient Multi-Scale Attention for Cross-Load Few-Shot Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"27","author":[{"given":"Qinglei","family":"Zhang","sequence":"first","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyun","family":"Qin","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Duan","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109753","DOI":"10.1016\/j.ress.2023.109753","article-title":"Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit","volume":"242","author":"Ni","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5254","DOI":"10.1109\/TMECH.2022.3177174","article-title":"Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain","volume":"27","author":"Xiao","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109439","DOI":"10.1016\/j.knosys.2022.109439","article-title":"Data-augmented wavelet capsule generative adversarial network for rolling bearing fault diagnosis","volume":"252","author":"Liu","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.ymssp.2015.10.007","article-title":"K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited","volume":"70","author":"Wang","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mishra, M., and Srivastava, M. (2014, January 1\u20132). A view of artificial neural network. Proceedings of the 2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014), Unnao, India.","DOI":"10.1109\/ICAETR.2014.7012785"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s10462-018-9614-6","article-title":"Problem formulations and solvers in linear SVM: A review","volume":"52","author":"Chauhan","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"86510","DOI":"10.1109\/ACCESS.2020.2992692","article-title":"Convolutional neural network in intelligent fault diagnosis toward rotatory machinery","volume":"8","author":"Tang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107435","DOI":"10.1016\/j.apacoust.2020.107435","article-title":"Research on deep feature learning and condition recognition method for bearing vibration","volume":"168","author":"Zhu","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3513215","DOI":"10.1109\/TIM.2024.3374311","article-title":"Deep learning-based bearing fault diagnosis using a trusted multi-scale quadratic attention-embedded convolutional neural network","volume":"73","author":"Tang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"750","DOI":"10.26599\/TST.2018.9010144","article-title":"A deep adaptive learning method for rolling bearing fault diagnosis using immunity","volume":"24","author":"Tian","year":"2019","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6647","DOI":"10.1007\/s10489-021-02229-1","article-title":"Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM","volume":"51","author":"Zou","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"39664","DOI":"10.1109\/ACCESS.2023.3268534","article-title":"A fault diagnosis method for rolling bearing based on 1D-ViT model","volume":"11","author":"Xu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108616","DOI":"10.1016\/j.ymssp.2021.108616","article-title":"A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings","volume":"168","author":"Ding","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, S., Li, Z., Gu, J., Wang, R., Liu, X., Li, L., Guo, F., and Ren, Y. (2025). CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0319202"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"095104","DOI":"10.1088\/1361-6501\/acd6ac","article-title":"Category-aware dual adversarial domain adaptation model for rolling bearings fault diagnosis under variable conditions","volume":"34","author":"Lu","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012011","DOI":"10.1088\/1742-6596\/2467\/1\/012011","article-title":"Roller bearing fault diagnosis using deep transfer learning and adaptive weighting","volume":"2467","author":"Liu","year":"2023","journal-title":"J. Phys. Conf. Ser. IOP Publ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110001","DOI":"10.1016\/j.ress.2024.110001","article-title":"Meta-learning with Elastic Prototypical Network for Fault Transfer Diagnosis of Bearings under Unstable Speeds","volume":"245","author":"Luo","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110442","DOI":"10.1016\/j.apacoust.2024.110442","article-title":"Recursive Prototypical Network with Coordinate Attention: A Model for Few-Shot Cross-Condition Bearing Fault Diagnosis","volume":"231","author":"Jiang","year":"2024","journal-title":"Appl. Acoust."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"129012","DOI":"10.1016\/j.neucom.2024.129012","article-title":"Prototype Matching-based Meta-Learning Model for Few-Shot Fault Diagnosis of Mechanical System","volume":"617","author":"Lin","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, W., Chen, Y., Li, J., Wen, J., and Chen, J. (2024). Learn Then Adapt: A Novel Test-Time Adaptation Method for Cross-Domain Fault Diagnosis of Rolling Bearings. Electronics, 13.","DOI":"10.3390\/electronics13193898"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108803","DOI":"10.1016\/j.engappai.2024.109261","article-title":"Dictionary Domain Adaptation Transformer for Cross-Machine Fault Diagnosis of Rolling Bearings","volume":"138","author":"Cui","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_22","first-page":"121338","article-title":"LiConvFormer: A Lightweight Fault Diagnosis Framework Using Separable Multiscale Convolution and Broadcast Self-Attention","volume":"237 Pt A","author":"Yan","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115402","DOI":"10.1016\/j.measurement.2024.115402","article-title":"Few-shot Bearing Fault Diagnosis by Semi-supervised Meta-learning with Simplifying Graph Convolution under Variable Working Conditions","volume":"240","author":"Liu","year":"2025","journal-title":"Measurement"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102514","DOI":"10.1016\/j.aei.2024.102514","article-title":"Cloud-Edge Test-Time Adaptation for Cross-Domain Online Machinery Fault Diagnosis via Customized Contrastive Learning","volume":"61","author":"Zhu","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, W., Nie, Y., and Yang, F. (2025). Multi-Variable Transformer-Based Meta-Learning for Few-Shot Fault Diagnosis of Large-Scale Systems. Sensors, 25.","DOI":"10.20944\/preprints202504.0513.v1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103063","DOI":"10.1016\/j.aei.2024.103063","article-title":"Domain Generalization for Rotating Machinery Fault Diagnosis: A Survey","volume":"64","author":"Xiao","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107646","DOI":"10.1016\/j.knosys.2021.107646","article-title":"Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects","volume":"235","author":"Feng","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","article-title":"A survey of deep meta-learning","volume":"54","author":"Huisman","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_29","unstructured":"Yao, H., Wu, X., Tao, Z., Li, Y., Ding, B., Li, R., and Li, Z. (2020). Automated relational meta-learning. arXiv."},{"key":"ref_30","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201312). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, S., Ye, F., Wang, B., and Habetler, T.G. (2020, January 24\u201327). Few-shot bearing anomaly detection via model-agnostic meta-learning. Proceedings of the 2020 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan.","DOI":"10.23919\/ICEMS50442.2020.9291099"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2021.01.099","article-title":"Meta-learning for few-shot bearing fault diagnosis under complex working conditions","volume":"439","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1007\/s10845-024-02365-8","article-title":"A multi scale meta-learning network for cross domain fault diagnosis with limited samples","volume":"36","author":"Wang","year":"2025","journal-title":"J. Intell. Manuf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120696","DOI":"10.1016\/j.eswa.2023.120696","article-title":"Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals","volume":"230","author":"Lin","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.jmapro.2025.01.037","article-title":"A new cross-domain bearing fault diagnosis method with few samples under different working conditions","volume":"135","author":"Dong","year":"2025","journal-title":"J. Manuf. Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109884","DOI":"10.1016\/j.ymssp.2022.109884","article-title":"Deep discriminative transfer learning network for cross-machine fault diagnosis","volume":"186","author":"Qian","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108487","DOI":"10.1016\/j.ymssp.2021.108487","article-title":"A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges","volume":"167","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_39","first-page":"6789","article-title":"Non-deep networks","volume":"35","author":"Goyal","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4\u201310). Efficient multi-scale attention module with cross-spatial learning. Proceedings of the ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"93155","DOI":"10.1109\/ACCESS.2020.2990528","article-title":"Bearing fault detection and diagnosis using Case Western Reserve University dataset with deep learning approaches: A review","volume":"8","author":"Neupane","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.isatra.2021.03.013","article-title":"Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis","volume":"120","author":"Feng","year":"2022","journal-title":"ISA Trans."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/10\/1063\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T10:37:23Z","timestamp":1760438243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/10\/1063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["e27101063"],"URL":"https:\/\/doi.org\/10.3390\/e27101063","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]}}}