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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Transformer-based meta learning method for bearing fault identification under multiple small sample conditions
Xianze Li, Hao Su, Ling Xiang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 208, pp. 110967-110967
Closed Access | Times Cited: 25

Showing 25 citing articles:

Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
An information fusion-based meta transfer learning method for few-shot fault diagnosis under varying operating conditions
Cuiying Lin, Yun Kong, Qinkai Han, et al.
Mechanical Systems and Signal Processing (2024) Vol. 220, pp. 111652-111652
Closed Access | Times Cited: 17
Spatio-temporal correlation-based multiple regression for anomaly detection and recovery of unmanned aerial vehicle flight data
Lei Yang, Shaobo Li, Caichao Zhu, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102440-102440
Open Access | Times Cited: 8
TSoft-Net: A novel transfer soft thresholding network based on self-attention for intelligent fault diagnosis of rotating machinery
Shihang Yu, Shanchen Pang, Limei Song, et al.
Measurement (2024) Vol. 227, pp. 114237-114237
Closed Access | Times Cited: 5
Autonomous obstacle avoidance and target tracking of UAV: Transformer for observation sequence in reinforcement learning
Weilai Jiang, Tianqing Cai, Guoqiang Xu, et al.
Knowledge-Based Systems (2024) Vol. 290, pp. 111604-111604
Closed Access | Times Cited: 5
A meta-learning method based on meta-feature enhancement for bearing fault identification under few-sample conditions
Xianze Li, Guopeng Zhu, Aijun Hu, et al.
Mechanical Systems and Signal Processing (2025) Vol. 226, pp. 112370-112370
Closed Access
Adaptive process parameters decision-making in robotic grinding based on meta-reinforcement learning
Jie Pan, Fan Chen, Dan Han, et al.
Journal of Manufacturing Processes (2025) Vol. 137, pp. 376-396
Closed Access
Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions
Ryad Zemouri
Machines (2025) Vol. 13, Iss. 2, pp. 125-125
Open Access
A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges
Jingxin ZHAO, Wenming Wang, Ji Huang, et al.
AIP Advances (2025) Vol. 15, Iss. 2
Open Access
Deep-sea pipeline damage identification using digital twin-assisted enhanced meta-transfer learning
Jianxing Yu, Zihang Jin, Yang Yu, et al.
Ocean Engineering (2025) Vol. 324, pp. 120723-120723
Closed Access
Transformer-based conditional generative transfer learning network for cross domain fault diagnosis under limited data
Xiang Li, Jun Ma, Jiande Wu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Memory-Augmented Prototypical Meta-Learning Method for Bearing Fault Identification under Few-Sample Conditions
Xianze Li, Zhitai Xing, Ling Xiang, et al.
Neurocomputing (2025), pp. 129996-129996
Closed Access
Dynamic model-assisted disentanglement framework for rolling bearing fault diagnosis under time-varying speed conditions
Yuhui Xu, Yimin Jiang, Tangbin Xia, et al.
Mechanical Systems and Signal Processing (2025) Vol. 230, pp. 112588-112588
Closed Access
Biologically inspired compound defect detection using a spiking neural network with continuous time–frequency gradients
Zisheng Wang, Shaochen Li, Jianping Xuan, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103132-103132
Closed Access
Task-adaptive unbiased regularization meta-learning for few-shot cross-domain fault diagnosis
Huaqing Wang, Dongrui Lv, Tianjiao Lin, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110200-110200
Closed Access
A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions
Ling Xiang, Hankun Bing, Xianze Li, et al.
Expert Systems with Applications (2024) Vol. 262, pp. 125686-125686
Closed Access | Times Cited: 3
Intelligent Fault Diagnosis Across-Datasets Based on Second-Level Sequencing Meta-Learning for Small Samples
Ouyang Chengda, Noramalina Abdullah
IEEE Access (2024) Vol. 12, pp. 85376-85387
Open Access | Times Cited: 2
Estimating crop leaf area index and chlorophyll content using a deep learning-based hyperspectral analysis method
Jibo Yue, Jian Wang, Zhaoying Zhang, et al.
Computers and Electronics in Agriculture (2024) Vol. 227, pp. 109653-109653
Closed Access | Times Cited: 2
A path-dependent adaptive physics-informed neural network for multiaxial fatigue life prediction
H. Liao, Jun Pan, Xinren Su, et al.
International Journal of Fatigue (2024) Vol. 193, pp. 108799-108799
Closed Access | Times Cited: 2
A novel decoupling method of compound faults with incomplete dataset of rotating machinery
Aijun Hu, Bowen Yang, Lei Xing, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086136-086136
Open Access
A Meta-Learning Method for Few-Shot Multi-Domain State of Health Estimation of Lithium-ion Batteries
Xiaoyu Zhao, Zuolu Wang, Te Han, et al.
IEEE Transactions on Transportation Electrification (2024) Vol. 11, Iss. 1, pp. 4830-4840
Closed Access
Enhanced landslide susceptibility mapping in data-scarce regions via unsupervised few-shot learning
L. S. L. Kong, Wenkai Feng, Xiaoyu Yi, et al.
Gondwana Research (2024) Vol. 138, pp. 31-46
Closed Access
A meta transfer learning-driven few-shot fault diagnosis method for combine harvester gearboxes
Daoming She, Zhichao Yang, Yudan Duan, et al.
Computers and Electronics in Agriculture (2024) Vol. 227, pp. 109605-109605
Closed Access
Improving bearing fault diagnosis method based on the fusion of time- frequency diagram and a novel vision transformer
Jingyuan Wang, Yuan Zhao, Wenyan Wang, et al.
Research Square (Research Square) (2024)
Open Access
Improving bearing fault diagnosis method based on the fusion of time–frequency diagram and a novel vision transformer
Jingyuan Wang, Yuan Zhao, Wenyan Wang, et al.
The Journal of Supercomputing (2024) Vol. 81, Iss. 1
Closed Access
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