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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:

SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples
Xuejian Yao, Xingchi Lu, Quan Jiang, et al.
Advanced Engineering Informatics (2024) Vol. 61, pp. 102560-102560
Closed Access | Times Cited: 11

Showing 11 citing articles:

Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis
Ning Jia, Weiguo Huang, Chuancang Ding, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102774-102774
Closed Access | Times Cited: 10
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
Non-parametric semi-supervised chiller fault diagnosis via variational compressor under severe few labeled samples
Huazheng Han, Xuejin Gao, Huayun Han, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110233-110233
Closed Access
Dual-stage manifold preserving mixed supervised learning for bogie fault diagnosis under variable conditions
Ning Wang, Limin Jia, Yong Qin, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 149, pp. 110512-110512
Closed Access
A transfer learning method: Universal domain adaptation with noisy samples for bearing fault diagnosis
Yi Sun, Hong‐Liang Song, Liang Guo, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103243-103243
Closed Access
Multi-similarity and gradient fusion digital twins for fault detection and diagnosis of rolling bearings
Xiaotian Zhang, Handong Wang, Haiming Yao, et al.
Computers in Industry (2025) Vol. 168, pp. 104273-104273
Closed Access
A novel method for identifying sudden degradation changes in remaining useful life prediction for bearing
Xianhua Chen, Zhigang Tian
Expert Systems with Applications (2025), pp. 127315-127315
Open Access
A feature extension and reconstruction method with incremental learning capabilities under limited samples for intelligent diagnosis
Kui Hu, Zhihao Bi, Qingbo He, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102796-102796
Closed Access | Times Cited: 1
Intelligent diagnosis method of torque-angle dynamometer cards for beam pumping units based on transfer learning
Jincheng Huang, Wenjun Huang, Zi‐Ming Feng, et al.
Geoenergy Science and Engineering (2024) Vol. 241, pp. 213138-213138
Closed Access
A novel semi-supervised prediction modeling method based on deep learning for flotation process with large drift of working conditions
Fanlei Lu, Weihua Gui, Liyang Qin, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102934-102934
Closed Access
Semi-Supervised Contrastive Transfer Learning Network for Fault Diagnosis of Cross-Working Condition Rolling Bearings
Xingchi Lu, Quan Jiang, Zhengchang Su, et al.
(2024), pp. 1-6
Closed Access
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