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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:

A new meta-transfer learning method with freezing operation for few-shot bearing fault diagnosis
Peiqi Wang, Jingde Li, Shubei Wang, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 7, pp. 074005-074005
Closed Access | Times Cited: 21

Showing 21 citing articles:

Meta-learning with elastic prototypical network for fault transfer diagnosis of bearings under unstable speeds
Jingjie Luo, Haidong Shao, Jian Lin, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 110001-110001
Closed Access | Times Cited: 89
Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery
Shengnan Tang, Jingtao Ma, Zhengqi Yan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 134, pp. 108678-108678
Closed Access | Times Cited: 30
Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment
Jingchuan Dong, Hongyu Jiang, Depeng Su, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 046006-046006
Closed Access | Times Cited: 8
Few-Shot Class-Incremental Learning with Adjustable Pseudo-Incremental Sessions for Bearing Fault Diagnosis
Hongyan Zhu, Changqing Shen, Jiaan Wang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 12, pp. 19543-19552
Closed Access | Times Cited: 8
A meta-learning method for few-shot bearing fault diagnosis under variable working conditions
Liang Zeng, Junjie Jian, Xinyu Chang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 5, pp. 056205-056205
Open Access | Times Cited: 5
Fault diagnosis of nuclear power plant sliding bearing-rotor systems using deep convolutional generative adversarial networks
Qi Li, Weiwei Zhang, Feiyu Chen, et al.
Nuclear Engineering and Technology (2024) Vol. 56, Iss. 8, pp. 2958-2973
Open Access | Times Cited: 5
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
Rotating Machinery Fault Diagnosis Under Multiple Working Conditions via a Time-Series Transformer Enhanced by Convolutional Neural Network
Zhiqiang Lu, Longyang Liang, Jun Zhu, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Closed Access | Times Cited: 13
Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods
Attiq Ur Rehman, Weidong Jiao, Yonghua Jiang, et al.
Applied Soft Computing (2025), pp. 112785-112785
Closed Access
Reserving embedding space for new fault types: A new continual learning method for bearing fault diagnosis
Hongyan Zhu, Changqing Shen, Lin Li, et al.
Reliability Engineering & System Safety (2024) Vol. 252, pp. 110433-110433
Closed Access | Times Cited: 3
Transfer multiscale adaptive convolutional neural network for few-shot and cross-domain bearing fault diagnosis
F. Li, Liping Wang, Decheng Wang, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125002-125002
Closed Access | Times Cited: 9
Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review
Xiaoxia Liang, Ming Zhang, Guojin Feng, et al.
Sustainability (2023) Vol. 15, Iss. 20, pp. 14975-14975
Open Access | Times Cited: 8
Refining sleep staging accuracy: transfer learning coupled with scorability models
Wolfgang Ganglberger, Samaneh Nasiri, Haoqi Sun, et al.
SLEEP (2024) Vol. 47, Iss. 11
Closed Access | Times Cited: 2
Research on digital twin-assisted bearing fault diagnosis method based on virtual-real mapping
Zhiwu Shang, Xunbo Wang, Cailu Pan, et al.
Measurement Science and Technology (2024) Vol. 36, Iss. 1, pp. 016005-016005
Closed Access | Times Cited: 2
Non-intrusive multi-label load monitoring via transfer and contrastive learning architecture
Ang Gao, Jianyong Zheng, Fei Mei, et al.
International Journal of Electrical Power & Energy Systems (2023) Vol. 154, pp. 109443-109443
Open Access | Times Cited: 4
Special features on continuous learning based condition monitoring for crucial components
Changqing Shen, Xiang Li, Min Xia, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 5, pp. 050101-050101
Closed Access | Times Cited: 1
Small-sample damage detection of bleacher structure based on GAN and MSS-CNN models
Chaozhi Cai, Xiaoyu Guo, Jianhua Ren, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 1
Enhancing Fault Diagnosis in Industrial Processes through Adversarial Task Augmented Sequential Meta-Learning
Dexin Sun, Yunsheng Fan, Guofeng Wang
Applied Sciences (2024) Vol. 14, Iss. 11, pp. 4433-4433
Open Access | Times Cited: 1
Fault diagnosis method for bearings under variable working conditions based on transfer relation network
ran zhang, Zhihong Zhao, Tao Xu, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 12, pp. 126202-126202
Closed Access | Times Cited: 1
A few-shot based phase-batch multi-layer domain adaptation pattern recognition method
Yiming Li, Jiankang Wang, Guangxu Luo, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 076105-076105
Closed Access
Few-shot bearing fault diagnosis based on ResAttn-Siamese
Gang Wu, Ling Zhao, Hangjun Wu, et al.
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2024), pp. 1-6
Closed Access
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