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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:

Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments
Xiaoan Yan, Yanyu Lü, Ying Liu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125046-125046
Closed Access | Times Cited: 11

Showing 11 citing articles:

MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
Maoyou Ye, Xiaoan Yan, Xing Hua, et al.
Expert Systems with Applications (2024) Vol. 259, pp. 125214-125214
Closed Access | Times Cited: 11
A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis
Xiao Yang, Fengrong Bi, Jiangang Cheng, et al.
Sensors (2024) Vol. 24, Iss. 9, pp. 2708-2708
Open Access | Times Cited: 5
Attention activation network for bearing fault diagnosis under various noise environments
Yu Zhang, Lianlei Lin, Junkai Wang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Multi-scale quadratic convolutional neural network for bearing fault diagnosis based on multi-sensor data fusion
Yingying Ji, Jun Gao, Xing Shao, et al.
Nonlinear Dynamics (2025)
Closed Access
A small sample bearing fault diagnosis method based on ConvGRU relation network
Zhihong Zhao, ran zhang
Measurement Science and Technology (2024) Vol. 35, Iss. 6, pp. 066201-066201
Closed Access | Times Cited: 3
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
A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network
B.H. Zhu, Chunmeng He
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 106115-106115
Closed Access | Times Cited: 1
Deep Domain Adaptation Approach Using an Improved Parallel Residual Network for Cross‐Domain Bearing Fault Diagnosis
Jeng-Jie Huang
Shock and Vibration (2024) Vol. 2024, Iss. 1
Open Access
Fault Detection Based on Improved VAE-PPA
Mengyuan Xin, Yuan Li
(2024), pp. 258-262
Closed Access
Rapid learning of bearing signal pattern using CfCs promoted by a self-attention mechanism
Yanli Yang, Pan Wei-sheng, Huimin Zhou
Measurement Science and Technology (2023) Vol. 35, Iss. 3, pp. 035010-035010
Closed Access | Times Cited: 1
CWDCGAN-GP Fault Diagnosis Method for Rolling Bearing Under Imbalanced Sample Conditions
Xinna Ma, Yiyang Li, Yu Tang, et al.
(2023), pp. 226-230
Closed Access
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