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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:

Rolling bearing fault diagnosis using optimal ensemble deep transfer network
Xingqiu Li, Hongkai Jiang, Ruixin Wang, et al.
Knowledge-Based Systems (2020) Vol. 213, pp. 106695-106695
Closed Access | Times Cited: 134

Showing 51-75 of 134 citing articles:

Unlocking the power of knowledge for few-shot fault diagnosis: A review from a knowledge perspective
Pei Ling Lai, Fan Zhang, Tianrui Li, et al.
Information Sciences (2025), pp. 121996-121996
Closed Access
Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network
Peijie Lin, Feng Guo, Yaohai Lin, et al.
Applied Energy (2025) Vol. 386, pp. 125578-125578
Closed Access
A classification-driven neuron-grouped SAE for feature representation and its application to fault classification in chemical processes
Zhuofu Pan, Yalin Wang, Xiaofeng Yuan, et al.
Knowledge-Based Systems (2021) Vol. 230, pp. 107350-107350
Closed Access | Times Cited: 28
Deep domain adaptation with adversarial idea and coral alignment for transfer fault diagnosis of rolling bearing
Ranran Li, Shunming Li, Kun Xu, et al.
Measurement Science and Technology (2021) Vol. 32, Iss. 9, pp. 094009-094009
Closed Access | Times Cited: 27
Novel intelligent diagnosis method of oil and gas pipeline defects with transfer deep learning and feature fusion
Junming Yao, Wei Liang, Jingyi Xiong
International Journal of Pressure Vessels and Piping (2022) Vol. 200, pp. 104781-104781
Closed Access | Times Cited: 20
A Transfer Ensemble Learning Method for Evaluating Power Transformer Health Conditions with Limited Measurement Data
Jun Lin, Jin Ma, Jianguo Zhu, et al.
IEEE Transactions on Instrumentation and Measurement (2022), pp. 1-1
Closed Access | Times Cited: 19
Intelligent fault diagnosis of hydroelectric units based on radar maps and improved GoogleNet by depthwise separate convolution
Yunhe Wang, Yidong Zou, Wenqing Hu, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 2, pp. 025103-025103
Closed Access | Times Cited: 12
Fault Diagnosis for Power Converters Based on Incremental Learning
Shiqi Zhang, Rongjie Wang, Libao Wang, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-13
Closed Access | Times Cited: 11
Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer
Fengyun Xie, Wang Gan, Haiyan Zhu, et al.
Electronics (2023) Vol. 12, Iss. 16, pp. 3515-3515
Open Access | Times Cited: 10
Failure Mechanism Information-Assisted Multi-Domain Adversarial Transfer Fault Diagnosis Model for Rolling Bearings under Variable Operating Conditions
Zhidan Zhong, Zhihui Zhang, Yunhao Cui, et al.
Electronics (2024) Vol. 13, Iss. 11, pp. 2133-2133
Open Access | Times Cited: 3
A rolling bearing fault diagnosis method using novel lightweight neural network
Deqiang He, Chenyu Liu, Yanjun Chen, et al.
Measurement Science and Technology (2021) Vol. 32, Iss. 12, pp. 125102-125102
Closed Access | Times Cited: 25
PredMaX: Predictive maintenance with explainable deep convolutional autoencoders
Gergely Hajgató, Richárd Wéber, Botond Szilágyi, et al.
Advanced Engineering Informatics (2022) Vol. 54, pp. 101778-101778
Open Access | Times Cited: 17
Performance Degradation Prediction Using LSTM with Optimized Parameters
Yawei Hu, Ran Wei, Yang Yang, et al.
Sensors (2022) Vol. 22, Iss. 6, pp. 2407-2407
Open Access | Times Cited: 16
An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions
Wangpeng He, Jing Chen, Yue Zhou, et al.
Sensors (2022) Vol. 22, Iss. 23, pp. 9175-9175
Open Access | Times Cited: 16
A Novel Periodic Cyclic Sparse Network With Entire Domain Adaptation for Deep Transfer Fault Diagnosis of Rolling Bearing
Xing Zhan, Cai Yi, Jianhui Lin, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 12, pp. 13452-13468
Closed Access | Times Cited: 9
Deep semi-supervised transfer learning method on few source data with sensitivity-aware decision boundary adaptation for intelligent fault diagnosis
Zhiheng Su, Jiyang Zhang, Hongbing Xu, et al.
Expert Systems with Applications (2024) Vol. 249, pp. 123714-123714
Closed Access | Times Cited: 3
A new multi-layer adaptation cross-domain model for bearing fault diagnosis under different operating conditions
Huaiqian Bao, Lingtan Kong, Limei Lu, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 106116-106116
Closed Access | Times Cited: 3
Towards Prediction Constraints: A Novel Domain Adaptation Method for Machine Fault Diagnosis
Jinyang Jiao, Kaixuan Liang, Chuancang Ding, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 10, pp. 7198-7207
Closed Access | Times Cited: 22
Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions
Gye-Bong Jang, Sung-Bae Cho
Sensors (2021) Vol. 21, Iss. 4, pp. 1417-1417
Open Access | Times Cited: 20
Federated learning for intelligent fault diagnosis based on similarity collaboration
Yonghong Zhang, Xingan Xue, Xiaoping Zhao, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 4, pp. 045103-045103
Closed Access | Times Cited: 14
Simulation Data-driven Enhanced Unsupervised Domain Adaptation for Bearing Fault Diagnosis
Haidong Shao, Yiming Xiao, Shen Yan
Journal of Mechanical Engineering (2023) Vol. 59, Iss. 3, pp. 76-76
Open Access | Times Cited: 8
Deep dynamic adaptation network: a deep transfer learning framework for rolling bearing fault diagnosis under variable working conditions
Huoyao Xu, Jie Liu, Xiangyu Peng, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2022) Vol. 45, Iss. 1
Closed Access | Times Cited: 13
Randomization-based neural networks for image-based wind turbine fault diagnosis
Junda Wang, Yang Yang, Ning Li
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 106028-106028
Closed Access | Times Cited: 7
Feature-oriented unified dictionary learning-based sparse classification for multi-domain fault diagnosis
Xiaofeng Liu, Junfeng Li, Lin Bo, et al.
Signal Processing (2024) Vol. 221, pp. 109485-109485
Closed Access | Times Cited: 2
Feature Adaptive Modulation and Prototype Learning for Domain Generalization Intelligent Fault Diagnosis
Kaixiong Xu, Huafeng Li, Yi Chai, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 10, pp. 12363-12374
Closed Access | Times Cited: 2

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