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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:

An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings
Bin Yang, Yaguo Lei, Feng Jia, et al.
Mechanical Systems and Signal Processing (2019) Vol. 122, pp. 692-706
Closed Access | Times Cited: 707

Showing 1-25 of 707 citing articles:

Applications of machine learning to machine fault diagnosis: A review and roadmap
Yaguo Lei, Bin Yang, Xinwei Jiang, et al.
Mechanical Systems and Signal Processing (2020) Vol. 138, pp. 106587-106587
Open Access | Times Cited: 1948
A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Weihua Li, Ruyi Huang, Jipu Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 167, pp. 108487-108487
Open Access | Times Cited: 523
A comprehensive review on convolutional neural network in machine fault diagnosis
Jinyang Jiao, Ming Zhao, Jing Lin, et al.
Neurocomputing (2020) Vol. 417, pp. 36-63
Open Access | Times Cited: 403
Big data analytics for intelligent manufacturing systems: A review
Junliang Wang, Chuqiao Xu, Jie Zhang, et al.
Journal of Manufacturing Systems (2021) Vol. 62, pp. 738-752
Closed Access | Times Cited: 361
Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images
Haidong Shao, Min Xia, Guangjie Han, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 5, pp. 3488-3496
Open Access | Times Cited: 350
A systematic review of deep transfer learning for machinery fault diagnosis
Chuan Li, Shaohui Zhang, Qin Yi, et al.
Neurocomputing (2020) Vol. 407, pp. 121-135
Closed Access | Times Cited: 348
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-28
Open Access | Times Cited: 325
Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning
Ansi Zhang, Shaobo Li, Yuxin Cui, et al.
IEEE Access (2019) Vol. 7, pp. 110895-110904
Open Access | Times Cited: 299
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Zhang We, Xiang Li, Xiaodong Jia, et al.
Measurement (2019) Vol. 152, pp. 107377-107377
Closed Access | Times Cited: 299
A review on prognostics and health management (PHM) methods of lithium-ion batteries
Huixing Meng, Yan‐Fu Li
Renewable and Sustainable Energy Reviews (2019) Vol. 116, pp. 109405-109405
Closed Access | Times Cited: 291
Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning
Wentao Mao, Jianliang He, Ming J. Zuo
IEEE Transactions on Instrumentation and Measurement (2019) Vol. 69, Iss. 4, pp. 1594-1608
Closed Access | Times Cited: 270
Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning
Xiang Li, Zhang We, Qian Ding, et al.
IEEE Transactions on Industrial Informatics (2019) Vol. 16, Iss. 3, pp. 1688-1697
Closed Access | Times Cited: 254
Multi-scale deep intra-class transfer learning for bearing fault diagnosis
Xu Wang, Changqing Shen, Min Xia, et al.
Reliability Engineering & System Safety (2020) Vol. 202, pp. 107050-107050
Open Access | Times Cited: 252
An adaptive deep transfer learning method for bearing fault diagnosis
Zhenghong Wu, Hongkai Jiang, Ke Zhao, et al.
Measurement (2019) Vol. 151, pp. 107227-107227
Closed Access | Times Cited: 238
State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach
Jinpeng Tian, Rui Xiong, Weixiang Shen, et al.
Applied Energy (2021) Vol. 291, pp. 116812-116812
Closed Access | Times Cited: 233
Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder
Zhiyi He, Haidong Shao, Jing Lin, et al.
Measurement (2019) Vol. 152, pp. 107393-107393
Closed Access | Times Cited: 232
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
Behnoush Rezaeianjouybari, Yi Shang
Measurement (2020) Vol. 163, pp. 107929-107929
Closed Access | Times Cited: 231
A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
Kun Yu, Tian Ran Lin, Hui Ma, et al.
Mechanical Systems and Signal Processing (2020) Vol. 146, pp. 107043-107043
Closed Access | Times Cited: 230
Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain
Yiming Xiao, Haidong Shao, Song-Yu Han, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 27, Iss. 6, pp. 5254-5263
Closed Access | Times Cited: 225
Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
Shengnan Tang, Shouqi Yuan, Yong Zhu
IEEE Access (2019) Vol. 8, pp. 9335-9346
Open Access | Times Cited: 224
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Zhang We, Xiang Li, Hui Ma, et al.
Knowledge-Based Systems (2020) Vol. 213, pp. 106679-106679
Closed Access | Times Cited: 224
A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions
Jun Zhu, Nan Chen, Changqing Shen
IEEE Sensors Journal (2019) Vol. 20, Iss. 15, pp. 8394-8402
Closed Access | Times Cited: 220
Knowledge Transfer for Rotary Machine Fault Diagnosis
Ruqiang Yan, Fei Shen, Chuang Sun, et al.
IEEE Sensors Journal (2019) Vol. 20, Iss. 15, pp. 8374-8393
Closed Access | Times Cited: 215
A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines
Bin Yang, Yaguo Lei, Feng Jia, et al.
IEEE Transactions on Industrial Electronics (2019) Vol. 67, Iss. 11, pp. 9747-9757
Closed Access | Times Cited: 207
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
Zhiyi He, Haidong Shao, Xiang Zhong, et al.
Knowledge-Based Systems (2020) Vol. 207, pp. 106396-106396
Closed Access | Times Cited: 206
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