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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:

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

Showing 1-25 of 215 citing articles:

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: 520
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao, Tianfu Li, Jingyao Wu, et al.
ISA Transactions (2020) Vol. 107, pp. 224-255
Open Access | Times Cited: 425
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
Zhenghua Chen, Min Wu, Rui Zhao, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 3, pp. 2521-2531
Closed Access | Times Cited: 394
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
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
Jorge Arinez, Qing Chang, Robert X. Gao, et al.
Journal of Manufacturing Science and Engineering (2020) Vol. 142, Iss. 11
Open Access | Times Cited: 338
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
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhiqin Zhu, Yangbo Lei, Guanqiu Qi, et al.
Measurement (2022) Vol. 206, pp. 112346-112346
Closed Access | Times Cited: 281
Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning
Min Xia, Haidong Shao, Darren L. Williams, et al.
Reliability Engineering & System Safety (2021) Vol. 215, pp. 107938-107938
Open Access | Times Cited: 262
Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery
Zhuyun Chen, Guolin He, Jipu Li, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 69, Iss. 11, pp. 8702-8712
Open Access | Times Cited: 209
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
Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions
Tianfu Li, Zhibin Zhao, Chuang Sun, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-10
Closed Access | Times Cited: 195
Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
Xiaohan Chen, Rui Yang, Yihao Xue, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-21
Open Access | Times Cited: 185
A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions
Te Han, Yan‐Fu Li, Min Qian
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 178
Deep Learning-Based Partial Domain Adaptation Method on Intelligent Machinery Fault Diagnostics
Xiang Li, Zhang We
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 5, pp. 4351-4361
Closed Access | Times Cited: 169
A Two-Stage Transfer Adversarial Network for Intelligent Fault Diagnosis of Rotating Machinery With Multiple New Faults
Jipu Li, Ruyi Huang, Guolin He, et al.
IEEE/ASME Transactions on Mechatronics (2020) Vol. 26, Iss. 3, pp. 1591-1601
Open Access | Times Cited: 161
Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion
Hongfeng Tao, Jier Qiu, Yiyang Chen, et al.
Journal of the Franklin Institute (2022) Vol. 360, Iss. 2, pp. 1454-1477
Closed Access | Times Cited: 157
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Taotao Zhou, Te Han, Enrique López Droguett
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108525-108525
Closed Access | Times Cited: 153
A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines
Jun Zhu, Nan Chen, Changqing Shen
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 7, pp. 4788-4797
Closed Access | Times Cited: 146
Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network
Xingkai Chen, Haidong Shao, Yiming Xiao, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110427-110427
Open Access | Times Cited: 146
Deep Semi-supervised Domain Generalization Network for Rotary Machinery Fault Diagnosis under Variable Speed
Yixiao Liao, Ruyi Huang, Jipu Li, et al.
IEEE Transactions on Instrumentation and Measurement (2020), pp. 1-1
Closed Access | Times Cited: 140
Entropy Measures in Machine Fault Diagnosis: Insights and Applications
Zhiqiang Huo, Miguel Martínez-García, Yu Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 69, Iss. 6, pp. 2607-2620
Open Access | Times Cited: 138
A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
Mohammed Hakim, Abdoulhdi A. Borhana Omran, Ali Najah Ahmed, et al.
Ain Shams Engineering Journal (2022) Vol. 14, Iss. 4, pp. 101945-101945
Open Access | Times Cited: 129
A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery
Zhuyun Chen, Yixiao Liao, Jipu Li, et al.
IEEE Transactions on Cybernetics (2022) Vol. 53, Iss. 3, pp. 1982-1993
Closed Access | Times Cited: 108
Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations
Kai Zhou, Edward Diehl, Jiong Tang
Mechanical Systems and Signal Processing (2022) Vol. 185, pp. 109772-109772
Open Access | Times Cited: 106
Universal source-free domain adaptation method for cross-domain fault diagnosis of machines
Yongchao Zhang, Zhaohui Ren, Ke Feng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 191, pp. 110159-110159
Closed Access | Times Cited: 90
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