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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:

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
Liang Guo, Yaguo Lei, Saibo Xing, et al.
IEEE Transactions on Industrial Electronics (2018) Vol. 66, Iss. 9, pp. 7316-7325
Closed Access | Times Cited: 988

Showing 1-25 of 988 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
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
Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review
Shen Zhang, Shibo Zhang, Bingnan Wang, et al.
IEEE Access (2020) Vol. 8, pp. 29857-29881
Open Access | Times Cited: 591
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
Artificial intelligence for the metaverse: A survey
Thien Huynh‐The, Quoc‐Viet Pham, Xuan‐Qui Pham, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 117, pp. 105581-105581
Open Access | Times Cited: 465
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
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
Deep separable convolutional network for remaining useful life prediction of machinery
Biao Wang, Yaguo Lei, Naipeng Li, et al.
Mechanical Systems and Signal Processing (2019) Vol. 134, pp. 106330-106330
Closed Access | Times Cited: 307
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
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
Bo Zhao, Xianmin Zhang, Hai Li, et al.
Knowledge-Based Systems (2020) Vol. 199, pp. 105971-105971
Closed Access | Times Cited: 299
Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning
Yibin Li, Yan Song, Lei Jia, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 4, pp. 2833-2841
Closed Access | Times Cited: 294
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: 283
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
A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions
Jun Zhu, Nan Chen, Changqing Shen
Mechanical Systems and Signal Processing (2020) Vol. 139, pp. 106602-106602
Closed Access | Times Cited: 251
A new bearing fault diagnosis method based on modified convolutional neural networks
Jiangquan Zhang, Yi Sun, Liang Guo, et al.
Chinese Journal of Aeronautics (2019) Vol. 33, Iss. 2, pp. 439-447
Open Access | Times Cited: 250
Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review
Mohd Syahril Ramadhan Mohd Saufi, Zair Asrar Ahmad, M. Salman Leong, et al.
IEEE Access (2019) Vol. 7, pp. 122644-122662
Open Access | Times Cited: 226
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
Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data
Cheng Cheng, Beitong Zhou, Guijun Ma, et al.
Neurocomputing (2020) Vol. 409, pp. 35-45
Open Access | Times Cited: 211
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: 210
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