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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:

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

Showing 1-25 of 254 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: 523
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane, Jongwon Seok
IEEE Access (2020) Vol. 8, pp. 93155-93178
Open Access | Times Cited: 373
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
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi, et al.
Journal Of Big Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 320
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 of Deep Transfer Learning and Recent Advancements
Mohammadreza Iman, Hamid R. Arabnia, Khaled Rasheed
Technologies (2023) Vol. 11, Iss. 2, pp. 40-40
Open Access | Times Cited: 291
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
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
Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning
Zhang We, Xiang Li, Hui Ma, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 17, Iss. 12, pp. 7957-7967
Closed Access | Times Cited: 206
Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning
Zhang We, Xiang Li, Hui Ma, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 17, Iss. 11, pp. 7445-7455
Closed Access | Times Cited: 185
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 transfer learning with limited data for machinery fault diagnosis
Te Han, Chao Liu, Rui Wu, et al.
Applied Soft Computing (2021) Vol. 103, pp. 107150-107150
Closed Access | Times Cited: 170
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
Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation
Zhang We, Xiang Li, Xu Li
Measurement (2020) Vol. 164, pp. 108052-108052
Closed Access | Times Cited: 162
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: 149
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Xiang Li, Zhang We, Hui Ma, et al.
Knowledge-Based Systems (2020) Vol. 197, pp. 105843-105843
Closed Access | Times Cited: 147
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: 147
Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks With Data Privacy
Zhang We, Xiang Li
IEEE/ASME Transactions on Mechatronics (2021) Vol. 27, Iss. 1, pp. 430-439
Closed Access | Times Cited: 146
Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery
Xiang Li, Xu Li, Hui Ma
Mechanical Systems and Signal Processing (2020) Vol. 143, pp. 106825-106825
Closed Access | Times Cited: 137
Federated Transfer Learning Based Cross-Domain Prediction for Smart Manufacturing
Kevin I‐Kai Wang, Xiaokang Zhou, Wei Liang, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 6, pp. 4088-4096
Closed Access | Times Cited: 125
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Jian Lin, Haidong Shao, Xiangdong Zhou, et al.
Expert Systems with Applications (2023) Vol. 230, pp. 120696-120696
Open Access | Times Cited: 118
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions
Zhang We, Xiang Li
Structural Health Monitoring (2021) Vol. 21, Iss. 4, pp. 1329-1344
Closed Access | Times Cited: 104
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