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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:

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

Showing 1-25 of 252 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
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
Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Andreas Theissler, Judith Pérez-Velázquez, Marcel Kettelgerdes, et al.
Reliability Engineering & System Safety (2021) Vol. 215, pp. 107864-107864
Open Access | Times Cited: 300
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
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
Omar AlShorman, Muhammad Irfan, Nordin Saad, et al.
Shock and Vibration (2020) Vol. 2020, pp. 1-20
Open Access | Times Cited: 162
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
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
Te Han, Yan‐Fu Li
Reliability Engineering & System Safety (2022) Vol. 226, pp. 108648-108648
Closed Access | Times Cited: 156
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
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
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
Yifei Ding, Jichao Zhuang, Peng Ding, et al.
Reliability Engineering & System Safety (2021) Vol. 218, pp. 108126-108126
Closed Access | Times Cited: 148
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
Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network
Xiaoli Zhao, Jianyong Yao, Wenxiang Deng, et al.
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 34, Iss. 9, pp. 6339-6353
Closed Access | Times Cited: 127
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Bayu Adhi Tama, Malinda Vania, Seung‐Chul Lee, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 5, pp. 4667-4709
Open Access | Times Cited: 120
Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform
Shengnan Tang, Yong Zhu, Shouqi Yuan
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108560-108560
Closed Access | Times Cited: 110
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
Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Chuanjiang Li, Shaobo Li, Huan Wang, et al.
Knowledge-Based Systems (2023) Vol. 264, pp. 110345-110345
Open Access | Times Cited: 108
Application of recurrent neural network to mechanical fault diagnosis: a review
Junjun Zhu, Quan Jiang, Yehu Shen, et al.
Journal of Mechanical Science and Technology (2022) Vol. 36, Iss. 2, pp. 527-542
Closed Access | Times Cited: 105
An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition
Jiusi Zhang, Ke Zhang, Yiyao An, et al.
IEEE Transactions on Neural Networks and Learning Systems (2023) Vol. 35, Iss. 5, pp. 6231-6242
Closed Access | Times Cited: 104
Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy
Ke Zhao, Junchen Hu, Haidong Shao, et al.
Reliability Engineering & System Safety (2023) Vol. 236, pp. 109246-109246
Closed Access | Times Cited: 93
A multi-layer spiking neural network-based approach to bearing fault diagnosis
Lin Zuo, Fengjie Xu, Changhua Zhang, et al.
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108561-108561
Closed Access | Times Cited: 85
Dual adversarial network for cross-domain open set fault diagnosis
Chao Zhao, Weiming Shen
Reliability Engineering & System Safety (2022) Vol. 221, pp. 108358-108358
Closed Access | Times Cited: 84
Multiscale Deep Graph Convolutional Networks for Intelligent Fault Diagnosis of Rotor-Bearing System Under Fluctuating Working Conditions
Xiaoli Zhao, Jianyong Yao, Wenxiang Deng, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 1, pp. 166-176
Closed Access | Times Cited: 78
Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching
Shaowei Liu, Hongkai Jiang, Zhenghong Wu, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 109036-109036
Closed Access | Times Cited: 75
A survey of transfer learning for machinery diagnostics and prognostics
Siya Yao, Qi Kang, MengChu Zhou, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 4, pp. 2871-2922
Closed Access | Times Cited: 74
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