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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:

Rolling bearing fault diagnosis using optimal ensemble deep transfer network
Xingqiu Li, Hongkai Jiang, Ruixin Wang, et al.
Knowledge-Based Systems (2020) Vol. 213, pp. 106695-106695
Closed Access | Times Cited: 134

Showing 1-25 of 134 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
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
Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions
Liang Chen, Qi Li, Changqing Shen, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 3, pp. 1790-1800
Open Access | Times Cited: 137
A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis
Jinghui Tian, Dongying Han, Mengdi Li, et al.
Knowledge-Based Systems (2022) Vol. 243, pp. 108466-108466
Closed Access | Times Cited: 113
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
Transfer learning based on improved stacked autoencoder for bearing fault diagnosis
Shuyang Luo, Xufeng Huang, Yanzhi Wang, et al.
Knowledge-Based Systems (2022) Vol. 256, pp. 109846-109846
Closed Access | Times Cited: 72
A domain generalization network combing invariance and specificity towards real-time intelligent fault diagnosis
Chao Zhao, Weiming Shen
Mechanical Systems and Signal Processing (2022) Vol. 173, pp. 108990-108990
Closed Access | Times Cited: 71
Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions
Xiaoan Yan, Daoming She, Yadong Xu
Expert Systems with Applications (2022) Vol. 216, pp. 119479-119479
Closed Access | Times Cited: 68
Fault diagnosis in rotating machines based on transfer learning: Literature review
Iqbal Misbah, C.K.M. Lee, K. L. Keung
Knowledge-Based Systems (2023) Vol. 283, pp. 111158-111158
Closed Access | Times Cited: 57
A literature review of fault diagnosis based on ensemble learning
Zhibao Mian, Xiaofei Deng, Xiaohui Dong, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107357-107357
Open Access | Times Cited: 47
MgNet: A fault diagnosis approach for multi-bearing system based on auxiliary bearing and multi-granularity information fusion
Jin Deng, Han Liu, Hairui Fang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 193, pp. 110253-110253
Closed Access | Times Cited: 46
An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model
Hongming Chen, Wei Meng, Yongjian Li, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 4, pp. 045013-045013
Closed Access | Times Cited: 45
A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
Mehdi Saman Azari, Francesco Flammini, Stefania Santini, et al.
IEEE Access (2023) Vol. 11, pp. 12887-12910
Open Access | Times Cited: 42
Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis
Jun Wang, He Ren, Changqing Shen, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109879-109879
Closed Access | Times Cited: 41
A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges
Omri Matania, Itai Dattner, Jacob Bortman, et al.
Journal of Sound and Vibration (2024) Vol. 590, pp. 118562-118562
Closed Access | Times Cited: 15
A lightweight progressive joint transfer ensemble network inspired by the Markov process for imbalanced mechanical fault diagnosis
Changdong Wang, Jingli Yang, Huamin Jie, et al.
Mechanical Systems and Signal Processing (2024) Vol. 224, pp. 111994-111994
Closed Access | Times Cited: 15
A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains
Xingqiu Li, Hongkai Jiang, Min Xie, et al.
Advanced Engineering Informatics (2021) Vol. 51, pp. 101480-101480
Closed Access | Times Cited: 98
Multi-class fuzzy support matrix machine for classification in roller bearing fault diagnosis
Haiyang Pan, Haifeng Xu, Jinde Zheng, et al.
Advanced Engineering Informatics (2021) Vol. 51, pp. 101445-101445
Closed Access | Times Cited: 84
An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data
Xingqiu Li, Hongkai Jiang, Yuan Liu, et al.
Knowledge-Based Systems (2021) Vol. 235, pp. 107652-107652
Closed Access | Times Cited: 73
Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey
Siyu Zhang, Lei Su, Jiefei Gu, et al.
Chinese Journal of Aeronautics (2021) Vol. 36, Iss. 1, pp. 45-74
Open Access | Times Cited: 73
Deep multiple auto-encoder with attention mechanism network: A dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions
Shengkang Yang, Xianguang Kong, Qibin Wang, et al.
Knowledge-Based Systems (2022) Vol. 249, pp. 108639-108639
Closed Access | Times Cited: 63
Balanced Adaptation Regularization Based Transfer Learning for Unsupervised Cross-Domain Fault Diagnosis
Qin Hu, Xiaosheng Si, Aisong Qin, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 12, pp. 12139-12151
Closed Access | Times Cited: 60
Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data
Huan Wang, Zhiliang Liu, Yipei Ge, et al.
Knowledge-Based Systems (2021) Vol. 239, pp. 107978-107978
Closed Access | Times Cited: 56
A deep feature alignment adaptation network for rolling bearing intelligent fault diagnosis
Shaowei Liu, Hongkai Jiang, Yanfeng Wang, et al.
Advanced Engineering Informatics (2022) Vol. 52, pp. 101598-101598
Closed Access | Times Cited: 40
A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis
Zhenghong Wu, Hongkai Jiang, Shaowei Liu, et al.
Advanced Engineering Informatics (2022) Vol. 53, pp. 101651-101651
Closed Access | Times Cited: 40
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