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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:

Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis
Zhongwei Zhang, Huaihai Chen, Shunming Li, et al.
Measurement (2020) Vol. 165, pp. 108071-108071
Open Access | Times Cited: 60

Showing 1-25 of 60 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: 527
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
Tianci Zhang, Jinglong Chen, Fudong Li, et al.
ISA Transactions (2021) Vol. 119, pp. 152-171
Closed Access | Times Cited: 371
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: 332
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: 194
A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis
Quan Qian, Yi Qin, Yi Wang, et al.
Measurement (2021) Vol. 178, pp. 109352-109352
Closed Access | Times Cited: 144
Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery
Anil Kumar, Govind Vashishtha, C.P. Gandhi, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-10
Closed Access | Times Cited: 137
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
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: 61
Deep causal factorization network: A novel domain generalization method for cross-machine bearing fault diagnosis
Sixiang Jia, Yongbo Li, Xinyue Wang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 192, pp. 110228-110228
Closed Access | Times Cited: 49
Joint distribution adaptation with diverse feature aggregation: A new transfer learning framework for bearing diagnosis across different machines
Shiyao Jia, Yafei Deng, Jun Lv, et al.
Measurement (2021) Vol. 187, pp. 110332-110332
Closed Access | Times Cited: 89
Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions
Baokun Han, Xiao Zhang, Jinrui Wang, et al.
Measurement (2021) Vol. 176, pp. 109197-109197
Closed Access | Times Cited: 87
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: 75
Joint learning system based on semi–pseudo–label reliability assessment for weak–fault diagnosis with few labels
Dawei Gao, Yongsheng Zhu, Ke Yan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 189, pp. 110089-110089
Closed Access | Times Cited: 32
A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data
Wenbo Hou, Chunlin Zhang, Yunqian Jiang, et al.
Measurement (2023) Vol. 215, pp. 112879-112879
Closed Access | Times Cited: 31
A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis
Guiting Tang, Cai Yi, Lei Liu, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106395-106395
Closed Access | Times Cited: 31
Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction
Ismail Nejjar, Fabian Geissmann, Mengjie Zhao, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109718-109718
Open Access | Times Cited: 22
PhysiCausalNet: A Causal- and Physics-Driven Domain Generalization Network for Cross-Machine Fault Diagnosis of Unseen Domain
Yumeng Zhu, Yanyang Zi, Jie Li, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 6, pp. 8488-8498
Closed Access | Times Cited: 8
Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects
Zhuohang Chen, Jinglong Chen, Yong Feng, et al.
Knowledge-Based Systems (2022) Vol. 258, pp. 110008-110008
Closed Access | Times Cited: 35
A balanced and weighted alignment network for partial transfer fault diagnosis
Chao Zhao, Guokai Liu, Weiming Shen
ISA Transactions (2022) Vol. 130, pp. 449-462
Closed Access | Times Cited: 30
The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network
Xuejun Liu, Wei Sun, Hongkun Li, et al.
Energies (2022) Vol. 15, Iss. 13, pp. 4614-4614
Open Access | Times Cited: 29
Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data
Xuan Liu, Jinglong Chen, Kaiyu Zhang, et al.
Knowledge-Based Systems (2022) Vol. 251, pp. 109272-109272
Closed Access | Times Cited: 28
Multi-sensor signals multi-scale fusion method for fault detection of high-speed and high-power diesel engine under variable operating conditions
Jiaqi Liang, Zhiwei Mao, Fengchun Liu, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106912-106912
Closed Access | Times Cited: 21
A novel lightweight relation network for cross-domain few-shot fault diagnosis
Tang Tang, Chuanhang Qiu, Tianyuan Yang, et al.
Measurement (2023) Vol. 213, pp. 112697-112697
Closed Access | Times Cited: 17
Leveraging Meta-Learning To Improve Unsupervised Domain Adaptation
Amirfarhad Farhadi, Arash Sharifi
The Computer Journal (2023) Vol. 67, Iss. 5, pp. 1838-1850
Closed Access | Times Cited: 17
Unsupervised Domain Adaptation for Hvac Fault Diagnosis Using Contrastive Adaptation Network
Naghmeh Ghalamsiah, Jin Wen, K. Selçuk Candan, et al.
(2025)
Closed Access
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