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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 imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions
Yifei Ding, Minping Jia, Jichao Zhuang, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108890-108890
Closed Access | Times Cited: 125

Showing 1-25 of 125 citing articles:

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 novel data augmentation approach to fault diagnosis with class-imbalance problem
Jilun Tian, Yuchen Jiang, Jiusi Zhang, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109832-109832
Closed Access | Times Cited: 50
A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern
Pengcheng Xia, Yixiang Huang, Zhiyu Tao, et al.
Reliability Engineering & System Safety (2023) Vol. 235, pp. 109256-109256
Closed Access | Times Cited: 42
Rolling Bearing Fault Diagnosis Under Data Imbalance and Variable Speed Based on Adaptive Clustering Weighted Oversampling
Sai Li, Yanfeng Peng, Yiping Shen, et al.
Reliability Engineering & System Safety (2024) Vol. 244, pp. 109938-109938
Closed Access | Times Cited: 42
Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing
Lingli Cui, Yongchang Xiao, Dongdong Liu, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 109991-109991
Closed Access | Times Cited: 41
Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization
Shen Yan, Xiang Zhong, Haidong Shao, et al.
Reliability Engineering & System Safety (2023) Vol. 239, pp. 109522-109522
Open Access | Times Cited: 40
Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery
Shengnan Tang, Jingtao Ma, Zhengqi Yan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 134, pp. 108678-108678
Closed Access | Times Cited: 27
Extended attention signal transformer with adaptive class imbalance loss for Long-tailed intelligent fault diagnosis of rotating machinery
Shuyuan Chang, Liyong Wang, Mingkuan Shi, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102436-102436
Closed Access | Times Cited: 18
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
A Novel Transformer-based Few-Shot Learning Method for Intelligent Fault Diagnosis with Noisy Labels under Varying Working Conditions
Haoyu Wang, Chuanjiang Li, Peng Ding, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110400-110400
Closed Access | Times Cited: 14
A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism
Jiang Wang, Junyu Guo, Lin Wang, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 8, pp. 085106-085106
Closed Access | Times Cited: 29
Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery
Mingkuan Shi, Chuancang Ding, Rui Wang, et al.
Reliability Engineering & System Safety (2023) Vol. 240, pp. 109601-109601
Closed Access | Times Cited: 29
A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities
Rismaya Kumar Mishra, Anurag Choudhary, Shahab Fatima, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107973-107973
Closed Access | Times Cited: 11
Federated transfer learning for machinery fault diagnosis: A comprehensive review of technique and application
Quan Qian, Bin Zhang, Chuan Li, et al.
Mechanical Systems and Signal Processing (2024) Vol. 223, pp. 111837-111837
Closed Access | Times Cited: 10
A Novel Multidomain Contrastive-Coding-Based Open-Set Domain Generalization Framework for Machinery Fault Diagnosis
Biliang Lu, Yingjie Zhang, Qingshuai Sun, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 4, pp. 6369-6381
Closed Access | Times Cited: 8
Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning
Xuemin Cheng, Shuihai Dou, Yanping Du, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Knowledge Distillation-Guided Cost-Sensitive Ensemble Learning Framework for Imbalanced Fault Diagnosis
Shuaiqing Deng, Zihao Lei, Guangrui Wen, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 23110-23122
Closed Access | Times Cited: 7
Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging
Xin Li, Yong Li, Ke Yan, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108921-108921
Closed Access | Times Cited: 36
Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model
Xingyuan Miao, Hong Zhao, Boxuan Gao, et al.
Reliability Engineering & System Safety (2023) Vol. 238, pp. 109486-109486
Closed Access | Times Cited: 20
Source-free domain adaptation framework for fault diagnosis of rotation machinery under data privacy
Qikang Li, Baoping Tang, Lei Deng, et al.
Reliability Engineering & System Safety (2023) Vol. 238, pp. 109468-109468
Closed Access | Times Cited: 19
Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis
Dawei Gao, Kai Huang, Yongsheng Zhu, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109746-109746
Closed Access | Times Cited: 19
A novel sample selection approach based universal unsupervised domain adaptation for fault diagnosis of rotating machinery
Biliang Lu, Yingjie Zhang, Zhaohua Liu, et al.
Reliability Engineering & System Safety (2023) Vol. 240, pp. 109618-109618
Closed Access | Times Cited: 18
Machinery cross domain degradation prognostics considering compound domain shifts
Peng Ding, Xiaoli Zhao, Haidong Shao, et al.
Reliability Engineering & System Safety (2023) Vol. 239, pp. 109490-109490
Closed Access | Times Cited: 16
Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information
Aobo Yu, Bolin Cai, Qiujie Wu, et al.
Reliability Engineering & System Safety (2024) Vol. 248, pp. 110181-110181
Closed Access | Times Cited: 7
Imbalanced deep transfer network for fault diagnosis of high-speed train traction motor bearings
Yilong Liu, Xinyuan Li, Xingwu Zhang, et al.
Knowledge-Based Systems (2024) Vol. 293, pp. 111682-111682
Closed Access | Times Cited: 6
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