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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:

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

Showing 1-25 of 40 citing articles:

Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis
Xin Wang, Hongkai Jiang, Zhenghong Wu, et al.
Advanced Engineering Informatics (2023) Vol. 56, pp. 102027-102027
Closed Access | Times Cited: 98
A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis
Ruixin Wang, Hongkai Jiang, Ke Zhu, et al.
Advanced Engineering Informatics (2022) Vol. 54, pp. 101750-101750
Closed Access | Times Cited: 91
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 knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis
Zhenghong Wu, Hongkai Jiang, Hongxuan Zhu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 189, pp. 110098-110098
Closed Access | Times Cited: 50
Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions
Jipu Li, Ruyi Huang, Zhuyun Chen, et al.
Advanced Engineering Informatics (2023) Vol. 55, pp. 101883-101883
Closed Access | Times Cited: 46
Machine fault diagnosis with small sample based on variational information constrained generative adversarial network
Shaowei Liu, Hongkai Jiang, Zhenghong Wu, et al.
Advanced Engineering Informatics (2022) Vol. 54, pp. 101762-101762
Closed Access | Times Cited: 49
Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis
Zhenghong Wu, Hongkai Jiang, Shaowei Liu, et al.
Advanced Engineering Informatics (2023) Vol. 56, pp. 101993-101993
Closed Access | Times Cited: 31
Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis
Ning Jia, Weiguo Huang, Chuancang Ding, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102774-102774
Closed Access | Times Cited: 10
Quantifying risk of service failure in customer complaints: A textual analysis-based approach
Wenyan Song, Rong Wan, Yuqi Tang
Advanced Engineering Informatics (2024) Vol. 60, pp. 102377-102377
Closed Access | Times Cited: 7
A novel Brownian correlation metric prototypical network for rotating machinery fault diagnosis with few and zero shot learners
Jingli Yang, Changdong Wang, Changan Wei
Advanced Engineering Informatics (2022) Vol. 54, pp. 101815-101815
Closed Access | Times Cited: 30
One-fault-shot learning for fault severity estimation of gears that addresses differences between simulation and experimental signals and transfer function effects
Omri Matania, Lior Bachar, Varun Khemani, et al.
Advanced Engineering Informatics (2023) Vol. 56, pp. 101945-101945
Closed Access | Times Cited: 16
A gray texture image data-driven intelligent fault diagnosis method of induction motor rotor-bearing system under variable load conditions
Hongwei Fan, Zhongfu Ren, Xuhui Zhang, et al.
Measurement (2024) Vol. 233, pp. 114742-114742
Closed Access | Times Cited: 7
A dynamic collaborative adversarial domain adaptation network for unsupervised rotating machinery fault diagnosis
Xin Wang, Hongkai Jiang, Mu Mingzhe, et al.
Reliability Engineering & System Safety (2024), pp. 110662-110662
Closed Access | Times Cited: 7
A task-oriented theil index-based meta-learning network with gradient calibration strategy for rotating machinery fault diagnosis with limited samples
Mingzhe Mu, Hongkai Jiang, Xin Wang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102870-102870
Closed Access | Times Cited: 5
Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy
Xinyu Ren, Wanli Zhao, Mengmeng Liu, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110380-110380
Closed Access | Times Cited: 4
Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods
Attiq Ur Rehman, Weidong Jiao, Yonghua Jiang, et al.
Applied Soft Computing (2025), pp. 112785-112785
Closed Access
Remaining useful life estimation based on selective ensemble of deep neural networks with diversity
Tangbin Xia, Dongyang Han, Yimin Jiang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102608-102608
Closed Access | Times Cited: 3
Dual-path multi-scale attention residual network for fault diagnosis of rolling bearings under complex operating conditions
Linfeng Deng, Yuanwen Zhang, Cheng Zhao, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086106-086106
Closed Access | Times Cited: 2
A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions
Yanning Li, Yi Zhang, Ruixin Wang, et al.
Sensors (2024) Vol. 24, Iss. 11, pp. 3323-3323
Open Access | Times Cited: 2
Knowledge correlation graph-guided multi-source interaction domain adaptation network for rotating machinery fault diagnosis
Zhenghong Wu, Hongkai Jiang, Xin Wang, et al.
ISA Transactions (2023) Vol. 142, pp. 663-682
Closed Access | Times Cited: 5
A zero-sample intelligent fault diagnosis method for bearings based on category relationship model
Qibin Wang, Ni Liu, Junji Wang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 130, pp. 107739-107739
Closed Access | Times Cited: 5
A reinforcement double deep Q-network with prioritised experience replay for rolling bearing fault diagnosis
Zhenning Li, Hongkai Jiang, Yunpeng Liu
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125133-125133
Closed Access | Times Cited: 5
Cross-domain open-set rolling bearing fault diagnosis based on feature improvement adversarial network under noise condition
Haomiao Wang, Yibin Li, Mingshun Jiang, et al.
Journal of Intelligent & Fuzzy Systems (2024) Vol. 46, Iss. 2, pp. 5073-5085
Closed Access | Times Cited: 1
Multi-position industrial defect inspection using self-training siamese networks with mix strategies
Fangjun Wang, Xurong Chi, Liangwu Wei, et al.
Journal of Industrial Information Integration (2024) Vol. 40, pp. 100615-100615
Closed Access | Times Cited: 1
An Improved Fault Diagnosis Method of Rolling Bearings Based on Multi-Scale Attention CNN
Linfeng Deng, Yuanwen Zhang, Zhifeng Shi
Journal of Failure Analysis and Prevention (2024) Vol. 24, Iss. 4, pp. 1814-1827
Closed Access | Times Cited: 1
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