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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:

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

Showing 1-25 of 49 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
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
Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network
Zhuyun Chen, Jingyan Xia, Jipu Li, et al.
Advanced Engineering Informatics (2023) Vol. 57, pp. 102033-102033
Closed Access | Times Cited: 50
Few-shot fault diagnosis of turnout switch machine based on semi-supervised weighted prototypical network
Zhenpeng Lao, Deqiang He, Zhenzhen Jin, et al.
Knowledge-Based Systems (2023) Vol. 274, pp. 110634-110634
Closed Access | Times Cited: 48
Semisupervised Subdomain Adaptation Graph Convolutional Network for Fault Transfer Diagnosis of Rotating Machinery Under Time-Varying Speeds
Pengfei Liang, Leitao Xu, Hanqin Shuai, et al.
IEEE/ASME Transactions on Mechatronics (2023) Vol. 29, Iss. 1, pp. 730-741
Closed Access | Times Cited: 47
Application of deep learning to fault diagnosis of rotating machineries
Hao Su, Ling Xiang, Aijun Hu
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 042003-042003
Open Access | Times Cited: 15
Novel imbalanced fault diagnosis method based on generative adversarial networks with balancing serial CNN and Transformer (BCTGAN)
Hualin Chen, Jianan Wei, Haisong Huang, et al.
Expert Systems with Applications (2024) Vol. 258, pp. 125171-125171
Closed Access | Times Cited: 10
Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review
Diwang Ruan, Xuran Chen, Clemens Gühmann, et al.
Lubricants (2023) Vol. 11, Iss. 2, pp. 74-74
Open Access | Times Cited: 21
Latest innovations in the field of condition-based maintenance of rotatory machinery: a review
Anil Kumar, Chander Parkash, Hesheng Tang, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 2, pp. 022003-022003
Closed Access | Times Cited: 20
Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis
Xin Wang, Hongkai Jiang, Yunpeng Liu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 5, pp. 055102-055102
Closed Access | Times Cited: 19
Electricity theft detection based on hybrid random forest and weighted support vector data description
Qingyuan Cai, Peng Li, Ruchuan Wang
International Journal of Electrical Power & Energy Systems (2023) Vol. 153, pp. 109283-109283
Open Access | Times Cited: 18
A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data
Xin Wang, Hongkai Jiang, Yunpeng Liu, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106872-106872
Closed Access | Times Cited: 16
A review: the application of generative adversarial network for mechanical fault diagnosis
Weiqing Liao, Ke Yang, Wenlong Fu, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 6, pp. 062002-062002
Closed Access | Times Cited: 7
An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment
Zisheng Wang, Jianping Xuan, Tielin Shi
Mechanical Systems and Signal Processing (2024) Vol. 219, pp. 111596-111596
Closed Access | Times Cited: 6
Digital twin-assisted AI framework based on domain adaptation for bearing defect diagnosis in the centrifugal pump
Anil Kumar, Rajesh Kumar, Jiawei Xiang, et al.
Measurement (2024) Vol. 235, pp. 115013-115013
Closed Access | Times Cited: 5
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
Ali Saeed, Muazzam A. Khan, Usman Akram, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A Novel Small Samples Fault Diagnosis Method Based on the Self-attention Wasserstein Generative Adversarial Network
Zhiwu Shang, Jie Zhang, Wanxiang Li, et al.
Neural Processing Letters (2023) Vol. 55, Iss. 5, pp. 6377-6407
Closed Access | Times Cited: 14
A novel generation network using feature fusion and guided adversarial learning for fault diagnosis of rotating machinery
Zong Meng, Huihui He, Wei Cao, et al.
Expert Systems with Applications (2023) Vol. 234, pp. 121058-121058
Closed Access | Times Cited: 14
Power transformer fault diagnosis based on a self-strengthening offline pre-training model
Mingwei Zhong, Siqi Yi, Jingmin Fan, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107142-107142
Closed Access | Times Cited: 13
CE-FFGAN: A feature fusion generative adversarial network with deep embedded category information for limited sample fault diagnosis of rotating machinery under speed variation
Chen Yang, Hongkun Li, Shunxin Cao, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102605-102605
Closed Access | Times Cited: 4
Domain reinforcement feature adaptation methodology with correlation alignment for compound fault diagnosis of rolling bearing
Zisheng Wang, Jianping Xuan, Tielin Shi
Expert Systems with Applications (2024), pp. 125594-125594
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
Enhanced feedback analysis of vertical load reliability parameters for airplane landing gear using an improved generative adversarial network and explainable artificial intelligence techniques
Wei-Huang Pan, Yunwen Feng, Cheng Lu, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110175-110175
Closed Access
A federated cross-machine diagnostic framework for machine-level motors with extreme label shortage
Yiming He, Weiming Shen
Advanced Engineering Informatics (2024) Vol. 61, pp. 102511-102511
Closed Access | Times Cited: 3
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