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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:

Semi-supervised multi-scale attention-aware graph convolution network for intelligent fault diagnosis of machine under extremely-limited labeled samples
Zongliang Xie, Jinglong Chen, Yong Feng, et al.
Journal of Manufacturing Systems (2022) Vol. 64, pp. 561-577
Closed Access | Times Cited: 62

Showing 1-25 of 62 citing articles:

Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data
Hongfeng Tao, Haojin Shi, Jier Qiu, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 2, pp. 025036-025036
Closed Access | Times Cited: 60
Federated transfer learning in fault diagnosis under data privacy with target self-adaptation
Xu Li, Chi Zhang, Xiang Li, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 523-535
Closed Access | Times Cited: 48
Graph features dynamic fusion learning driven by multi-head attention for large rotating machinery fault diagnosis with multi-sensor data
Xin Zhang, Xi Zhang, Jie Liu, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 125, pp. 106601-106601
Closed Access | Times Cited: 44
Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings
Jinxin Wu, Deqiang He, Jiayi Li, et al.
Reliability Engineering & System Safety (2024) Vol. 247, pp. 110143-110143
Closed Access | Times Cited: 29
Single-domain incremental generation network for machinery intelligent fault diagnosis under unknown working speeds
Yuanyue Pu, Jian Tang, Xue‐Gang Li, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102400-102400
Closed Access | Times Cited: 15
Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
José Miguel Ramírez‐Sanz, Jose-Alberto Maestro-Prieto, Álvar Arnaiz‐González, et al.
ISA Transactions (2023) Vol. 143, pp. 255-270
Open Access | Times Cited: 36
Trusted multi-source information fusion for fault diagnosis of electromechanical system with modified graph convolution network
Kongliang Zhang, Hongkun Li, Shunxin Cao, et al.
Advanced Engineering Informatics (2023) Vol. 57, pp. 102088-102088
Closed Access | Times Cited: 28
MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios
Maoyou Ye, Xiaoan Yan, Dong Jiang, et al.
Knowledge-Based Systems (2023) Vol. 284, pp. 111294-111294
Closed Access | Times Cited: 28
A federated learning approach to mixed fault diagnosis in rotating machinery
Manan Mehta, Siyuan Chen, Haichuan Tang, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 687-694
Closed Access | Times Cited: 27
Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis
Xin Zhang, Jie Liu, Xi Zhang, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 9, pp. 11002-11013
Closed Access | Times Cited: 8
A Semisupervised GCN Framework for Transfer Diagnosis Crossing Different Machines
Liuyang Song, Pengyuan Hao, Shikuan Zhang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 6, pp. 8326-8336
Closed Access | Times Cited: 7
Remaining useful life prediction of bearings with attention-awared graph convolutional network
Yupeng Wei, Dazhong Wu
Advanced Engineering Informatics (2023) Vol. 58, pp. 102143-102143
Open Access | Times Cited: 21
Interpretable data-augmented adversarial variational autoencoder with sequential attention for imbalanced fault diagnosis
Yunpeng Liu, Hongkai Jiang, Renhe Yao, et al.
Journal of Manufacturing Systems (2023) Vol. 71, pp. 342-359
Closed Access | Times Cited: 21
Cross-domain bearing fault diagnosis using dual-path convolutional neural networks and multi-parallel graph convolutional networks
Zhang Yong, Songzhao Zhang, Yuhao Zhu, et al.
ISA Transactions (2024) Vol. 152, pp. 129-142
Closed Access | Times Cited: 6
Autoregressive data generation method based on wavelet packet transform and cascaded stochastic quantization for bearing fault diagnosis under unbalanced samples
Yawei Sun, Hongfeng Tao, Vladimir Stojanović
Engineering Applications of Artificial Intelligence (2024) Vol. 138, pp. 109402-109402
Closed Access | Times Cited: 6
Health Evaluation Techniques Towards Rotating Machinery: A Systematic Literature Review and Implementation Guideline
Weixiong Jiang, Jun Wu, Yifan Yang, et al.
Reliability Engineering & System Safety (2025) Vol. 260, pp. 110924-110924
Closed Access
An Auto Hierarchical Clustering Algorithm to Distinguish Geometries Suitable for Additive and Traditional Manufacturing Technologies: Comparing Humans and Unsupervised Learning
Baris Ördek, Éric Coatanéa, Yuri Borgianni
Results in Engineering (2025), pp. 104418-104418
Open Access
A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges
Jingxin ZHAO, Wenming Wang, Ji Huang, et al.
AIP Advances (2025) Vol. 15, Iss. 2
Open Access
DCAGGCN: A novel method for remaining useful life prediction of bearings
Deqiang He, Jiayang Zhao, Zhenzhen Jin, et al.
Reliability Engineering & System Safety (2025), pp. 110978-110978
Closed Access
A Novel Spiking Graph Attention Network for Intelligent Fault Diagnosis of Planetary Gearboxes
Shunxin Cao, Hongkun Li, Kongliang Zhang, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 12, pp. 13140-13154
Closed Access | Times Cited: 13
Triplet adversarial Learning-driven graph architecture search network augmented with Probsparse-attention mechanism for fault diagnosis under Few-shot & Domain-shift
Yuanhong Chang, Jinglong Chen, Weiguang Zheng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 199, pp. 110462-110462
Closed Access | Times Cited: 13
Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections
Yupeng Wei, Dazhong Wu, Janis Terpenny
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109776-109776
Open Access | Times Cited: 13
Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults
P. Maestro, José Miguel Ramírez‐Sanz, Andrés Bustillo, et al.
Applied Intelligence (2024) Vol. 54, Iss. 6, pp. 4525-4544
Open Access | Times Cited: 4
Semi-supervised few-shot fault diagnosis driven by multi-head dynamic graph attention network under speed fluctuations
Li Jiang, Shuaiyu Wang, Tianao Zhang, et al.
Digital Signal Processing (2024) Vol. 151, pp. 104528-104528
Closed Access | Times Cited: 4
Self-supervised graph feature enhancement and scale attention for mechanical signal node-level representation and diagnosis
Xin Zhang, Jie Liu, Xi Zhang, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103197-103197
Closed Access
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