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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:

An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
Zidong Yu, Changhe Zhang, Chao Deng
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110534-110534
Closed Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

A pruned-optimized weighted graph convolutional network for axial flow pump fault diagnosis with hydrophone signals
Xin Zhang, Li Jiang, Lei Wang, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102365-102365
Closed Access | Times Cited: 18
Research on fault diagnosis method of vehicle cable terminal based on time series segmentation for graph neural network model
Kai Liu, Guangbo Nie, Shibo Jiao, et al.
Measurement (2024) Vol. 237, pp. 114999-114999
Closed Access | Times Cited: 18
Multi-perception Graph Convolutional Tree-embedded Network for Aero-engine Bearing Health Monitoring with Unbalanced Data
Dezun Zhao, Wenbin Cai, Lingli Cui
Reliability Engineering & System Safety (2025) Vol. 257, pp. 110888-110888
Closed Access | Times Cited: 2
AGFCN:A bearing fault diagnosis method for high-speed train bogie under complex working conditions
Deqiang He, Jinxin Wu, Zhenzhen Jin, et al.
Reliability Engineering & System Safety (2025), pp. 110907-110907
Closed Access | Times Cited: 1
Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments
Yan Zhang, Changqing Shen, Juanjuan Shi, et al.
Knowledge-Based Systems (2024) Vol. 293, pp. 111679-111679
Closed Access | Times Cited: 10
A novel method for fault diagnosis of fluid end of drilling pump under complex working conditions
Gang Li, Jiayao Hu, Yaping Ding, et al.
Reliability Engineering & System Safety (2024) Vol. 248, pp. 110145-110145
Closed Access | Times Cited: 10
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
Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network
Xiaoyang Zheng, Peixi Yang, Kai Yan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108087-108087
Closed Access | Times Cited: 7
Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery
Lei Wang, Fuchen Xie, Xin Zhang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102711-102711
Closed Access | Times Cited: 6
Bearing fault diagnosis based on POA-VMD with GADF-Swin Transformer transfer learning network
Xin Dai, Yi Kang, Fuling Wang, et al.
Measurement (2024) Vol. 238, pp. 115328-115328
Closed Access | Times Cited: 5
An adaptive fault diagnosis method for rotating machinery based on GCN deep feature extraction and OptGBM
Linjun Wang, Zhenxiong Wu, Haihua Wu, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2025) Vol. 47, Iss. 2
Closed Access
A novel sliding mixing graph contrastive domain adaptation method for fault diagnosis under time-varying speeds
Kai Chen, Zong Meng, Dengyun Sun, et al.
Expert Systems with Applications (2025), pp. 126576-126576
Closed 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
Collaborative monitoring method for cutter anomaly detection and RUL prediction based on multi-task learning
X. Y. Shao, Xiaoyin Nie, Hui Shi, et al.
Journal of Mechanical Science and Technology (2025)
Closed Access
A Survey on Fault Diagnosis of Rotating Machinery Based on Machine Learning
Qi Wang, Rui Huang, Jianbin Xiong, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 102001-102001
Closed Access | Times Cited: 4
Temporal-constrained parallel graph neural networks for recognizing motion patterns and gait phases in class-imbalanced scenarios
Changhe Zhang, Zidong Yu, Xiaoyun Wang, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 143, pp. 110106-110106
Closed Access
Cross-sensor contrastive learning-based pre-training for machinery fault diagnosis under sample-limited conditions
Hao Hu, Yue Ma, Ruoxue Li, et al.
Knowledge-Based Systems (2025), pp. 113075-113075
Closed Access
A GraphKAN-Based Intelligent Fault Diagnosis Method of Rolling Bearing Under Variable Working Conditions
Ye Liu, Yanhe Xu, Jie Liu, et al.
Symmetry (2025) Vol. 17, Iss. 2, pp. 241-241
Open Access
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
Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition
Changhe Zhang, Zidong Yu, Xiaoyun Wang, et al.
Biomedical Signal Processing and Control (2024) Vol. 96, pp. 106551-106551
Closed Access | Times Cited: 3
Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine
Xin Li, Shuhua Li, Dong Wei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109882-109882
Closed Access | Times Cited: 10
SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer
Shilong Sun, Hao Ding, Zida Zhao, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102579-102579
Closed Access | Times Cited: 3
A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions
Ling Xiang, Hankun Bing, Xianze Li, et al.
Expert Systems with Applications (2024) Vol. 262, pp. 125686-125686
Closed Access | Times Cited: 3
Mode-Decoupling Auto-Encoder for Machinery Fault Diagnosis Under Unknown Working Conditions
Zenghui An, Xingxing Jiang, Jie Liu
IEEE Transactions on Industrial Informatics (2023) Vol. 20, Iss. 3, pp. 4990-5003
Closed Access | Times Cited: 7
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