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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:

SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis
Chaoying Yang, Kaibo Zhou, Jie Liu
IEEE Transactions on Industrial Electronics (2021) Vol. 69, Iss. 4, pp. 4167-4176
Closed Access | Times Cited: 123

Showing 1-25 of 123 citing articles:

A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu, et al.
Neural Networks (2024) Vol. 173, pp. 106207-106207
Open Access | Times Cited: 131
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Bayu Adhi Tama, Malinda Vania, Seung‐Chul Lee, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 5, pp. 4667-4709
Open Access | Times Cited: 128
Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles
Zhongxu Hu, Shanhe Lou, Yang Xing, et al.
IEEE Transactions on Intelligent Vehicles (2022) Vol. 7, Iss. 3, pp. 417-440
Open Access | Times Cited: 106
Toward cognitive predictive maintenance: A survey of graph-based approaches
Liqiao Xia, Pai Zheng, Xinyu Li, et al.
Journal of Manufacturing Systems (2022) Vol. 64, pp. 107-120
Closed Access | Times Cited: 101
Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study
Chao Zhao, Enrico Zio, Weiming Shen
Reliability Engineering & System Safety (2024) Vol. 245, pp. 109964-109964
Closed Access | Times Cited: 88
Adversarial Mutual Information-Guided Single Domain Generalization Network for Intelligent Fault Diagnosis
Chao Zhao, Weiming Shen
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 3, pp. 2909-2918
Closed Access | Times Cited: 72
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: 55
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: 46
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
A Fusion CWSMM-Based Framework for Rotating Machinery Fault Diagnosis Under Strong Interference and Imbalanced Case
Xin Li, Jian Cheng, Haidong Shao, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 8, pp. 5180-5189
Closed Access | Times Cited: 103
Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis
Kaibo Zhou, Chaoying Yang, Jie Liu, et al.
IEEE Transactions on Industrial Electronics (2021) Vol. 69, Iss. 10, pp. 10595-10604
Closed Access | Times Cited: 74
Composite fault diagnosis for rolling bearing based on parameter-optimized VMD
Hua Li, Xing Wu, Tao Liu, et al.
Measurement (2022) Vol. 201, pp. 111637-111637
Closed Access | Times Cited: 65
Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering
Zhongxu Hu, Yang Xing, Weihao Gu, et al.
IEEE Transactions on Intelligent Vehicles (2022) Vol. 8, Iss. 1, pp. 37-47
Open Access | Times Cited: 54
Explainable Graph Wavelet Denoising Network for Intelligent Fault Diagnosis
Tianfu Li, Chuang Sun, Sinan Li, et al.
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 35, Iss. 6, pp. 8535-8548
Closed Access | Times Cited: 46
Bearing fault diagnosis method based on a multi-head graph attention network
Li Jiang, Xingjie Li, Lin Wu, et al.
Measurement Science and Technology (2022) Vol. 33, Iss. 7, pp. 075012-075012
Closed Access | Times Cited: 45
An improved multi-channel graph convolutional network and its applications for rotating machinery diagnosis
Chaoying Yang, Jie Liu, Kaibo Zhou, et al.
Measurement (2022) Vol. 190, pp. 110720-110720
Closed Access | Times Cited: 43
A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions
Mohammadreza Kavianpour, Amin Ramezani, Mohammad Taghi Hamidi Beheshti
Measurement (2022) Vol. 199, pp. 111536-111536
Closed Access | Times Cited: 41
Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion
Yuntian Ta, Yan‐Feng Li, Wenan Cai, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 109033-109033
Closed Access | Times Cited: 40
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: 30
Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects
Lei Ren, Zidi Jia, Yuanjun Laili, et al.
IEEE Transactions on Neural Networks and Learning Systems (2023), pp. 1-20
Open Access | Times Cited: 26
A multi-scale graph convolutional network with contrastive-learning enhanced self-attention pooling for intelligent fault diagnosis of gearbox
Zixu Chen, Jinchen Ji, Wennian Yu, et al.
Measurement (2024) Vol. 230, pp. 114497-114497
Closed Access | Times Cited: 10
A new generative adversarial network based imbalanced fault diagnosis method
Menglei Li, Dacheng Zou, Shuyang Luo, et al.
Measurement (2022) Vol. 194, pp. 111045-111045
Closed Access | Times Cited: 35
A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data
Xin Zhang, Haifeng Wang, Bo Wu, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 5, pp. 2449-2462
Closed Access | Times Cited: 33
Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data
Jianyu Long, Rongxin Zhang, Zhe Yang, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-11
Closed Access | Times Cited: 32
Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system
Kongliang Zhang, Hongkun Li, Shunxin Cao, et al.
Measurement (2022) Vol. 201, pp. 111697-111697
Closed Access | Times Cited: 32
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