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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:

Fault detection in wind turbine generators using a meta-learning-based convolutional neural network
Likui Qiao, Yuxian Zhang, Qisen Wang
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110528-110528
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis
Yu Zhang, Dongying Han, Peiming Shi
Expert Systems with Applications (2024) Vol. 255, pp. 124660-124660
Closed Access | Times Cited: 6
A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis
Zhiqian Zhao, Yeyin Xu, Jiabin Zhang, et al.
Neural Networks (2024) Vol. 178, pp. 106482-106482
Closed Access | Times Cited: 5
Non-Temporal Neural Networks for Predicting Degradation Trends of Key Wind-Turbine Gearbox Components
Xiaoxia Yu, Zhigang Zhang, Baoping Tang, et al.
Renewable Energy (2025), pp. 122438-122438
Closed Access
Task-adaptive unbiased regularization meta-learning for few-shot cross-domain fault diagnosis
Huaqing Wang, Dongrui Lv, Tianjiao Lin, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110200-110200
Closed Access
Quantum machine learning based wind turbine condition monitoring: State of the art and future prospects
Zhefeng Zhang, Yueqi Wu, Xiandong Ma
Energy Conversion and Management (2025) Vol. 332, pp. 119694-119694
Open Access
Domain-specific adaptation network for inverter fault diagnosis: Knowledge transfer from simulation to physical domain
Qun Guo, Gang Li, Jun Lin
Measurement (2024) Vol. 227, pp. 114299-114299
Closed Access | Times Cited: 4
On-line monitoring of egg freshness using a portable NIR spectrometer combined with deep learning algorithm
Kunshan Yao, Jun Sun, Bing Zhang, et al.
Infrared Physics & Technology (2024) Vol. 138, pp. 105207-105207
Closed Access | Times Cited: 4
A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*
Xiaoxia Yu, Zhigang Zhang, Baoping Tang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086137-086137
Closed Access | Times Cited: 3
Anomaly detection of wind turbines based on stationarity analysis of SCADA data
Phong B. Dao, Tomasz Barszcz, Wiesław J. Staszewski
Renewable Energy (2024) Vol. 232, pp. 121076-121076
Open Access | Times Cited: 3
Novel Meta-Learning for Few-Shot Bearing Fault Diagnosis Under Varying Working Conditions
Chuanhao Wang, Jigang Peng, Yongjian Sun
Engineering Research Express (2024) Vol. 6, Iss. 3, pp. 035239-035239
Closed Access | Times Cited: 2
Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
Yu Zhang, Runcai Huang, Zhiwei Li
Applied Sciences (2023) Vol. 13, Iss. 16, pp. 9276-9276
Open Access | Times Cited: 5
Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems
Jia Tian, Xingqin Zhang, Shuangqing Zheng, et al.
Mathematics (2024) Vol. 12, Iss. 9, pp. 1301-1301
Open Access | Times Cited: 1
An Unsupervised Fault Warning Method Based on Hybrid Information Gain and a Convolutional Autoencoder for Steam Turbines
Jinxing Zhai, Jing Ye, Yue Cao
Energies (2024) Vol. 17, Iss. 16, pp. 4098-4098
Open Access | Times Cited: 1
Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network
Fei Song, Yaozhen Han, A. Heath, et al.
Engineering Failure Analysis (2024), pp. 108896-108896
Closed Access | Times Cited: 1
Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow
Yunyi Zhu, Bin Xie, Anqi Wang, et al.
Reliability Engineering & System Safety (2024) Vol. 253, pp. 110554-110554
Closed Access | Times Cited: 1
AI-Based Fault Detection and Predictive Maintenance in Wind Power Conversion Systems
D.B. Hulwan, S. Chitra, Arun Chokkalingan, et al.
E3S Web of Conferences (2024) Vol. 591, pp. 02003-02003
Open Access | Times Cited: 1
A Fault Warning Approach Using an Enhanced Sand Cat Swarm Optimization Algorithm and a Generalized Neural Network
Youchun Pi, Yun Tan, Amir-Mohammad Golmohammadi, et al.
Processes (2023) Vol. 11, Iss. 9, pp. 2543-2543
Open Access | Times Cited: 3
A novel GPU-based approach for embedded NARMAX/FROLS system identification
Marlon Marques Soudré, Helon Vicente Hultmann Ayala, Alba Cristina Magalhães Alves de Melo, et al.
Mechanical Systems and Signal Processing (2024) Vol. 211, pp. 111261-111261
Closed Access
A method based on DWRNet and MGAU for RUL prediction of bearing with few samples
Xiaoxia Yu, Zhigang Zhang, Baoping Tang, et al.
Journal of Vibration and Control (2024)
Closed Access
Wind Turbine Blade Fault Diagnosis: Approximate Entropy as a Tool to Detect Erosion and Mass Imbalance
Salim Lahmiri
Fractal and Fractional (2024) Vol. 8, Iss. 8, pp. 484-484
Open Access
Predicting the degree of rubber rupture damage using a GAN-enhanced Bayesian-optimized 1DCNN network
Yi Zeng, Chubing Deng, Feng Xiong, et al.
Structural Health Monitoring (2024)
Closed Access
Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach
Paweł Knes, Phong B. Dao
Energies (2024) Vol. 17, Iss. 20, pp. 5055-5055
Open Access
DSTF-Net: A Novel Framework for Intelligent Diagnosis of Insulated Bearings in Wind Turbines with Multi-Source Data and Its Interpretability
Tongguang Yang, Ming Xu, Chun‐Lung Chen, et al.
Renewable Energy (2024), pp. 121965-121965
Closed Access
Meta-adaptive graph convolutional networks with few samples for the fault diagnosis of rotating machinery
Xiaoxia Yu, Zhigang Zhang, Baoping Tang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 12, pp. 19237-19252
Closed Access
Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method
Tianyi Deng, Chengqi Xue, Gengpei Zhang
PLoS ONE (2024) Vol. 19, Iss. 7, pp. e0305038-e0305038
Open Access
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