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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:

DeepFedWT: A federated deep learning framework for fault detection of wind turbines
Guoqian Jiang, Weipeng Fan, Wenyue Li, et al.
Measurement (2022) Vol. 199, pp. 111529-111529
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis
Qing Li
Expert Systems with Applications (2023) Vol. 229, pp. 120517-120517
Open Access | Times Cited: 30
ResDenIncepNet-CBAM with principal component analysis for wind turbine blade cracking fault prediction with only short time scale SCADA data
Quan Lu, Wanxing Ye, Linfei Yin
Measurement (2023) Vol. 212, pp. 112696-112696
Closed Access | Times Cited: 22
An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms
Lijin Wang, Weipeng Fan, Guoqian Jiang, et al.
Energy (2023) Vol. 284, pp. 128518-128518
Closed Access | Times Cited: 17
A Federated Learning Framework for Cloud–Edge Collaborative Fault Diagnosis of Wind Turbines
Guoqian Jiang, Kai Zhao, Xiufeng Liu, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 23170-23185
Closed Access | Times Cited: 6
A review of federated learning in renewable energy applications: Potential, challenges, and future directions
Albin Grataloup, Stefan Jonas, Angela Meyer
Energy and AI (2024) Vol. 17, pp. 100375-100375
Open Access | Times Cited: 6
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
Machine learning applications in health monitoring of renewable energy systems
Bo Ren, Yuan Chi, Niancheng Zhou, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 189, pp. 114039-114039
Closed Access | Times Cited: 13
Active federated transfer algorithm based on broad learning for fault diagnosis
Guokai Liu, Weiming Shen, Liang Gao, et al.
Measurement (2023) Vol. 208, pp. 112452-112452
Closed Access | Times Cited: 12
Challenges on prognostics and health management for wind turbine components
Jokin Cuesta, Urko Leturiondo, Yolanda Vidal, et al.
Journal of Physics Conference Series (2024) Vol. 2745, Iss. 1, pp. 012003-012003
Open Access | Times Cited: 4
A clustered federated learning framework for collaborative fault diagnosis of wind turbines
Rui Zhou, Yanting Li, Xinhua Lin
Applied Energy (2024) Vol. 377, pp. 124532-124532
Closed Access | Times Cited: 3
Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects
Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, et al.
Electronics (2022) Vol. 12, Iss. 1, pp. 158-158
Open Access | Times Cited: 17
FedBIP: A Federated Learning-Based Model for Wind Turbine Blade Icing Prediction
Dongtian Zhang, Weiwei Tian, Xu Cheng, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Open Access | Times Cited: 9
A robust fleet-based anomaly detection framework applied to wind turbine vibration data
Gustavo de Novaes Pires Leite, Felipe Farias, Tiago Gomes de Sá, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106859-106859
Closed Access | Times Cited: 8
Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science
Bita Ghasemkhani, Özlem Varlıklar, Yunus Doğan, et al.
Animals (2024) Vol. 14, Iss. 14, pp. 2021-2021
Open Access | Times Cited: 2
Interpretable federated learning for machine condition monitoring: Interpretable average global model as a fault feature library
Xiao Feng, Dong Wang, Bingchang Hou, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106632-106632
Closed Access | Times Cited: 7
Improved lightweight federated learning network for fault feature extraction of reciprocating machinery
Junling Zhang, Lixiang Duan, Ke Li, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 045115-045115
Closed Access | Times Cited: 2
Anomaly detection in wind turbine blades based on PCA and convolutional kernel transform models: employing multivariate SCADA time series analysis
Lingchao Meng, Jianxiong Gao, Yiping Yuan, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 085109-085109
Closed Access | Times Cited: 2
A differential privacy-preserving federated learning scheme with predictive maintenance of wind turbines based on deep learning for feature compression and anomaly detection with state assessment
Huan Chen, Hsin-Yao Hsu, Jia‐You Hsieh, et al.
Journal of Mechanical Science and Technology (2024) Vol. 38, Iss. 7, pp. 3413-3429
Closed Access | Times Cited: 2
MVGNet: Multiview Graph Network With Interactive Shared Fusion for Fault Diagnosis of Wind Turbines
Lijin Wang, Guoqian Jiang, Jing Wang, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 21, pp. 26804-26819
Closed Access | Times Cited: 5
FedAlign: Federated Model Alignment via Data-Free Knowledge Distillation for Machine Fault Diagnosis
Wenjun Sun, Ruqiang Yan, Ruibing Jin, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 73, pp. 1-12
Closed Access | Times Cited: 5
Deep learning-based fuzzy decision support system-based fault diagnosis of wind turbine generators in electrical machines
Wei Kong Pang, Kangming Xu, Qingyuan Wu, et al.
Electrical Engineering (2024)
Closed Access | Times Cited: 1
Fault detection and identification for control systems in floating offshore wind farms: A supervised Deep Learning methodology
Ana Fernández-Navamuel, Yerai Peña‐Sanchez, Vincenzo Nava
Ocean Engineering (2024) Vol. 310, pp. 118678-118678
Open Access | Times Cited: 1
Research on maintenance cycle prediction for energy equipment with limited and sensitive data
Junming Yao, Wei Liang, Zhiming Zheng, et al.
Engineering Failure Analysis (2024) Vol. 164, pp. 108696-108696
Closed Access | Times Cited: 1
An improved federated learning method based on MF1-FedAvg and MSRANet for machinery fault diagnosis
Xiuyan Liu, Chunqiu Pang, Tingting Guo, et al.
Journal of Mechanical Science and Technology (2024) Vol. 38, Iss. 9, pp. 4683-4697
Closed Access | Times Cited: 1
A Semi-Supervised Federated Learning Fault Diagnosis Method Based on Adaptive Class Prototype Points for Data Suffered by High Missing Rate
Funa Zhou, Wei Xu, Chaoge Wang, et al.
Journal of Intelligent & Robotic Systems (2023) Vol. 109, Iss. 4
Closed Access | Times Cited: 2
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