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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:

Active vibration-based structural health monitoring system for wind turbine blade: Demonstration on an operating Vestas V27 wind turbine
Dmitri Tcherniak, Lasse L Mølgaard
Structural Health Monitoring (2017) Vol. 16, Iss. 5, pp. 536-550
Closed Access | Times Cited: 94

Showing 1-25 of 94 citing articles:

Materials for Wind Turbine Blades: An Overview
Leon Mishnaevsky, Kim Branner, Helga Nørgaard Petersen, et al.
Materials (2017) Vol. 10, Iss. 11, pp. 1285-1285
Open Access | Times Cited: 577
A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis
Hamed Badihi, Youmin Zhang, Bin Jiang, et al.
Proceedings of the IEEE (2022) Vol. 110, Iss. 6, pp. 754-806
Open Access | Times Cited: 134
Recent advances in damage detection of wind turbine blades: A state-of-the-art review
Panida Kaewniam, Maosen Cao, Nizar Faisal Alkayem, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 167, pp. 112723-112723
Closed Access | Times Cited: 72
Structural Health Monitoring using deep learning with optimal finite element model generated data
Panagiotis Seventekidis, Dimitrios Giagopoulos, Αλέξανδρος Αραϊλόπουλος, et al.
Mechanical Systems and Signal Processing (2020) Vol. 145, pp. 106972-106972
Closed Access | Times Cited: 128
Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines
Luis David Avendaño-Valencia, Eleni Chatzi, Dmitri Tcherniak
Mechanical Systems and Signal Processing (2020) Vol. 142, pp. 106686-106686
Open Access | Times Cited: 88
An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade
Artur Movsessian, David García Cava, Dmitri Tcherniak
Mechanical Systems and Signal Processing (2021) Vol. 159, pp. 107766-107766
Open Access | Times Cited: 71
A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure
Panagiotis Seventekidis, Dimitrios Giagopoulos
Mechanical Systems and Signal Processing (2021) Vol. 157, pp. 107735-107735
Closed Access | Times Cited: 65
Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades
Wenjie Wang, Yu Xue, Chengkuan He, et al.
Energies (2022) Vol. 15, Iss. 15, pp. 5672-5672
Open Access | Times Cited: 65
Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy
Shilin Sun, Tianyang Wang, Hongxing Yang, et al.
Applied Energy (2022) Vol. 313, pp. 118882-118882
Closed Access | Times Cited: 51
In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures
Shilin Sun, Tianyang Wang, Fulei Chu
Renewable and Sustainable Energy Reviews (2022) Vol. 160, pp. 112326-112326
Closed Access | Times Cited: 50
Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements
Mingming Song, Babak Moaveni, Hamed Ebrahimian, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110425-110425
Open Access | Times Cited: 23
Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation
Xinyu Hang, Xiaoxun Zhu, Xiaoxia Gao, et al.
Renewable Energy (2024) Vol. 224, pp. 120152-120152
Closed Access | Times Cited: 14
Condition monitoring framework for damage identification in CFRP rotating shafts using Model-Driven Machine learning techniques
George Karyofyllas, Dimitrios Giagopoulos
Engineering Failure Analysis (2024) Vol. 158, pp. 108052-108052
Closed Access | Times Cited: 10
An experimental study on the data-driven structural health monitoring of large wind turbine blades using a single accelerometer and actuator
David García Cava, Dmitri Tcherniak
Mechanical Systems and Signal Processing (2019) Vol. 127, pp. 102-119
Open Access | Times Cited: 61
An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron
A. Joshuva, R. Sathish Kumar, S. Sivakumar, et al.
Alexandria Engineering Journal (2020) Vol. 59, Iss. 5, pp. 3863-3879
Open Access | Times Cited: 57
A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning
Ahmed Ali Farhan Ogaili, Alaa Abdulhady Jaber, Mohsin Noori Hamzah
Curved and Layered Structures (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 19
One year monitoring of an offshore wind turbine: Variability of modal parameters to ambient and operational conditions
Mingming Song, Nasim Partovi Mehr, Babak Moaveni, et al.
Engineering Structures (2023) Vol. 297, pp. 117022-117022
Closed Access | Times Cited: 19
Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder
Kang Yang, Sungwon Kim, Joel B. Harley
Mechanical Systems and Signal Processing (2023) Vol. 199, pp. 110473-110473
Open Access | Times Cited: 16
A case study on risk-based maintenance of wind turbine blades with structural health monitoring
Jannie Sønderkær Nielsen, Dmitri Tcherniak, Martin Dalgaard Ulriksen
Structure and Infrastructure Engineering (2020) Vol. 17, Iss. 3, pp. 302-318
Closed Access | Times Cited: 39
Research on vibration fatigue behavior of blade structures based on infrared thermography
Hui Cai, Jiawei Chen, Guangjie Kou, et al.
Infrared Physics & Technology (2024) Vol. 139, pp. 105277-105277
Closed Access | Times Cited: 4
Early detection of impact fatigue damage in an adhesively-bonded connection using acoustic emission
S. Khoshmanesh, Simon Watson, Dimitrios Zarouchas
Engineering Structures (2024) Vol. 308, pp. 117973-117973
Open Access | Times Cited: 4
Análisis modal experimental en un prototipo de la semiala de un avión usando equipo de bajo costo
Oswaldo Emiliano Rufino-Arteaga, Oscar A. Garcia-Perez
PÄDI Boletín Científico de Ciencias Básicas e Ingenierías del ICBI (2025) Vol. 12, Iss. 24, pp. 28-39
Open Access
Terrestrial laser scanning for wind turbine blade defect detection
Paulina Stałowska, Czesław Suchocki, Adam Zagubień
Measurement (2025), pp. 116706-116706
Closed Access
Machine Learning-Based Damage Diagnosis in Floating Wind Turbines Using Vibration Signals: A Lab-Scale Study Under Different Wind Speeds and Directions
John S. Korolis, Dimitrios M. Bourdalos, John S. Sakellariou
Sensors (2025) Vol. 25, Iss. 4, pp. 1170-1170
Open Access
Optimum feature selection for the supervised damage classification of an operating wind turbine blade
Mohadeseh Ashkarkalaei, Ramin Ghiasi, Vikram Pakrashi, et al.
Structural Health Monitoring (2025)
Closed Access
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