15 citations as recorded by crossref.

1. Imbalance-induced nonlinear vibrations in wind turbine towers L. da Mata Rocha Menezes et al. https://doi.org/10.1016/j.ijnonlinmec.2026.105371
2. Diagnosis Algorithm for Wind Turbine Blade Imbalance based on Nacelle Acceleration D. Jeong & I. Paek https://doi.org/10.1088/1742-6596/3224/6/062027
3. A review of failure prognostics for predictive maintenance of offshore wind turbines W. Zhang et al. https://doi.org/10.1088/1742-6596/2362/1/012043
4. Automatic detection and correction of aerodynamic imbalances: a noise-based approach C. Sucameli et al. https://doi.org/10.1088/1742-6596/2767/2/022022
5. New method to characterize aerodynamic flow state around wind turbine blades D. Voisin et al. https://doi.org/10.5194/wes-11-1383-2026
6. Quantification of performance degradation due to wind turbine aging: Estimating the reduction in annual energy production using the annual degradation rate D. Kim & B. Kim https://doi.org/10.1016/j.energy.2025.136142
7. Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring F. Mehlan & A. Nejad https://doi.org/10.1016/j.renene.2023.04.102
8. Prognostics of Thermal Anomalies in Wind Turbines via Deep Learning and Conformal Prediction Using SCADA Data A. Benabdesselam et al. https://doi.org/10.1109/ACCESS.2026.3694778
9. On the Analysis and Synthesis of Wind Turbine Side–Side Tower Load Control via Demodulation A. Pamososuryo et al. https://doi.org/10.1109/TCST.2024.3377508
10. A review of prognostics and health management techniques in wind energy J. Cuesta et al. https://doi.org/10.1016/j.ress.2025.111004
11. Demonstration of an Integrated Framework to Support Major Wind Energy Investments A. Kerr & R. Carriveau https://doi.org/10.1088/1742-6596/3185/1/012006
12. Blade-tower clearance-based aerodynamic imbalance analysis and fault diagnosis of wind turbines J. Pan et al. https://doi.org/10.1177/13694332251360070
13. Development and experimental evaluation of a mixing device for the dry production of sodium percarbonate F. Abdullayev et al. https://doi.org/10.1016/j.nxcen.2026.100029
14. A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines S. Milani et al. https://doi.org/10.5194/wes-10-497-2025
15. Wind farm analysis using SailoR diagram-based diagnostics to quantify yaw misalignment correction A. Ulazia et al. https://doi.org/10.1016/j.ecmx.2025.100890