Publications about AQUADA-GO

  • Chen, X., Shihavuddin, ASM., Madsen, S. H., Thomsen, K., Rasmussen, S., & Branner, K. (2021). AQUADA: Automated quantification of damages in composite wind turbine blades for LCOE reduction. Wind Energy, 24(6), 535-548. (https://doi.org/10.1002/we.2587).
  • Chen, X., Semenov, S., McGugan, M., Madsen, S. H., Yeniceli, S. C., Berring, P., & Branner, K. (2021). Fatigue testing of a 14.3 m composite blade embedded with artificial defects – damage growth and structural health monitoring. Composites - Part A: Applied Science and Manufacturing, 140, [106189]. (https://doi.org/10.1016/j.compositesa.2020.106189).
  • Chen, X., Sheiati, S., & Shihavuddin, ASM. (2023). AQUADA PLUS: Automated Damage Inspection of Cyclic-loaded Large-scale Composite Structures using Thermal Imagery and Computer Vision. Composite Structures, 318, [117085]. (https://doi.org/10.1016/j.compstruct.2023.117085).
  • Chen, X., Janeliukstis, R., & Sarhadi, A. (2022). Thermographic data analytics-based damage characterization in a large-scale composite structure under cyclic loading. Composite Structures, [115525]. (https://doi.org/10.1016/j.compstruct.2022.115525).
  • Sheiati, S., & Chen, X. (2023). Deep learning-based fatigue damage segmentation of wind turbine blades under complex dynamic thermal backgrounds. Structural Health Monitoring, [14759217231174377]. (https://doi.org/10.1177/14759217231174377).
  • Spencer, M., Sheiati, S., & Chen, X. (2023). AQUADA GUI: A graphical user interface for automated quantification of damages in composite structures under fatigue loading using computer vision and thermography. SoftwareX, 22, [101392]. (https://doi.org/10.1016/j.softx.2023.101392).
  • Eder, M. A., Sarhadi, A., & Chen, X. (2021). A novel and robust method to quantify fatigue damage in fibre composite materials using thermal imagine analysis. International Journal of Fatigue, 150, [106326]. (https://doi.org/10.1016/j.ijfatigue.2021.106326).
  • Jia, X., & Chen, X. (2024). AI-based optical-thermal video data fusion for near real-time blade segmentation in normal wind turbine operation. Engineering Applications of Artificial Intelligence, 127, [107325]. (https://doi.org/10.1016/j.engappai.2023.107325).
  • Jia, X., & Chen, X. (2024). Unsupervised Wind Turbine Blade Damage Detection With Memory-Aided Denoising Reconstruction. IEEE Transactions on Industrial Informatics. (https://doi.org/10.1109/TII.2024.3459612).
  • Sheiati, S., Jia, X., McGugan, M., Branner, K., & Chen, X. (2024). Artificial intelligence-based blade identification in operational wind turbines through similarity analysis aided drone inspection. Engineering Applications of Artificial Intelligence, 137, 109234. (https://doi.org/10.1016/j.engappai.2024.109234).