| Abstract |
This paper provides an overview of the experimental techniques used in health monitoring for wind turbine blades. It discusses methods such as acoustic emission, ultrasonic testing, vibration-based analysis, wavelet analysis, visual inspection, and machine learning, which are employed to identify, localize, and assess the severity of damage. The paper also evaluates the detection standards, improvement strategies, as well as the advantages and limitations of these methods. It is noted that some damage detection techniques are not widely utilized. The paper concludes by summarizing the potential of these techniques for identifying damage in wind turbine blades, highlighting their usefulness for future research. Techniques based on acoustic emission, ultrasonic testing, and vibration analysis are effective for determining damage severity, and combining any two of these methods could provide more accurate results. |
| Keywords |
Health monitoring, wind turbine blade, damage identification, acoustic emission, ultrasonic testing, vibration analysis, wavelet analysis, visual inspection, machine learning |
