Review of Wind Turbine Blade Failure Detection Based on Acoustic Emission

The global wind energy industry has long faced challenges in monitoring and maintaining the health status of wind turbine blades. Accurately detecting the damage modes of wind turbine blades is crucial for effectively planning blade maintenance, preventing further damage, and extending their service life. This paper delves into the acoustic signal-based techniques for monitoring the structural integrity and damage localization of wind turbine blades, and elucidates methods for automatic detection and classification of blade faults using machine learning algorithms.