Low-speed impact localization of wind turbine blades with a single sensor utilizing multiscale feature fusion convolutional neural networks

Impact, which may occur during manufacturing, serving and maintaining, is a significant threat to in-service composite structures, e.g. wind turbine blades. It calls for developing a method for assessment and localization of impact. In this paper, a single-sensor impact localization method based on deep learning is proposed. Specifically, a multiscale feature fusion convolutional neural network is designed, which, in combination with a convolutional block attention module, adaptively extracts features from single-sensor signals to achieve accurate region-level source localization. Complete ensemble empirical mode decomposition with adaptive noise is employed to reduce noise and extract intrinsic mode functions from acoustic emission signals, enabling more effective feature extraction. The decomposed signals are then converted into grayscale images, forming a dataset for the deep learning model. This approach allows for the extraction of rich feature information. A steel ball drop experiment is conducted to simulate the low-speed impact response of the wind turbine blade spar. The experimental results show significant advantages in localization accuracy. This study offers a promising solution for acoustic emission source region localization in complex composite structures.