A hybrid deep learning method for AE source localization for heterostructure of wind turbine blades

Abstract

An acoustic emission (AE) and hybrid deep learning networks based damage source localization method for heterostructure of wind turbine blades is proposed in this paper. Firstly, comprehensive data preprocessing is performed, including AE signal denoising, feature extraction, feature selection and normalization. New training features including AE descriptors, features of time, frequency domains and spectral features are extracted. A feature selection method based on Light-GBM and correlation analysis is employed to identify relevant features for AE source localization. Subsequently, two deep learning networks, AM-BiLNN and AM-LCNN, are developed to locate the damage source in two steps. Then, numerical tests are implemented on localized structure of a wind turbine blade to verify the performance of the proposed method and the performance of the selected features and the robustness of the proposed method under noise are investigated. Furthermore, a comparative investigation between the proposed method with long short-term memory neural networks (LSTM), convolutional neural networks (CNN) and the cluster-based method is carried out to demonstrate the superiority of the proposed method. The results highlight the superiority and robustness of the proposed method. Feature selection is shown to effectively enhance coordinate localization performance.