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.

声发射 (AE) 和基于混合深度学习网络的损伤源异质结构定位方法本文提出了风力涡轮机叶片的设计。首先，进行全面的数据预处理，包括AE信号去噪、特征提取、特征选择和归一化。提取新的训练特征，包括AE描述符、时域特征、频域特征和频谱特征。采用基于Light-GBM和相关分析的特征选择方法来识别AE源定位的相关特征。随后，开发了两个深度学习网络AM-BiLNN和AM-LCNN，以分两步定位损坏源。然后，对风力涡轮机叶片的局部结构进行数值试验，以验证所提方法的性能，并研究所选特征的性能以及所提方法在噪声下的鲁棒性。此外，还对所提出的方法与长短期记忆神经网络（LSTM）、卷积神经网络（CNN）和基于集群的方法进行了比较研究方法的实施验证了该方法的优越性。结果凸显了所提出方法的优越性和鲁棒性。结果表明，特征选择可以有效提高坐标定位性能。