Abstract Wind turbine blades are prone to various types of damage under long-term fatigue loading, making accurate damage type identification critical for structural health monitoring and operation and maintenance decision-making. This study proposes a hybrid algorithm framework that integrates SOM, Principal Component Analysis (PCA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Random Forest (RF) to identify damage in wind turbine blades based on acoustic emission (AE) signals collected during fatigue testing. Firstly, nonlinear and linear dimensionality reduction is performed on the raw AE features using SOM and PCA, respectively, resulting in six representative feature parameters. Then, DBSCAN is employed to cluster and label the reduced-dimension samples, enabling unsupervised signal classification without requiring prior knowledge. Based on the clustering results, a Random Forest model is trained and evaluated in a supervised manner, with classification accuracy, F1-score, and generalization performance quantitatively assessed. Experimental results show that the proposed method achieves over 90% accuracy in a four-class classification task, significantly outperforming traditional methods in both precision and stability. The clustering process exhibits strong robustness and is suitable for monitoring damage evolution at various stages of fatigue for the blade. This study provides an efficient and scalable signal processing approach for damage identification in composite wind turbine blades, laying a methodological foundation for intelligent and automated AE-based monitoring systems.
Shouxiang et al. (Thu,) studied this question.
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