Fault Diagnosis of Wind Turbine Blades Based on One-Dimensional Convolutional Neural Network-Bidirectional Long Short-Term Memory-Adaptive Boosting and Multi-Source Data Fusion

To prevent wind turbine blade accidents and improve fault detection accuracy, a hybrid deep learning model based on 1D CNN-BiLSTM-AdaBoost for wind turbine-blade fault classification is proposed. Fault data are first preprocessed by segmenting and labeling the fault patterns. Features are extracted through the convolutional layers, followed by dimensionality reduction and denoising using the pooling layers, and feature fusion. The multi-source sensor features are then fed into the BiLSTM layer for further processing of the time-series characteristics. The processed data are classified through a fully connected layer. Finally, multiple weak classifiers are combined to generate the final classification result. Experimental results show that the 1D CNN-BiLSTM-AdaBoost model outperforms models that use only 1D CNN, BiLSTM, and 1D CNN-BiLSTM, achieving an accuracy of 96.88%, precision of 97.22%, recall of 96.92%, and an F1 score of 96.86%, with a maximum accuracy of 100%. These results validate the model’s effectiveness for fault classification.