###### Abstract  
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. wind turbine blades; fault detection; 1D CNN-BiLSTM; multi-source data fusion; AdaBoost; confusion matrix +
Footnote †: journal: Applied sciences  
13 February 202513 March 202513 March 202513 February 2  
demonstrated that integrating multi-sensor features significantly enhances bearing fault detection accuracy. Similarly, [PERSON] et al. [7] employed multi-source sensor data, including vibration and pressure signals, to achieve precise fault diagnosis in reciprocating pumps through feature fusion techniques. Deep learning, particularly in the context of multi-sensor data fusion and temporal sequence modeling, has further revolutionized fault detection. Convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and their variants, such as BiLSTM, have been extensively applied to fault mode recognition and health monitoring [8; 9]. Among these models, one-dimensional CNNs (1D-CNNs) have been widely adopted for processing time-series data due to their ability to automatically extract local features, capture short-term dependencies, and reduce preprocessing requirements [10]. However, 1D-CNNs exhibit limitations in capturing long-term dependencies and global dynamic patterns, which may lead to critical information loss in certain fault signals, thereby affecting diagnostic performance. To address these shortcomings, this study integrates LSTM networks with 1D-CNNs. LSTM's superior memory capabilities enable the effective capture of long-term dependencies within time-series data, complementing 1D-CNN's local feature extraction. By combining 1D-CNN and LSTM, the proposed model preserves the sensitivity of CNN to local patterns while simultaneously leveraging LSTM's ability to model long-range temporal dependencies. This hybrid approach significantly enhances fault diagnosis accuracy and robustness [11]. For instance, [PERSON] et al. demonstrated the effectiveness of 1D-CNN in fusing multimodal bearing sensor signals while incorporating visualization techniques to analyze model behavior [12]. [PERSON] et al. utilized 1D-CNNs for the real-time processing of large-scale bearing datasets, revealing superior performance compared to other deep learning models [13]. Additionally, [PERSON] et al. proposed a 1D-CNN architecture incorporating progressively reduced convolutional kernel sizes and dropout operations, achieving a diagnostic accuracy of 98% on public bearing datasets [14]. Furthermore, [PERSON] et al. developed a hybrid CNN-LSTM model, demonstrating high recognition accuracy even under noisy conditions [15], while [PERSON] et al. introduced a CNN-BiLSTM framework for extracting integrated vibration and rotational speed features, effectively diagnosing gearbox faults in varying operational conditions [16]. Despite the significant advancements achieved through CNN-LSTM integration, practical deployment remains challenging due to data complexity, noise interference, and imbalanced fault sample distributions.