Multi-Bolt Structural Loosening Fault Identification Based on Black-Winged Kite Algorithm and CNN-GRU

Wind turbines serve as core equipment for renewable energy development and utilization. Tower high-strength bolts are critical load-bearing components of wind turbines, making precise detection and early identification of their failures of significant importance. By addressing the challenges of extracting failure features from tower high-strength bolts and the insufficient generalization capability of traditional intelligent diagnostic models, this study achieves the precise detection and early identification of bolt-loosening failures. This study independently constructed a simulation platform to collect bolt vibration data and proposed a diagnostic model—BKA-CNN-GRU—based on a CNN-GRU architecture enhanced by the Black-winged Kite Algorithm (BKA). This approach enables precise detection and early identification of high-strength bolt-loosening failures. The specific research approach involved first establishing experimental conditions with varying bolt tightening levels to capture time-domain vibration signals. Subsequently, Fast Fourier Transform (FFT) was applied to convert these signals into frequency-domain data. Building upon this, BKA optimization was employed to refine key hyperparameters of the CNN-GRU model, thereby enhancing diagnostic performance. Experimental results demonstrate that, under the same dataset, the BKA-CNN-GRU model achieves an average accuracy of 99.31%, significantly outperforming comparison models such as CNN-GRU. Furthermore, the model exhibits robust performance and strong generalization capabilities under varying sample proportions and small-sample conditions.