基于深度学习的海上风电导管架基础振动异常模式识别与预警技术OA

Aiming at the problem of abnormal vibration identification of offshore wind power jacket foundations,a recognition and early warning system based on deep learning is established.By combining wavelet packet transform and convolutional neural networks,multi-scale vibration features are extracted;By fusing multi-source sensor data through the attention mechanism,an end-to-end anomaly recognition model is constructed,and an energy-saving technical solution for vibration control and energy recovery is designed.The results show that through scale-down tests and engineering verification,the structural optimization reduces weight by 25%,and the vibration control reduces the peak response by 35%to 42%,effectively lowering operation and maintenance costs and shortening the payback period of investment.