Research on Application of Intelligent Algorithm for Structural Health Monitoring of Onshore Assembled Wind Power Mixing Tower

Dry-connected prefabricated steel-concrete composite tower (mixed tower) has become a new trend of wind power tower construction because of its advantages of high efficiency, economy and environmental protection. In order to ensure the healthy state of the mixed tower structure, a comprehensive index system covering vibration, strain, temperature and tilt monitoring is constructed in this study, and high-performance sensors and XBee modules are used to realize real-time data acquisition and remote monitoring. Preprocessing techniques such as filtering and normalization are used to improve the data quality, and convolutional neural network (CNN) is used to learn the structural state and identify the damage. The experimental results show that CNN model shows high accuracy and consistency in structural damage identification and performance degradation prediction, and with the growth of data set, the response time of the model is still within the acceptable range. This shows that the effective combination of the monitoring system and deep learning algorithm established in this study provides strong support for the health assessment and maintenance of the mixed tower.