A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

Abstract

The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal visualization and Data Transfer (DT) are used to add tower fault data samples to solve the problem of insufficient actual data quantity. Subsequently, the dynamic response test is carried out under different tower fault states, and the tower fault monitoring is carried out by the DL method. Finally, the proposed method is compared with the traditional online monitoring method, and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process. The results show that the method can effectively identify the tower foot displacement state, which can greatly reduce the accidents that occurred due to the tower foot displacement.