Neural Network-Based Method for Structural Damage and Scour Estimation Using Modal Parameters and Dynamic Responses

Abstract

Wind energy is the most promising clean, renewable energies to the power industry in the world. More and more wind turbine structures equipped with the larger capacity, taller towers, and longer blades were installed at the offshore/onshore wind farms. But these structures face many harsh environmental conditions, and structural damage and foundation scour are continuously accumulated. It could alter the modal parameter and dynamic response and further reduce the safety of structures. It is a significant challenge on how to accurately estimate the structural states if there is structural damage or foundation scour. For addressing these limitations, a One Dimensional Convolutional Neural Network (1D CNN) method is developed to estimate the structural state. After the Fast Fourier Transform of the acceleration signals, these frequency responses are used as the input to train the 1D CNN, while these states are estimated as the output. A simplified spring-beam model is introduced to simulate the pile-soil interaction, and the effects of the damage and scour on natural frequencies are investigated and compared. The effectiveness and robustness of the proposed 1D CNN method have been numerically investigated by several scenarios associated with the wind turbine structure. Results demonstrate that the 1D CNN method can accurately estimate the structural states, even under a noisy environment. Further, the 1D CNN method can identify the location of damage and scour depth with very high accuracy. This approach may be useful in the on-site structural health monitoring in the wind turbine structure.