TY  - EJOU
AU  - Zhao, Wenhai 
AU  - Li, Wanrun 
AU  - Li, Ximei 
AU  - Li, Shoutu 
AU  - Du, Yongfeng 

TI  - Dynamic Characteristic Testing of Wind Turbine Structure Based on Visual Monitoring Data Fusion
T2  - Structural Durability \& Health Monitoring

PY  - 2025
VL  - 19
IS  - 3
SN  - 1930-2991

AB  - Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies, as well as the inability to achieve precise full-field monitoring, this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion. Firstly, the Lucas-Kanade Tomasi (LKT) optical flow method and a multi-region of interest (ROI) monitoring structure are employed to track pixel displacements, which are subsequently subjected to band pass filtering and resampling operations. Secondly, the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering. The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data. Based on this, the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor. Subsequently, when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion, the structural vibration equation and boundary condition constraints, among other key parameters, are employed to predict the displacements at unknown monitoring points, thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure. Finally, a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification. The research results indicate that the proposed method achieves a time-domain error within the sub-millimeter range and a frequency-domain accuracy of over 99%, effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.
KW  - Structural health monitoring; dynamic characteristics; computer vision; vibration monitoring; data fusion

DO  - 10.32604/sdhm.2024.057759