Submit

Login Login

Register Register

Home / Journals / SDHM / Online First / doi:10.32604/sdhm.2024.057759
Journal Menu
Special Issues
Table of Content

All issues

Online First

2025

2024

2023

2022

2021

2020

2019

2018

2017

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

Open Access

ARTICLE

Dynamic Characteristic Testing of Wind Turbine Structure Based on Visual Monitoring Data Fusion

Wenhai Zhao1, Wanrun Li1,2,*, Ximei Li1,2, Shoutu Li3, Yongfeng Du1,2
1 Institution of Earthquake Protection and Disaster Mitigation, Lanzhou University of Technology, Lanzhou, 730050, China
2 International Research Base on Seismic Mitigation and Isolation of GANSU Province, Lanzhou University of Technology, Lanzhou, 730050, China
3 School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
* Corresponding Author: Wanrun Li. Email: email
(This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)

Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2024.057759

Received 27 August 2024; Accepted 17 October 2024; Published online 25 November 2024

Abstract

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.

Keywords

Structural health monitoring; dynamic characteristics; computer vision; vibration monitoring; data fusion

  • 99

    View

  • 15

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link