Karami-Mohammadi R, Mirtaheri M, Salkhordeh M, Hariri-Ardebili M A
Hybrid Simulation Laboratory, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.
University of Maryland, College Park, MD 20742, USA.
Sensors (Basel). 2020 Mar 20;20(6):1731. doi: 10.3390/s20061731.
This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert-Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring.
本研究提出了一种识别输电塔振动特性并检测潜在结构损伤的技术。该方法基于振型曲率与连续小波变换相结合。所阐述的数值方法基于环境振动产生的输出信号处理。该技术得益于数量有限的传感器，与其他方法相比，这使其成为一种具有成本效益的方法。这些传感器的最佳空间位置通过最小化模态置信准则（MAC）矩阵中的非对角元素来获得。希尔伯特 - 黄变换也被用于识别结构的动态特性。为了模拟现场条件下测量的结构响应的实际情况，在数值模型的响应中添加了10%的噪声。考虑了四种损伤情况，并使用小波变换对完好塔和受损塔的振型曲率差异进行分析，从而识别潜在损伤。结果表明，考虑到所使用的传感器数量较少，该方法具有可观的精度。本研究提出了一种用于结构健康监测的低成本且可行的技术。
