结构连续动态监测的自动化程序:理论与验证

IF 2.7 3区 工程技术
Y. V. Ardila, I. D. Gómez-Araújo, J. D. Villalba-Morales
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引用次数: 0

摘要

摘要稳定性图是进行运行模态分析以获得结构物理模态的有用工具。这些模式可以通过可视化稳定的列来定义,这些列是由一系列模型顺序上一致识别的模式形成的。如果需要连续识别模态参数,手动提取这些模态将成为一个障碍。材料和方法本文配置了一个程序来自动解释由SSI-COV/ref算法识别结果构建的稳定性图。这个过程是基于文献中发现的一些方法,它遵循三个阶段。首先,定义了稳定性图清理阶段,使用模态验证标准和划分聚类算法检测杂散模态。其次,实现基于层次聚类算法的模式分组阶段,形成共享相似模态信息的模式集;最后,采用选择阶段从物理模态集合中定义具有代表性的模态参数。结果通过对一个受环境温度函数影响的十自由度梁型结构模型的模拟,验证了所提方法的有效性。用频域分解方法计算的DT140和DT220数据集的固有频率与采用本文方法计算的固有频率一致,MAC系数均大于0.97。共模拟了192个数据集,加速度响应被SRN = 40 [dB]和SNR = 20 [dB]两种噪声水平污染。结论对于所分析的梁,模态跟踪结果表明,该方法可以实现连续的自动识别。固有频率的变化与环境温度函数的变化相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automated Procedure for Continuous Dynamic Monitoring of Structures: Theory and Validation
Abstract Introduction Stability diagrams are a helpful tool for operational modal analysis to obtain the physical modes of a structure. These modes can be defined by visualizing stable columns formed by consistently identified modes over a range of model orders. Extracting these modes manually becomes an obstacle if continuous identification of modal parameters is required. Materials and methods In this paper, a procedure is configured to automatically interpret the stability diagrams constructed with the identification results of the SSI-COV/ref algorithm. This procedure is based on some methodologies found in the literature, which follow three stages. First, a stability diagram cleaning stage is defined where modal validation criteria and partitioning clustering algorithms are used to detect spurious modes. Second, a mode grouping stage based on a hierarchical clustering algorithm is implemented to form sets of modes that share similar modal information. Finally, a selection stage is applied to define representative modal parameters from the set of physical modes. Results The proposed procedure is validated by simulating a beam-type structural model with ten degrees of freedom affected by ambient temperature functions. Natural frequencies computed for the DT140 and DT220 datasets collection with the frequency-domain decomposition method agree with the computed ones with the proposed procedure, presenting MAC coefficients higher than 0.97. A total of 192 datasets are simulated, and the acceleration responses are polluted with two noise levels, SRN = 40 [dB] and SNR = 20 [dB]. Conclusions For the analyzed beam, the modal tracking results showed that the procedure could perform continuous identification automatically. The variations in the natural frequencies are correlated to the variations in the ambient temperature functions.
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