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Enhanced least square complex frequency method for operational modal analysis of noisy data

机译:增强的最小平方复杂频率方法,用于噪声数据的操作模态分析

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摘要

Operational modal analysis is being widely used in aerospace, mechanical and civil engineering. Common research fields include optimal design and rehabilitation under dynamic loads, structural health monitoring, modification and control of dynamic response and analytical model updating In many practical cases, influence of noise contamination in the recorded data makes it difficult to identify the modal parameters accurately. In this paper, an improved frequency domain method called Enhanced Least Square Complex Frequency (eLSCF) is developed to extract modal parameters from noisy recorded data. The proposed method makes the use of pre-defined approximate mode shape vectors to refine the cross-power spectral density matrix and extract fundamental frequency for the mode of interest. The efficiency of the proposed method is illustrated using an example five story shear frame loaded by random excitation and different noise signals.
机译:操作模态分析广泛应用于航空航天,机械和土木工程。 常见的研究领域包括在许多实际情况下的动态负荷下的最佳设计和康复,动态响应和分析模型更新的动态响应和分析模型,噪声污染的影响难以准确地识别模态参数。 在本文中,开发了一种称为增强型最小二乘复杂频率(ELSCF)的改进的频域方法以从嘈杂的记录数据中提取模态参数。 该方法使得使用预定义的近似模式形状向量来优化交叉功率谱密度矩阵并提取感兴趣模式的基本频率。 使用由随机激励和不同噪声信号加载的示例五层剪切框架来说明所提出的方法的效率。

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