近年来，工作模态分析在结构参数识别中的地位逐步上升。针对环境激励下结构振动响应信噪比低的特点，引入自回归功率谱对经验傅里叶分解进行改进，并提出了一种基于改进经验傅里叶分解的结构工作模态分析方法。为了验证该方法的可行性和有效性，对四层模拟框架和某人行斜拉桥进行工作模态参数识别，并利用随机子空间所识别的结果进行对比。结果表明，该方法识别的模态参数与随机子空间的结果相当，并且在密集模态情况下，该方法具有一定优势。因此，改进经验傅里叶分解能为今后的结构模态识别提供参考。
Abstract
In recent years, the status of operational modal analysis in structural parameter identification is gradually rising.Here, aiming at low signal-to-noise ratio (SNR) of structural vibration response under environmental excitation, the auto-regressive (AR) power spectrum was introduced to improve the empirical Fourier decomposition, and a structural operational modal analysis method based on improved empirical Fourier decomposition (EFD) was proposed.In order to verify the feasibility and effectiveness of the proposed method, operational modal parameters of a 4-story simulated frame and a pedestrian cable-stayed bridge were recognized using this method, and the results were compared with those identified using the stochastic subspace identification (SSI).The results showed that modal parameters identified using the proposed method agree well with those identified using SSI; in case of dense modes, this method has certain advantages, so improving empirical Fourier decomposition can provide a reference for future structural modal identification.
关键词
模态参数识别 /
经验傅里叶分解(EFD) /
自回归功率谱 /
环境激励
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Key words
modal parameter identification /
empirical Fourier decomposition (EFD) /
auto regression (AR) power spectrum /
ambient excitation
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