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Modal identification based on the time-frequency domain decomposition of unknown-input dynamic tests

机译:基于未知输入动态测试时频域分解的模态识别

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摘要

This paper proposes the time-frequency domain decomposition method for the modal identification of ambient vibration testing. The continuous wavelet transform of the matrix of response correlation functions is first computed from raw data and then decomposed using singular value decomposition, leading to singular values and singular vectors. The singular values are used for the identification of frequencies and damping ratios, while the singular vectors are used for mode shapes. Modal parameters are estimated over a stability interval of time, taking into account the influence of the edge effects of the continuous wavelet transform, the weak variation in instantaneous damped frequencies and the high correlation of successive instantaneous mode shapes. A full procedure for the method is proposed and validated using both simulated responses and recorded data from a real structure. The results show a good identification of frequencies and mode shapes for linear systems having close modes or even repeated modes. Damping estimates present however some discrepancies for lower modes in comparison with other operational modal analysis techniques.
机译:提出了一种时频域分解方法,用于环境振动测试的模态识别。首先从原始数据计算响应相关函数矩阵的连续小波变换,然后使用奇异值分解将其分解,从而得到奇异值和奇异矢量。奇异值用于识别频率和阻尼比,而奇异矢量用于模式形状。考虑到连续小波变换的边缘效应,瞬时阻尼频率的弱变化以及连续瞬时模式形状的高度相关性,在稳定的时间间隔内估计模态参数。提出并使用模拟响应和来自真实结构的记录数据验证了该方法的完整过程。结果表明,对于具有闭合模式甚至重复模式的线性系统,可以很好地识别频率和模式形状。但是,与其他运行模式分析技术相比,阻尼估计值显示出较低模式的一些差异。

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