Modal parameter identification is a useful tool to reveal natural vibration characteristics in dynamic design and analysis. Due to the influence of closely spaced modes with close frequencies and large dampings, identification accuracy using traditional Hilbert–Huang transform is always unsatisfactory, since the deadline requirement of neighboring intrinsic mode functions cannot be satisfied when the frequency ratio (high frequency versus low frequency) of two adjacent modes is less than two. Therefore, an inverse decaying frequency modulation empirical mode decomposition method is proposed, in order to separate multiple closely spaced modes in high precision, which not only adjusts the frequency ratio by frequency modulation, but also decreases damping by inverse decaying simultaneously. Next, the modal parameters of each separated single mode are picked up by means of Laplace wavelet correlation filtering. The effectiveness of the proposed method is validated by simulated sign...

中文翻译：

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拉普拉斯小波相关滤波的高精度衰减模态反演EMD方法

模态参数识别是揭示动态设计和分析中固有振动特性的有用工具。由于具有紧密频率和大阻尼的紧密间隔模式的影响，使用传统的希尔伯特-黄变换（Hilbert-Huang transform）的识别精度始终不能令人满意，因为当频率比（高频与低频）无法满足相邻固有模式函数的截止期限要求时，两个相邻模式的小于两个。因此，提出了一种反衰减频率调制经验模式分解方法，以高精度分离多个紧密间隔的模式，不仅可以通过调频来调整频率比，而且可以同时降低反衰减的阻尼。下一个，每个分离的单模的模态参数通过拉普拉斯小波相关滤波得到。仿真结果验证了该方法的有效性。