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Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method
Applied Mathematics and Mechanics Pub Date : 2019-11-08 , DOI: 10.1007/s10483-019-2547-9
Sha Wei , Shiqian Chen , Zhike Peng , Xingjian Dong , Wenming Zhang

Modal parameter identification is a mature technology. However, there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination. This paper proposes a new time-frequency method based on intrinsic chirp component decomposition (ICCD) to address these issues. In this method, a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction. The effectiveness and accuracy of the proposed method are illustrated using three examples: a cantilever beam structure with intensive noise contamination or environmental interference, a four-degree-of-freedom structure with two closely spaced modes, and an impact test on a cantilever rectangular plate. By comparison with the identification method based on the empirical wavelet transform (EWT), it is shown that the presented method is effective, even in a high-noise environment, and the dynamic characteristics of closely spaced modes are accurately determined.

中文翻译:

基于固有chi成分分解法的多自由度结构模态识别

模态参数识别是一项成熟的技术。但是,在其实际应用中仍然存在一些挑战,例如,确定涉及紧密间隔模式和强烈噪声污染的振动系统。为了解决这些问题,本文提出了一种基于固有线性调频成分分解(ICCD)的时频方法。在这种方法中,使用冗余傅立叶模型来改善边界失真并提高信号重建的准确性。通过三个示例说明了所提方法的有效性和准确性:具有强烈噪声污染或环境干扰的悬臂梁结构,具有两个紧密间隔模式的四自由度结构以及在悬臂矩形板上的冲击试验。
更新日期:2019-11-08
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