基于EEMD的立管涡激振动响应最优降噪光滑模型参数识别研究

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摘要深水海洋立管因受到海洋环境的影响产生涡激振动现象，长期的涡激振动易导致立管产生疲劳破坏。为了获得真实的涡激振动特性，文中进行海洋立管涡激振动实验并对测试信号进行总体平均经验模态（EEMD）分解。基于分解得到的本征模态函数建立滤波算法，考虑曲线光滑度和重构信号与实测信号相似度，确立最优降噪光滑模型区间，依据存在度确定有效本征模态函数以及最优降噪光滑模型，优化目标函数。从相关度、频谱分析、能量谱分析三个角度出发进行合成信号仿真识别，并验证了算法的准确性。结果表明：构建的最优降噪光滑模型得到的有效本征模态函数，与原始信号频率成分相关系数最高，达到0.96以上，且频率对应相等；其能量占原始信号总能量的97.98%。涡激振动试验结果表明：建立的最优降噪光滑模型确定的有效本征模态函数包含顺流向、横流向振动信息，且顺流向主频是横流向频率的2倍。

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	刘志慧1
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	徐兴平2
	牛怀磊3
	王腾1

关键词 ：涡激振动, 总体平均经验模态, 光滑性, 相似度, 存在度, 有效本征模态函数

Abstract：In order to obtain the real characteristics of vortex-induced vibration, the overall ensemble empirical mode decomposition (EEMD) of the signals is carried out. A filtering algorithm is established using the decomposed intrinsic mode function. Considering the smoothness of the curve and the similarity between the reconstructed signal and the measured signal, the optimal noise reduction smooth model interval is established. Determining the effective intrinsic mode function according to the degree of existence. The synthetic signal is simulated and identified from the perspectives of correlation degree, spectrum analysis and energy spectrum analysis, and the accuracy of the algorithm is verified. The accuracy of the algorithm is verified from three angles: correlation degree, spectrum analysis and energy spectrum analysis. The results show that the optimized IMFs contains the information of vortex-induced vibration, and the main frequency in the downstream direction is twice as much as that in the transverse direction.

Key words： vortex-induced vibration ensemble empirical mode decomposition (EEMD) smoothness similarity; the degree of existence effective intrinsic mode function

收稿日期: 2021-03-01 出版日期: 2022-06-28

引用本文:

刘志慧1,2，徐兴平2，牛怀磊3，王腾1. 基于EEMD的立管涡激振动响应最优降噪光滑模型参数识别研究[J]. 振动与冲击, 2022, 41(12): 254-260.
LIU Zhihui1，2, XU Xingping2, NIU Huailei3, WANG Teng1. A study on parameter identification of optimal noise reduction smooth model for vortex-induced vibration response of riser based on EEMD. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(12): 254-260.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I12/254

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