Complete ensemble empirical mode decomposition with adaptive noise for dynamic response reconstruction of spacecraft structures under random vibration

Yumei Ye, Jingang Zhang, Qiang Yang, Songhe Meng, Jun Wang
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Abstract

The dynamic responses of key regions are critical inputs for the structural life estimation of spacecraft. Response reconstruction methods are needed for structural locations where sensors are not placed due to resource limitations. In this paper, a reconstruction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. CEEMDAN can eliminate the mode-mixing phenomenon of traditional empirical mode decomposition (EMD) during signal decompositions to improve the reconstruction accuracy. The proposed method is applied to the reconstruction of acceleration and strain responses at critical locations of a load-bearing structure under sinusoidal and random vibration loads. Numerical and experimental validation are carried out. The numerical results show that the reconstructions are almost unaffected by the selected white noise levels of CEEMDAN and the locations of measured and targeted points. The experimental results show that compared with traditional EMD, the reconstruction accuracy of CEEMDAN is improved by a maximum of 79.94% with almost no additional computational cost. The proposed reconstruction method shows efficiency and accuracy for a wide range of applications.
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带有自适应噪声的完整集合经验模式分解,用于随机振动下航天器结构的动态响应重建
关键区域的动态响应是航天器结构寿命估算的关键输入。对于因资源限制而未放置传感器的结构位置,需要响应重建方法。本文提出了一种基于自适应噪声的完全集合经验模式分解(CEEMDAN)的重构方法。CEEMDAN 可以消除传统经验模态分解(EMD)在信号分解过程中的模态混合现象,从而提高重建精度。所提出的方法被应用于正弦和随机振动载荷下承重结构关键位置的加速度和应变响应的重建。进行了数值和实验验证。数值结果表明,重建结果几乎不受所选 CEEMDAN 白噪声水平以及测量点和目标点位置的影响。实验结果表明,与传统的 EMD 相比,CEEMDAN 的重建精度最高提高了 79.94%,而且几乎不增加计算成本。所提出的重建方法显示出了广泛的应用效率和准确性。
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