Abstract Contrary to most signal decomposition methods that usually decompose an original signal into a series of components simultaneously, a novel approach based on repeated extraction of Maximum Energy Component (MEC) is proposed. The approach starts from determination of the MEC referring to the estimated Power Spectral Density (PSD) function, and then represents the MEC by employing an exponential function to fit the original signal. By defining a stopping criterion based on two adjacent estimated PSDs, each MEC can be accurately extracted with an improved performance throughout the entire signal decomposition. To verify the proposed method, a single degree-of-freedom system subject to harmonic loads has been examined. Numerical results show that the analytical response can not only be decomposed into four MECs corresponding to the excitation and the system, respectively, but also provide an accurate estimation of natural frequency and damping ratio of the system. Meanwhile, by observing results from the Ensemble Empirical Mode Decomposition (EEMD), Variational Mode Decomposition (VMD) and Prony based on state-space model (Prony-SS), an improved decomposition accuracy has been achieved from the proposed approach. Furthermore, experimental data from the Norwegian Deepwater Programme and two sets of field-test data from one fixed offshore platform and an offshore wind turbine have been used to demonstrate the correctness of the developed signal decomposition method. It is noted that divergence in results by Prony-SS can be observed when a very large model order is used, while the proposed method provides the better decomposition and reconstruction of signals.

中文翻译：

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一种基于重复提取的海上结构物最大能量分量的信号分解方法

摘要 与大多数信号分解方法通常将原始信号同时分解为一系列分量不同，提出了一种基于重复提取最大能量分量（MEC）的新方法。该方法从参考估计的功率谱密度 (PSD) 函数确定 MEC 开始，然后通过使用指数函数拟合原始信号来表示 MEC。通过定义基于两个相邻估计 PSD 的停止标准，可以准确地提取每个 MEC，并在整个信号分解过程中提高性能。为了验证所提出的方法，已经检查了受谐波载荷影响的单自由度系统。数值结果表明，解析响应不仅可以分解为分别对应于激励和系统的四个 MEC，而且可以准确估计系统的固有频率和阻尼比。同时，通过观察集成经验模式分解（EEMD）、变分模式分解（VMD）和基于状态空间模型的 Prony（Prony-SS）的结果，该方法提高了分解精度。此外，来自挪威深水计划的实验数据和来自一个固定海上平台和一个海上风力涡轮机的两组现场测试数据已被用来证明所开发的信号分解方法的正确性。