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Localization of steel strand damage based on Empirical Mode Decomposition algorithm

机译:基于经验模态分解算法的钢绞线损伤定位

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In order to accomplish rapid detection of the damage in mooring steel strand of the offshore platform this paper proposes to use the ultrasonic guided wave method for the online damage detection. Considering the non-stationary characteristics of the ultrasonic signal, this paper proposes an Empirical Mode Decomposition} (EMD)-based algorithm to detect and localize the position of steel strand damage. To be specific, the detect signal is firstly decomposed by EMD into multiple Intrinsic Mode Functions (IMFs). Then, in order to find the weak damage signal which is usually buried in noise, a weighted synthesis procedure is adopted, i.e., assigning more weights to the dominant components of IMFs. By doing so, the reconstruction signal is denoised and the damage signal can be much more easily captured. Simulation results show that the proposed method can effectively detect the ultrasonic guided wave signal and realize the accurate localization of the weak damage to the steel strand by comparing with the traditional method of directly selecting several natural IMFs.
机译:为了实现对海上平台系泊钢绞线损伤的快速检测,本文提出了利用超声波导波法进行在线损伤检测的方法。考虑到超声信号的非平稳特性,本文提出了一种基于经验模态分解(EMD)的算法来检测和定位钢绞线损伤的位置。具体而言,首先将检测信号通过EMD分解为多个本征模式功能(IMF)。然后,为了找到通常被噪声掩盖的弱损伤信号,采用加权合成程序,即,给IMF的主要成分分配更多的权重。这样,可以对重建信号进行去噪,并且可以更容易地捕获损坏信号。仿真结果表明,与直接选择几种天然IMF的传统方法相比,该方法可以有效地检测出超声波导波信号,实现对钢绞线弱损伤的精确定位。

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