Median ensemble empirical mode decomposition
Median ensemble empirical mode decomposition
复制标题
中值系综经验模态分解
DOI：
10.1016/j.sigpro.2020.107686
复制
发表时间：
2020-11-01
影响因子：
4.4
通讯作者：
Su, Hongye
中科院分区：
文献类型：
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作者：
Lang, Xun;Rehman, Naveed Ur;Su, Hongye
集成经验模式分解(EEMD)是一类噪声辅助的EMD方法，旨在缓解噪声和信号间歇性引起的模式混合。在这项工作中，我们提出了一种中值集成版本的EEMD(MEEMD)，以帮助减少原始EEMD算法的附加模式分裂问题。这是通过在集成过程中用中值操作符替换平均操作符来实现的。我们使用中值算符的动机是对EEMD和MEEMD的模式分离率进行了严格的分析。结果表明，EEMD具有不可移除的新模式分裂，而所提出的方法可以在50%的故障点上大大减少这一问题。通过大量的数值算例和工业振荡情况，验证了这项工作在减小模式分裂方面的作用。(C)2020爱思唯尔B.V.保留所有权利。
Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm. That is achieved by replacing the mean operator with the median operator during the ensemble process. Our use of the median operator is motivated by a rigorous analysis of mode splitting rates for both EEMD and MEEMD. It is shown that EEMD comes with irremovable new mode splitting while the proposed method can greatly reduce this problem on a breakdown point of 50%. This work is verified by extensive numerical examples as well as industrial oscillation case in terms of reducing the mode splitting. (C) 2020 Elsevier B.V. All rights reserved.