Abstract 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.

中文翻译：

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中值集合经验模式分解

摘要 集成经验模态分解(EEMD)属于一类噪声辅助的EMD方法，旨在减轻噪声和信号间断引起的模态混合。在这项工作中，我们提出了 EEMD (MEEMD) 的中值集成版本，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算子替换平均算子来实现的。我们使用中值算子的动机是对 EEMD 和 MEEMD 的模式分裂率进行严格分析。结果表明，EEMD 带有不可去除的新模式分裂，而所提出的方法可以在 50% 的击穿点上大大减少这个问题。在减少模式分裂方面，这项工作得到了大量数值例子以及工业振荡案例的验证。