This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.

中文翻译：

---

一种识别油水两相流流型的新方法

本文提出了一种结合极限学习机（ELM）和多经验模式分解（MEMD）的新颖方法来识别油水两相流的流型。提出的方法可以准确识别水平油水两相流的五个典型流型。以Lorenz系统为例，我们证明了MEMD比经验模态分解和整体经验模态分解更适合同时分解多通道信号。在提出的方法中，我们利用MEMD分解油水两相流的多元电导信号以获得相同的固有模式函数模式，选择高频分量的归一化能量作为特征值，并利用经过训练的ELM取得了良好的识别效果。