A MVMD–MMFE algorithm and its application in the flow patterns identification of horizontal oil–water two-phase flow

Abstract

Abstract Aiming to extract the main information features of fluid multivariate conductance signals and identify the flow patterns under different flow velocities, we present a multichannel time series analysis algorithm based on the multivariate variational mode decomposition (MVMD) and multivariate multiscale fuzzy entropy (MMFE). Firstly, by simulating a multichannel complex signal and performing a series of sensitivity experiments within various noise intensities, we prove the feasibility of the MVMD in chaotic time series. Then, we employ the MVMD to decompose multivariate conductance signals into the intrinsic mode function (IMF) and calculate the MMFE of the IMFs for different flow patterns. Meanwhile, the multivariate empirical mode decomposition (MEMD) is also applied on the comparison of signal decomposition. Finally, we discuss the classification consequence under different mode values k to realize the optimal decomposition. The experimental results show that the MVMD–MMFE algorithm can extract the main information of fluid multichannel signals and distinguish three horizontal oil–water flow patterns effectively, which provides an idea for studying the nonlinear characteristics of the chaotic system.