Adaptive EMD based induction signal extraction of electrostatic sensor for particle velocity measurement

Abstract

As the fluid movement, particles transporting in the pipeline will produce electrostatic charge. The electrostatic induction signal has been widely applied in measuring the flow parameters of pneumatic conveying due to its symmetry characteristic and simplicity. However, due to the weak signal and the complexity of the particles in the flow process, the electrostatic signal is easy to be disturbed by the environment, which affects the accuracy of particle flow parameter measurement. In this paper, an adaptive EMD method for extracting electrostatic induction signals of gas solid two-phase flow is proposed. The real IMF components and the IMF components which belong to noise are decomposed adaptively. Then according to the correlation coefficient of autocorrelation function, the corresponding IMF components are selected to reconstruct the electrostatic induction signal. The results show that in the gas-solid two-phase flow experiment, compared with the measurement signals, the mean relative standard deviation of cross-correlation velocity is reduced from 2.87% to 2.61%. This study is conducive to the accurate measurement of gas-solid two-phase flow parameters and provides an effective help for the electrostatic signal analysis of gas-solid two-phase flow.