Signal Processing Scheme of Step Excitation Electromagnetic Flowmeter Based on Outlier Elimination and Double Median Filtering

Abstract

The step excitation electromagnetic flowmeter (EMF) can be used to measure slurry-type fluid. Due to the interference of slurry noise, the flow signal will have irregular baseline drift and fluctuation during measurement. This will cause abnormal abrupt changes in the amplitude demodulated flow signal. In order to reduce the influence of slurry noise, in this paper, a step excitation electromagnetic flowmeter baseline drift model is established, and the outlier identification method is proposed accordingly. Based on this, the paper proposes a step excitation electromagnetic flowmeter signal processing scheme based on outlier elimination and double median filtering. This scheme uses polynomial least squares method to remove outliers firstly, then uses double median filtering to smooth the flow signal. The paper carried out the comparison experiment between this scheme and moving average median algorithm. Experiments show that steady-state volatility of the outlier elimination and double median filtering algorithm are better than the comparative algorithm. The scheme of outlier elimination and double median filtering can effectively suppress slurry noise and improve the ability of step excitation electromagnetic flowmeter to overcome slurry noise.