Developing a Long Short-Term Memory-based signal processing method for Coriolis mass flowmeter

Abstract

Coriolis mass flowmeter is widely used in various fields due to its high accuracy, but it still needs to be improved in some special conditions. This paper proposes a deep learning-based signal processing method for Coriolis mass flowmeter. Firstly, we set up an experimental platform to collect data, taking the vibration signal as the input feature and the mass flow as the sample label. Secondly, we designed networks with different structures (including LSTM, RNN and ANN) and adopted batch normalization to speed up convergence. Finally, Bayesian model fusion and moving average were used to reduce generalization error. Experiment results prove that the model with LSTM layer is better than other single models and the mean square error of the optimized model reduces to 0.0047, which is far superior to the calibrated meter (0.1200). These findings that get rid of traditional methods are expected to break through existing bottlenecks.