Developing a Long Short-Term Memory-based signal processing method for Coriolis mass flowmeter
| Autor: | Liu Yajun, Zhang Yanjin, Weiping Liang, Liu Zhendong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Signal processing
Mean squared error Mass flow meter Computer science business.industry Applied Mathematics Deep learning 020208 electrical & electronic engineering 010401 analytical chemistry Normalization (image processing) 02 engineering and technology Condensed Matter Physics 01 natural sciences Signal Flow measurement 0104 chemical sciences Moving average 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering business Instrumentation Algorithm |
| Zdroj: | Measurement. 148:106896 |
| ISSN: | 0263-2241 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect