Using Neural Networks with Linear Regression as a Scalable Model to Predict the Behaviour of Pumps

This article presents a method for predicting the behavior of external gear pumps using a neural network, to optimize a dosing process. Small neural networks learn to map the characteristic curves of fluid machinery. The presented method achieves an error flow rate of approximately 3.28 milliliters per minute. The neural networks make it possible to substitute the volume flow sensor in order to design dosing applications without cost-intensive volume flow sensors. However, due to the existing manufacturing tolerances of the used pumps, the overall pump efficiency differs significantly. For this reason, it is not directly possible to apply the networks universally to an entire pump series, which strongly limits the industrially scalable solution. For this reason, the algorithms are extended by a linear regression model, which enables users to calibrate the neural networks to the actually used pumps based on very few reference measurements.