Detecting flow meter drift by using artificial neural networks

Abstract

In this paper, artificial neural networks (ANNs) were used to assess the performance of flow meters used in industrial water supply. These flow meters are susceptible to drift, a condition causing them to give erroneous readings that are inconsistent with the actual flow. A simulation of industrial water flow to the industrial consumers was made. This simulation contained both healthy and drifting flow meter readings. ANN was built and trained on the simulated data. At the time of testing, the ANN developed was correct 89.52% of the time in determining the status of the flow recorded by a flow meter. © 2011 WIT Press.