DETECTING FLOW METER DRIFT BY USING ARTIFICIAL NEURAL NETWORKS

In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meters used in industrial water supply. These fl ow meters are susceptible to drift, a condition causing them to give errone-ous readings that are inconsistent with the actual fl ow. A simulation of industrial water fl ow to the industrial consumers was made. This simulation contained both healthy and drifting fl ow 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 fl ow recorded by a fl ow meter