Water meter calibration is an essential process in order to maintain the performance of a meter, but it is a complicated process. The process contains several stages, when each step includes sampling many measurements from the uncalibrated meters and error calculation for each measurement. Consequently, the calibration process is very slow and expensive. The goal of this project is to significantly shorten the calibration time using a deep learning based method for predicting the results, while maintaining a certain bound on the prediction error. The system uses a dataset provided by ARAD technologies, that contains calibration factors of different water meters.
We present a method that uses many multilayer perceptron networks for prediction. The separation to different networks is a result of the different physical behaviors of fluids at different flow rates.