Modeling and learning-based calibration of residual current devices

An important step in the manufacturing process of residual current devices consists of their calibration. The latter is a time-consuming procedure necessary for the proper operation of these devices. The main goal of this document is to propose a solution to increase the efficiency of the calibration workstations, by reducing the overall time of this manufacturing step. To successfully achieve this goal, a much more accurate model is needed compared to the one being used nowadays. The system under study is dominated by a huge amount of uncertainty. This is due to the high number of parameters involved, each of them characterized by very large tolerances. In the approach being used here, the governing physical equations have been integrated with a Bayesian learning process. By doing that, the benefits concerning the knowledge of the underlying physics are combined with the uncertainty estimation provided by the stochastic model resulting in a more robust and accurate model. The Bayesian learning method used in this study case regards Gaussian Process modeling, starting from a physically-based prior and updating it as data is being observed. This is repeated for every device that needs to be calibrated. The result is an adaptive modeling procedure that can be easily implemented and used directly in the manufacturing process to achieve a faster calibration and decrease the overall process time. The estimated improvement of the proposed solution compared to the one being used nowadays is about 24% fewer calibration attempts on average. The encouraging results that have been obtained in the simulation have prompted implementing and testing it on the real process.