Setting up a reliable electric propulsion system in the automotive domain calls for a smart condition monitoring device that is able to reliably assess the state and the health of the electric motor. To allow massive integration of such monitoring devices, it is required of them to be low-cost and miniature. Those requirements pose limitations on their accuracy, however, we show in this paper that those limitations can be significantly reduced by suitably processing the sensor data. We used machine learning models (random forest and XGBoost) to transform very noisy measurements of motor winding insulation resistance measured by a low-cost device to the much more reliable value with which we are able to compete with measurements made by the state-of-the-art high-priced measuring system. The proposed methodology represents a crucial building block in future smart condition monitoring system and enables low-cost and accurate assessment of electric motor health connected to the state of its winding insulation.