Since the advent of Industry 4.0, many companies feel the need to rely on industrial renewal to be more competitive on the market in terms of increasing productivity and reducing time and costs. In the context of predictive maintenance, the estimation of the State of Health (SoH) of CNC machines end-effector tools plays a crucial role and turns out to be an interesting challenge for the scientific community and a real need for cutting-edge companies. Some possible solutions have been proposed in literature to address this topic but most of them rely on machine learning and cloud computing algorithms which are not efficiently suitable to a real-time edge device implementation.