In this work, I discuss the implementation and optimization of an Artificial Neural Network based on the analysis of the back-EMF coefficient capable of making ElectroMechanical Actuator (EMA) prognostics. Aircraft manufacturers are increasingly focusing on the use of electromechanical actuators as they have numerous advantages in terms of weight and compactness respect to the technologies adopted at this time. However, they are early technology and for this reason, engineers are still studying the failure modes that characterize this component. The objective of this thesis is to study a methodology for the recognition of faults within the system. To solve the problem my supervisors have thought of implementing a logic, based on artificial intelligence, particularly on artificial neural networks, which allows to estimate the remaining useful life of the system components starting from a training dataset.