DRIMOT

This project aims to extend technologies based on the advanced analysis of currents and stray fluxes to the diagnosis of failures in drives, coupling systems and loads in kinematic chains of electric motors. The aim is to continue the fruitful research developed by the team in previous projects related to the determination of the condition of electric motors. As a final objective, it is intended to extend the scope of application of the intelligent sensors developed in previous projects, which were mainly focused on the electric motor itself, so that they are also capable of diagnosing the health of other elements of the kinematic chain. Additionally, the system to be developed must allow the determination of the efficiency of the considered elements of the kinematic chain, as well as quantify the degree in which their efficiency is affected by the health of the corresponding element. For the development of the intelligent system, techniques based on the combined analysis of currents and stray fluxes will be used, measured on the electric motor itself. The idea is that, using these quantities, it is possible to determine the condition of the rest of the components of the kinematic chain (drive, coupling system and load), detecting possible failures in them and quantifying their severity. The team experience in past projects, in which these techniques were successfully applied to detect faults in a wide range of electric motors (induction motors, wound-rotor synchronous motors, synchronous reluctance motors and even direct current motors), as well as the preliminary tests carried out in relation to the diagnosis of the condition of other components of the chain, sustain the viability of the proposal. The intelligent system to be developed will combine the use of classical techniques based on the analysis of currents and stray fluxes at steady state, with modern methodologies based on the analysis of these quantities in transient regimes, which have been optimized in previous projects. These technologies will be completed with the computation of statistical parameters on the measured signals, as well as with other techniques that have shown great potential for the diagnosis. On the other hand, artificial intelligence techniques will be used, so that the system can operate autonomously, providing an automatic diagnosis without the need for intervention by an expert user. The system will be validated in multiple laboratory kinematic chains, as well as in the industrial environment (there is support from up to ten companies interested in collaborating with the research).

For the development of the project, there is a powerful team, with great experience and prestige in the area, based on researchers from the Universitat Politecnica de Valencia (UPV) and Universidad Politecnica de Madrid (UPM), as well as on world experts from other Universities. such as Korea University (South Korea), Michigan State University (USA), Technical University of Crete (Greece), Université Claude Bernard Lyon 1 (France), Universidad de Queretaro (Mexico) and Universidad de Concepción (Chile).

Duration: September 2025- September 2028                 Principal Investigators: Prof. JOSE A ANTONINO-DAVIU, Prof. LARISA DUNAI