This paper presents a neural network that is able to give, together with the rotor fault diagnosis, the combined rotor-load inertia momentum of an induction machine. The inputs of the network are the spectral components of machine input currents, speed and torque. A specific neural network architecture containing new fast spline-based neurons with improved generalization capabilities has been used. The training set is obtained by a faulted machine dynamical model as simulator.
Neural network architectures for fault diagnosis and parameter recognition in induction machines / Filippetti, F.; Uncini, A.; Piazza, C.; Campolucci, P.; Tassoni, C.; Franceschini, G.. - 1:(1996), pp. 289-293. (Intervento presentato al convegno Proceedings of the 1996 8th Mediterranean Electrotechnical Conference, MELECON'06. Part 3 (of 3) tenutosi a Bari, Italy, null nel 1996).
Neural network architectures for fault diagnosis and parameter recognition in induction machines
Franceschini, G.
1996
Abstract
This paper presents a neural network that is able to give, together with the rotor fault diagnosis, the combined rotor-load inertia momentum of an induction machine. The inputs of the network are the spectral components of machine input currents, speed and torque. A specific neural network architecture containing new fast spline-based neurons with improved generalization capabilities has been used. The training set is obtained by a faulted machine dynamical model as simulator.
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