A Multy Layer Perceptron Neural Network able to recognize inter-turn short-circuits in the stator of induction machines is presented. The network is inserted in an on-line diagnostic system which utilizes the machine voltages and currents as input signals. The current computed by a faulted machine models are analyzed with the aim to evidence the variables more suitable to characterize the short circuit's effects. These variables, properly normalized, are the input data sets for the learning process of the network, which is therefore applicable to a class of induction machines.
Neural network aided on-line diagnostics of induction machine stator faults / Filippetti, F.; Franceschini, G.; Tassoni, C.; Meo, S.; Ometto, A.. - 1:(1995), pp. 148-151. (Intervento presentato al convegno Proceedings of the 1995 30th Universities Power Engineering Conference. Part 1 (of 2) tenutosi a London, UK, null nel 1995).
Neural network aided on-line diagnostics of induction machine stator faults
Franceschini, G.;
1995
Abstract
A Multy Layer Perceptron Neural Network able to recognize inter-turn short-circuits in the stator of induction machines is presented. The network is inserted in an on-line diagnostic system which utilizes the machine voltages and currents as input signals. The current computed by a faulted machine models are analyzed with the aim to evidence the variables more suitable to characterize the short circuit's effects. These variables, properly normalized, are the input data sets for the learning process of the network, which is therefore applicable to a class of induction machines.
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
