An improvement of induction machine rotor fault diagnosis based on neural network approach is presented. A neural network can substitute in a more effective way the faulted machine models used to formalize the knowledge base of the diagnostic system with suitably chosen inputs and outputs. Training the neural network by data achieved through experimental tests on healthy machines and through simulation in case of faulted machines, the diagnostic system can discern between `healthy' and `faulty' machines. This procedure substitutes the statement of a trigger threshold, needed in the diagnostic procedure based on the machine models.
Neural networks aided on-line diagnostics of induction motor rotor faults / Filippetti, F.; Franceschini, G.; Tassoni Carla, Null. - 1:(1993), pp. 316-323. (Intervento presentato al convegno Proceedings of the 28th Annual Meeting of the IEEE Industry Applications Conference tenutosi a Toronto, Ont, Can, null nel 1993).
Neural networks aided on-line diagnostics of induction motor rotor faults
Franceschini, G.;
1993
Abstract
An improvement of induction machine rotor fault diagnosis based on neural network approach is presented. A neural network can substitute in a more effective way the faulted machine models used to formalize the knowledge base of the diagnostic system with suitably chosen inputs and outputs. Training the neural network by data achieved through experimental tests on healthy machines and through simulation in case of faulted machines, the diagnostic system can discern between `healthy' and `faulty' machines. This procedure substitutes the statement of a trigger threshold, needed in the diagnostic procedure based on the machine models.
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