Neural networks aided on-line diagnostics of induction motor rotor faults

Abstract

An improvement of induction machine rotor fault diagnosis based on a 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 when inputs and outputs are suitably chosen. 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, required by the diagnostic procedure based on the machine models.