Incipient stator fault detector based on neural networks end symmetrical components analysis for induction motor drives

In this paper a computer system for monitoring and diagnostics of the stator windings of the induction motor fed from the frequencies converter is presented. A possibility of stator faults detection using a neural-network detector, possibly at the early stage of the damage, is checked. The experimental research was carried out for a small power induction motor, with different levels of the stator failures (special motor design allowed to test short circuits in each phase of the motor), load torque and converter frequencies. The measurement of stator phase currents, phase-to-phase voltages and their transformation to symmetrical components are used in stator winding condition monitoring. For diagnostic purposes, the Multi-Layer Perceptron (MLP) neural detectors are developed. Preliminary research of the neural network response (offline) showed that the neural networks with simple internal structure, used for the fault level detection, give satisfactory results, that is very important in practical realization. The designed detector was implemented in LabVIEW and tested during the operation of the machine (online).