A fault diagnostics system of three-phase induction motors was implemented. The implemented system was based on acoustic signals of three-phase induction motors. A feature extraction step was performed using SMOFS-20-EXPANDED (shortened method of frequencies selection-20-Expanded). A classification step was performed using 3 classifiers: LDA (Linear Discriminant Analysis), NBC (Naive Bayes Classifier), CT (Classification Tree). An analysis was carried out for incipient states of three-phase induction motors measured under laboratory conditions. The author measured and analysed the following states of motors: healthy motor, motor with one faulty rotor bar, motor with two faulty rotor bars, motor with faulty ring of squirrel-cage. Measured and analysed states were caused by natural degradation of parts of the machine. The efficiency of recognition of the analysed states was good. The proposed method of fault diagnostics can find application in protection of three-phase induction motors.

The aim of this work is to study the influence of closed loop control on diagnostic indices of both broken bar and mixed air-gap eccentricity fault indices of the squirrel cage induction motor drive. The present work is focused on the direct stator current isd signal analysis, which is independent of torque load when the induction motor is controlled by an indirect control field. The fault signatures are on the line extracted from the direct stator current signal using the discrete Fourier transformation (DFT). The formula of the measured direct stator current at both conditions is determined by the transfer function of the current loop. The obtained results show that the current loop corresponds to a low pass filter and can reduce the magnitude of diagnostic indicators which lead to wrong evaluation of the fault. Simulation and experiments were carried out in order to confirm the theoretical analysis.