Detection of IM broken rotor bars using MUSIC pseudo-spectrum and pattern recognition

This paper presents some analysis techniques of the space-vector of voltages induced in the stator windings after supply disconnection, to detect broken rotor bars in squirrel cage induction machines. When the motor is disconnected from the supply no currents flow in the stator windings and the voltages measurable at its terminals are due to flux produced by rotor currents. When the rotor is healthy, the voltages measured at motor terminals are almost sinusoidal because of the symmetry of rotor windings. When there are broken rotor bars, the magneto-motive force (m.m.f.) generated by rotor windings results distorted, and some particular harmonics, contained in the voltages induced in the stator windings, increase their amplitudes. The diagnostic technique is based on monitoring these voltage harmonics by analyzing the space vector of the voltages induced in the stator windings via short-time MUSIC (STMUSIC) time-frequency pseudo-representation. The obtained results have been used to train a Kohonen neural-network that is able to automatically classify data measured on healthy and faulty induction motors.