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A MUSIC-based method to detect broken rotor bars in induction motors

This paper addresses itself to develop a sensitive/reliable detection method for broken rotor bar fault in induction motors. The power spectrum density estimation of classical multiple signal classification (MUSIC), which possesses higher resolution with short-time sample, is applied to detect broken rotor bars as well as continuous subdivision Fourier transform, self-adaptive filter, and rotor slot harmonics based slip estimation. And thus, the detection sensitivity is improved satisfactorily. At the same time, the negative impact of the fluctuation of stator current on the detection reliability is decreased to some extent because only short-time sample is needed. Fault detection instances in laboratory demonstrate that the presented scheme can assure the detection sensitivity and reliability of broken rotor bar fault in induction motors.