On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods

Broken rotor bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Motor current signature analysis (MCSA) techniques are applied to inspect the spectrum amplitudes at the broken rotor bar specific frequencies for abnormality and to decide about broken rotor bar fault detection and diagnosis. In this paper, we have demonstrated with experimental results that the use of a lower sampling rate with a digital notch filter is feasible for MCSA in broken rotor bar detection with discrete-time Fourier transform and autoregressive-based spectrum methods. The use of the lower sampling rate does not affect the performance of the fault detection, while requiring much less computation and low cost in implementation, which would make it easier to implement in embedded systems for motor condition monitoring.