Multirate Signal Processing to Improve FFT-Based Analysis for Detecting Faults in Induction Motors

Abstract

Fault detection in induction motors (IM) has been studied during the past decades due to the role that these electric machines play in industry. Regular monitoring is performed on IM to diagnose their operating condition using vibration and stator current analysis. The acquired signal is then processed to extract the characteristic parameters of the fault. The fast Fourier transform (FFT) is used for this task, but it has intrinsic limitations like sensitivity to low signal-to-noise ratio, overlapping of closely-located spectral components, nonstationary signals, and spectral leakage. These limitations have been studied to improve the spectrum estimation, but spectral leakage has not received enough attention, even when its effects can be significant. This paper introduces multirate signal processing techniques that improve the FFT-based methods by reducing spectral leakage with fractional resampling. The methodology is applied to experimental signals to show the improvement of the FFT-based methods for detecting faults in IM.