Induction machine fault detection enhancement using a stator current high resolution spectrum

Abstract

Fault detection in squirrel cage induction machines based on stator current spectrum has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. In this paper, a modified version of MUSIC algorithm has been developed based on the faults characteristic frequencies. This method has been used to estimate the stator current spectrum. Then, an amplitude estimator has been proposed and a fault indicator has been derived for fault severity measurement. Simulated stator current data issued from a coupled electromagnetic circuits approach has been used to prove the appropriateness of the method for air gap eccentricity and broken rotor bars faults detection.