A Method for Detecting Half-Broken Rotor Bar in Lightly Loaded Induction Motors Using Current

TLDR: The proposed method is based on spectral preprocessing of the stator current followed by subspace decomposition of the signal autocorrelation matrix to detect relatively low-amplitude fault sidebands and is found to be very effective in detecting low-AMplitude sinusoids in a signal dominated by high-amPLitude fundamental.

Abstract: This paper presents an effective method of motor current signature analysis for detecting half- as well as full broken single rotor bar fault of a squirrel-cage induction machine under various loading conditions and speeds. The proposed method is based on spectral preprocessing of the stator current followed by subspace decomposition of the signal autocorrelation matrix to detect relatively low-amplitude fault sidebands. This method is found to be very effective in detecting low-amplitude sinusoids in a signal dominated by high-amplitude fundamental. The extended Kalman filter is used to estimate and track the fundamental component of the stator current. This component is subtracted from the measured stator current at every time step generating a resultant signal with a very low or negligible fundamental component. Subsequently, multiple-signal classification (MUSIC) is applied on the resultant stator current signal. Motor slip is estimated from principle slot harmonic to decide the approximate location of the fault sidebands. For effective fault detection, a hypothesis test is proposed to check the presence of sufficient fault frequency sideband in the current spectrum. This test works better if the lobe in the MUSIC plot due to the fault frequency is not distorted or overlapped by the fundamental component. Therefore, for each data window, the minimum size of the autocorrelation matrix is determined to generate distinct peaks. The proposed method applies to steady-state condition and is found to exhibit superior performance even during the light-load conditions with a half-broken bar.