bearing fault
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The present paper addresses a precise and an accurate mathematical model for three-phase squirrel cage induction motors, based on winding function theory. Through an analytical development, a comparative way is presented to separate the signature between the existence of the outer race bearing fault and the static eccentricity concerning the asymmetry of the air gap between the stator and the rotor. This analytical model proposes an effective signature of outer race defect separately from other signatures of static eccentricity. Simulation and experimental results are presented to validate the proposed analytical model.

The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method.