Stator resistance imbalance fault is one of the most common faults happens in induction motors (IMs). Effective stator resistance imbalance detection plays significant roles in reducing maintenance costs and unscheduled downtimes. Stator resistance imbalance faults information can be detected through analysing the motor supply current signals. Various motor current signal processing methods have been investigated to extract the weak diagnostic feature from noisy current signals based on advanced signal processing analysis. In this paper, Teager-Kaiser energy operator (TKEO) is proposed to identify fault signatures based on motor current signal analysis (MCSA). TKEO is able to increase the signal-to-noise ratio and has the ability of demodulating FM as well AM signal for fault feature extraction. Experimental analysis results show that TKEO can successfully identify and extract the fault characteristic frequency for the condition monitoring and fault diagnosis of IMs operating under different operations based on MCSA.
|Number of pages||4|
|Journal||International Journal of COMADEM|
|Publication status||Published - 7 May 2020|