Diagnosis of broken-bars fault in induction machines using higher order spectral analysis

Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5kW-220 V/380V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph. ► A new method has been proposed for the detection of bar breakages induction motors. ► This technique uses the Fourier transform to obtain the bi-spectrum of the motor stator current. ► The bi-spectrum is compared with the conventional power spectrum, showing its superiority. ► The bi-spectrum demonstrates superior fault diagnosis capabilities at low level of shaft load.