1315. Autoregressive modeling approach of vibration data for bearing fault diagnosis in electric motors

This study investigates the performance of autoregressive (AR) modeling method to detect bearing fault in induction motor. For this purpose, AR models of vibration signals which are acquired during the load performance tests of healthy and seven gradually aged cases of induction motor are constructed. The variation of AR coefficients with model order is compared for all cases of the motor from healthy to faulty. It is seen that sixtieth order model is adequate to reflect the progress of fault characteristics and the first AR coefficient gets bigger with aging. AR modeling error or residuals computed as the difference between original signals and their AR representation gets large in time domain which corresponds to decrease in modeling performance with aging. In addition the error computed as the absolute difference between spectra of the original signals and their AR models gets large in frequency domain and preserves bearing fault features as the energy increase in high frequencies above 1.5 kHz.