Bearing Faults Identification of an Induction Motor using Acoustic Emission Signals and Histogram Modeling

Abstract

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner,•제1저자 : 장원철 •교신저자 : 김종면