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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, ∙1 :  ∙ :  ∙ : 2014. 6. 26,  : 2014. 7. 4,  : 2014. 9. 19. *  (School of Electrical and Computer Engineering, University of Ulsan) ※ This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (NRF-2012R1A1A2043644) 18 Journal of The Korea Society of Computer and Information November 2014 outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well. ▸

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