This paper proposes a method using empirical mode decomposition (EMD) and histogram modeling for bearing defects of a low-speed rotational induction motor. The proposed method minimizes signal distortion caused by noise using the EMD and conducts histogram envelope modeling by normalizing a signal. Then, the method extracts and selects unique features of each fault using partial autocorrelation coefficients (PARCOR) and distance evaluation technique (DET). Using the extracted features as inputs, support vector Received(July 10, 2014), Review request(July 11, 2014), Review Result(1st: July 24, 2014) Accepted(December 10, 2014) Publish(December 31, 2014) 689-749 School of Electrical Engineering, University of Ulsan, Nam-gu, Ulsan, Korea. email: fahmid.farid@gmail.com 689-749 School of Electrical Engineering, University of Ulsan, Nam-gu, Ulsan, Korea. email: nasha0339@gmail.com (Corresponding Author) 689-749 School of Electrical Engineering, University of Ulsan, Nam-gu, Ulsan, Korea. email: jmkim07@ulsan.ac.kr * 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) A Feature Extraction Method Using Empirical Mode Decomposition and Histogram Modeling for Robust Bearing Fault Diagnosis Copyright c 2014 SERSC 282 regression (SVR) classifies inner, outer, and roller faults of a bearing. For the optimal classification performance, we vary a variable of the kernel function of SVR ranging from 0.01 to 1.0 and the number of features ranging from 2 to 250. Experimental results indicate that the proposed method having a parameter of 0.55 and 45 features outperforms conventional fault classification methods, yielding an average of 95% classification performance.