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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2014, 19(11), pp.17-24
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

장원철 1 서준상 1 Jong Myon Kim 1

1울산대학교

Accredited

ABSTRACT

This paper proposes a fault detection method for low-speed rolling element bearings of an inductionmotor using acoustic emission signals and histogram modeling. The proposed method performs envelopmodeling of the histogram of normalized fault signals. It then extracts and selects significant features ofeach 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, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed methodwith varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and thenumber of features from 2 to 150. Experimental results show that the proposed fault identification methodusing 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performanceand outperforms conventional fault diagnosis methods as well.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.