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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 : Computer Science
2014, 19(11), pp.17-24
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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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.
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