Fault Classification of an Induction Motor Using Texture Features of Vibration Signals

  • Won-Chul Jang
  • Myeongsu Kang
  • Jong-Myon Kim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 301)


This paper proposes an approach for a two-dimensional (2D) representation of vibration signals for the highly reliable fault classification of an induction motor. The resulting 2D data has texture characteristics showing repetitive patterns and this paper extracts these texture patterns by utilizing gray level co-occurrence matrix. Moreover, this paper employs a distance evaluation technique in order to avoid the unnecessary fault signatures in the extracted texture features. To identify multiple faults of an induction motor, this paper finally utilizes support vector regression as a classifier using the extracted fault signatures. Experimental results indicate that the proposed approach achieves high classification accuracy.


Fault classification Gray level co-occurrence matrix Induction motor Support vector regression Texture patterns 



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2012R1A1A2043644), and the Leading Industry Development for Economic Region (LeadER) grant funded the MOTIE (Ministry of Trade, Industry and Energy), Korea in 2013 (No. R0001220).


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of UlsanUlsanSouth Korea

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