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Mechanical Fault Classification of an Induction Motor using Texture Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2013, 18(12), pp.11-19
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

장원철 1 박용훈 1 강명수 1 Jong Myon Kim 1

1울산대학교

Accredited

ABSTRACT

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover,the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

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.