본문 바로가기
  • Home

Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2014, 19(4), pp.55-62
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

장원철 1 Jong Myon Kim 1

1울산대학교

Accredited

ABSTRACT

This paper proposes a fault detection method utilizing converted images of acoustic emissionsignals and texture analysis for identifying bearing’s multi-faults which frequently occur in aninduction motor. The proposed method analyzes three texture features from the converted imagesof multi-faults: multi-faults image’s entropy, homogeneity, and energy. These extracted featuresare then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner,inner-roller, and outer-roller. The experimental results using ten times trials indicate that theproposed method achieves 100% accuracy in the fault classification.

Citation status

* References for papers published after 2023 are currently being built.

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