Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Corrosion Failure Diagnosis of Rolling Bearing with SVM

SVM 기법을 적용한 구름베어링의 부식 고장진단

  • Go, Jeong-Il (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Lee, Eui-Young (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Lee, Min-Jae (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Choi, Seong-Dae (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Hur, Jang-Wook (Department of Mechanical System Engineering, Kumoh National institute of Technology)
  • 고정일 (금오공과대학교 기계시스템공학과) ;
  • 이의영 (금오공과대학교 기계시스템공학과) ;
  • 이민재 (금오공과대학교 기계시스템공학과) ;
  • 최성대 (금오공과대학교 기계시스템공학과) ;
  • 허장욱 (금오공과대학교 기계시스템공학과)
  • Received : 2021.05.27
  • Accepted : 2021.07.19
  • Published : 2021.09.30
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Abstract

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

Keywords

Acknowledgement

이 논문은 2019년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. 2019R1I1A3A01063935).

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