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

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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A Study on Fault Diagnosis Algorithm for Rotary Machine using Data Mining Method and Empirical Mode Decomposition

데이터 마이닝 기법 및 경험적 모드 분해법을 이용한 회전체 이상 진단 알고리즘 개발에 관한 연구

  • 윤상환 (창원대학교 컨소시엄사업단) ;
  • 박병희 (창원대학교 기계설계공학과) ;
  • 이창우 (창원대학교 기계설계공학과)
  • Received : 2016.04.08
  • Accepted : 2016.05.31
  • Published : 2016.08.31
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Abstract

Rotary machine is major equipment in industry. The rotary machine is applied for a machine tool, ship, vehicle, power plant, and so on. But a spindle fault increase product's expense and decrease quality of a workpiece in machine tool. A turbine in power plant is directly connected to human safety. National crisis could be happened by stopping of rotary machine in nuclear plant. Therefore, it is very important to know rotary machine condition in industry field. This study mentioned fault diagnosis algorithm with statistical parameter and empirical mode decomposition. Vibration locations can be found by analyze kurtosis of data from triaxial axis. Support vector of data determine threshold using hyperplane with fault location. Empirical mode decomposition is used to find fault caused by intrinsic mode. This paper suggested algorithm to find direction and causes from generated fault.

Keywords

References

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