Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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A Development on the Fault Prognosis of Bearing with Empirical Mode Decomposition and Artificial Neural Network

경험적 모드 분해법과 인공 신경 회로망을 적용한 베어링 상태 분류 기법

  • Park, Byeonghui (Department of Mechanical Design and Manufacturing, Changwon National University) ;
  • Lee, Changwoo (School of Mechanical Engineering, Changwon National University)
  • 박병희 (창원대학교 기계설계공학과) ;
  • 이창우 (창원대학교 기계공학부)
  • Received : 2016.10.14
  • Accepted : 2016.11.23
  • Published : 2016.12.01
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Abstract

Bearings have various uses in industrial equipment. The lifetime of bearings is often lesser than anticipated at the time of purchase, due to environmental wear, processing, and machining errors. Bearing conditions are important, since defects and damage can lead to significant issues in production processes. In this study, we developed a method to diagnose faults in the bearing conditions. The faults were determined using kurtosis, average, and standard deviation. An intrinsic mode function for the data from the selected axis was extracted using empirical mode decomposition. The intrinsic mode function was obtained based on the frequency, and the learning data of ANN (Artificial Neural Network) was concluded, following which the normal and fault conditions of the bearing were classified.

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References

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