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 Classification of Machining Center using Acceleration Data Based on 1D CNN Algorithm

1D CNN 알고리즘 기반의 가속도 데이터를 이용한 머시닝 센터의 고장 분류 기법 연구

  • Kim, Ji-Wook (Extreme Fabrication Technology Group, Korea Institute of Industrial Technology(KITECH)) ;
  • Jang, Jin-Seok (Construction Equipment R&D / AI System Engineering Group, Korea Institute of Industrial Technology(KITECH)) ;
  • Yang, Min-Seok (Extreme Fabrication Technology Group, Korea Institute of Industrial Technology(KITECH)) ;
  • Kang, Ji-Heon (Extreme Fabrication Technology Group, Korea Institute of Industrial Technology(KITECH)) ;
  • Kim, Kun-Woo (Construction Equipment R&D / AI System Engineering Group, Korea Institute of Industrial Technology(KITECH)) ;
  • Cho, Young-Jae (Extreme Fabrication Technology Group, Korea Institute of Industrial Technology(KITECH)) ;
  • Lee, Jae-Wook (Extreme Fabrication Technology Group, Korea Institute of Industrial Technology(KITECH))
  • 김지욱 (한국생산기술연구원 극한가공기술그룹) ;
  • 장진석 (한국생산기술연구원 건설기계부품그룹/AI 시스템응용기술그룹) ;
  • 양민석 (한국생산기술연구원 극한가공기술그룹) ;
  • 강지헌 (한국생산기술연구원 극한가공기술그룹) ;
  • 김건우 (한국생산기술연구원 건설기계부품그룹/AI 시스템응용기술그룹) ;
  • 조용재 (한국생산기술연구원 극한가공기술그룹) ;
  • 이재욱 (한국생산기술연구원 극한가공기술그룹)
  • Received : 2019.08.12
  • Accepted : 2019.09.02
  • Published : 2019.09.30
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Abstract

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

Keywords

References

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