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

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Machine Learning Based Failure Prognostics of Aluminum Electrolytic Capacitors

머신러닝을 이용한 알루미늄 전해 커패시터 고장예지

  • Park, Jeong-Hyun (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Seok, Jong-Hoon (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Cheon, Kang-Min (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Hur, Jang-Wook (Department of Mechanical System Engineering, Kumoh National institute of Technology)
  • 박정현 (금오공과대학교 기계시스템공학과) ;
  • 석종훈 (금오공과대학교 기계시스템공학과) ;
  • 천강민 (금오공과대학교 기계시스템공학과) ;
  • 허장욱 (금오공과대학교 기계시스템공학과)
  • Received : 2020.06.06
  • Accepted : 2020.08.26
  • Published : 2020.11.30
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Abstract

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

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References

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