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

  • 제19권9호
  • /
  • Pages.47-52
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  • 2020
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  • 1598-6721(pISSN)
  • /
  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
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PCA-SVM 기반의 SMPS 고장예지에 관한 연구

Fault Prognostics of a SMPS based on PCA-SVM

  • 유연수 (금오공과대학교 기계시스템공학과) ;
  • 김동현 (금오공과대학교 기계시스템공학과) ;
  • 김설 (금오공과대학교 기계시스템공학과) ;
  • 허장욱 (금오공과대학교 기계시스템공학과)
  • Yoo, Yeon-Su (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Kim, Dong-Hyeon (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Kim, Seol (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Hur, Jang-Wook (Department of Mechanical System Engineering, Kumoh National institute of Technology)
  • 투고 : 2020.06.03
  • 심사 : 2020.07.06
  • 발행 : 2020.09.30
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초록

With the 4th industrial revolution, condition monitoring using machine learning techniques has become popular among researchers. An overload due to complex operations causes several irregularities in MOSFETs. This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failure mode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the current is beyond the threshold value. Several features were extracted from the collected voltage signals that indicate the health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller sample space by using a principal component analysis (PCA). A robust machine learning algorithm, the support vector machine (SVM), was used to classify different health states of an SMPS, and the classification results are presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong fault diagnosis framework for an SMPS.

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참고문헌

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