Smart Structures and Systems

  • 제22권2호
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  • Pages.231-237
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  • 2018
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Application of compressive sensing and variance considered machine to condition monitoring

  • Lee, Myung Jun (School of Mechanical Engineering, Chonnam National University) ;
  • Jun, Jun Young (School of Mechanical Engineering, Chonnam National University) ;
  • Park, Gyuhae (School of Mechanical Engineering, Chonnam National University) ;
  • Kang, To (Nuclear Convergence Technology Division, Korea Atomic Energy Research Institute) ;
  • Han, Soon Woo (Nuclear Convergence Technology Division, Korea Atomic Energy Research Institute)
  • 투고 : 2017.05.20
  • 심사 : 2017.11.28
  • 발행 : 2018.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

키워드

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea (NRF)

참고문헌

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