대한임베디드공학회논문지 (IEMEK Journal of Embedded Systems and Applications)

대한임베디드공학회 (Institute of Embedded Engineering of Korea)
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전이 학습과 진동 신호를 이용한 설비 고장 진단 및 분석

Fault Diagnosis and Analysis Based on Transfer Learning and Vibration Signals

  • Yun, Jong Pil (Korea Institute of Industrial Technology) ;
  • Kim, Min Su (Pohang University of Science and Technology) ;
  • Koo, Gyogwon (Pohang University of Science and Technology) ;
  • Shin, Crino (Korea Institute of Industrial Technology, Kyungpook National University)
  • 투고 : 2019.09.04
  • 심사 : 2019.09.23
  • 발행 : 2019.12.31
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초록

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

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참고문헌

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