한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권7호
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  • Pages.981-988
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  • 2022
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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생산 공정에서 CNN을 이용한 음향 PSD 영상 기반 공구 상태 진단 기법

Sound PSD Image based Tool Condition Monitoring using CNN in Machining Process

  • 투고 : 2022.04.04
  • 심사 : 2022.06.21
  • 발행 : 2022.07.31
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⟨ 이전 논문 다음 논문 ⟩

초록

정보통신기술(ICT)를 적용한 스마트팩토리로 불리는 지능형 생산 공장은 각종 센서를 통해 공정 데이터를 실시간으로 수집하고 있다. 이렇게 수집된 데이터를 효과적으로 활용하는 연구가 많이 진행되고 있는데, 본 논문에서는 생산 공정에서 발생되는 음향 신호를 기반으로 공구 상태를 진단하는 기법을 제안한다. 첫 번째로 결함이 있는 공구를 감지할 뿐만 아니라 공회전 및 공정 운용에 따른 다양한 공구 상태를 제시한다. 두 번째로 푸리에 분석을 이용하여 사운드의 전력스펙트럼을 영상으로 표현하고, 데이터에 숨겨진 건강한 패턴을 드러내고, 강조하기 위해 일부 변형을 적용한다. 마지막으로 이렇게 획득한 대비 강화된 PSD 영상은 CNN을 이용해 상태별로 진단한다. 그 결과 제안한 음향 PSD 영상 + CNN 방법은 데이터의 차별화된 특징이 잘 반영되어 공구 상태에 따른 높은 진단 결과를 보여준다.

The intelligent production plant called smart factories that apply information and communication technology (ICT) are collecting data in real time through various sensors. Recently, researches that effectively applying to these collected data have gained a lot of attention. This paper proposes a method for the tool condition monitoring based on the sound signal generated in machining process. First, it not only detects a fault tool, but also presents various tool states according to idle and active operation. The second, it's to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. Finally, the contrast-enhanced PSD image obtained is diagnosed by using CNN. The results of the experiments demonstrate the high discrimination potential afforded by the proposed sound PSD image + CNN and show high diagnostic results according to the tool status.

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참고문헌

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