Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Fault Diagnosis of Industrial Robots using CNN and Vibration Data

CNN과 진동데이터를 활용한 산업용 로봇의 고장 진단

  • Mi Jin Kim (Department of IT Semiconductor Engineering, Tech University of Korea) ;
  • Kyo Mun Ku (Department of IT Semiconductor Engineering, Tech University of Korea) ;
  • Saiful Islam (Department of IT Semiconductor Engineering, Tech University of Korea) ;
  • Myung-Jin Chung (Department of Mechatronics Engineering, Tech University of Korea) ;
  • Hyo Young Kim (Department of Mechatronics Engineering, Tech University of Korea) ;
  • Kihyun Kim (Department of Mechatronics Engineering, Tech University of Korea)
  • 김미진 (한국공학대학교 IT반도체융합공학부) ;
  • 구교문 (한국공학대학교 IT반도체융합공학부) ;
  • 사이풀 (한국공학대학교 IT반도체융합공학부) ;
  • 정명진 (한국공학대학교 메카트로닉스공학부) ;
  • 김효영 (한국공학대학교 메카트로닉스공학부) ;
  • 김기현 (한국공학대학교 메카트로닉스공학부)
  • Received : 2024.09.16
  • Accepted : 2024.09.14
  • Published : 2024.09.30
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Abstract

Products were typically produced using specialized equipment such as CNC machines, milling machines, and lathes in traditional manufacturing. However, modern manufacturing is increasingly attempting with technological advancements to leverage large industrial robots for machining, offering greater flexibility, efficiency, and a high degree of freedom throughout the entire production process. Additionally, the demand for industrial robots continues to rise as industries adopt smart factories. These robots are becoming larger, more precise, and faster, as they take over tasks previously requiring specialized equipment or skilled human operators. Where numerous robots are in operation in factories, ensuring a stable supply chain and maintaining operational uptime is crucial. Therefore, preparing for potential mechanical failures in each robot is necessary, and there is a growing need for technologies that enable real-time fault diagnosis and predictive maintenance. A large industrial robot used for machining was employed as a testbed for fault diagnosis in this study. The Vibration data was collected from various robot axes under both normal operating conditions and abnormal conditions, such as end-effector overloads and drive malfunctions. The collected vibration data was then preprocessed, and key features were analyzed and extracted. The extracted features were used to build a learning model, and in this study, the CNN (Convolutional Neural Network) algorithm was applied instead of k-NN (k-Nearest Neighbors) to diagnose defects occurring in the discontinuous movements of the robot, thereby improving accuracy.

Keywords

Acknowledgement

본 연구는 산업통상자원부의 국제공동기술개발사업의 일환으로 수행하였습니다. [P159100011, 과제명: 스마트 워크피스 홀더와 스마트 그리퍼 적용 밀링/라우팅/트리밍 가공용 로봇 가공 시스템 개발]

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