Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Real-time Fault Detection System of a Pneumatic Cylinder Via Deep-learning Model Considering Time-variant Characteristic of Sensor Data

센서 데이터의 시계열 특성을 고려한 딥러닝 모델 기반의 공압 실린더 고장 감지 시스템 구현

  • Byeong Su Kim (Department of Industrial & Management Engineering, Hanbat National University) ;
  • Geun Myeong Song (Department of Industrial & Management Engineering, Hanbat National University) ;
  • Min Jeong Lee (Department of Industrial & Management Engineering, Hanbat National University) ;
  • Sujeong Baek (Department of Industrial & Management Engineering, Hanbat National University)
  • 김병수 (국립한밭대학교 산업경영공학과) ;
  • 송근명 (국립한밭대학교 산업경영공학과) ;
  • 이민정 (국립한밭대학교 산업경영공학과) ;
  • 백수정 (국립한밭대학교 산업경영공학과)
  • Received : 2024.05.23
  • Accepted : 2024.06.17
  • Published : 2024.06.30
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Abstract

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder's status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1069296). This work was also supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0012744, HRD program for industrial innovation).

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