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Real-time Fault Detection System of a Pneumatic Cylinder Via Deep-learning Model Considering Time-variant Characteristic of Sensor Data KCI 등재
센서 데이터의 시계열 특성을 고려한 딥러닝 모델 기반의 공압 실린더 고장 감지 시스템 구현
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/435312
pp.10-20
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.
2. 공압 실린더의 정상 및 고장 상태 정의 및센서 데이터 수집
2.1 연구 대상: 자동화 생산 시스템의 공압 실린더
2.2 공압 실린더의 정상 및 고장 상태 정의
2.3 다변량 아날로그 센서 데이터 수집
3. 공압 실린더의 고장 감지를 위한 센서데이터 분석
3.1 센서 데이터 전처리
3.2 고장 감지 및 분류를 위한 LSTM 기반 딥러닝모델 구축
3.3 고장 감지 성능 향상을 위한 차원 축소
4. 실시간 고장 감지 시스템 구현
5. 결 론
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