JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes |
| 영문제목(English Title) |
Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes |
| 저자(Author) |
Guik Jung
Hyunsoo Ha
Sangjun Lee
|
| 원문수록처(Citation) |
VOL 17 NO. 6 PP. 1071 ~ 1082 (2021. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models. |
| 키워드(Keyword) |
Anormal Detection
Continuously Learning
Kubernetes
Non-disruptive Operation
Smart Factory
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| 파일첨부 |
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