Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes

  • Jung, Guik (Dept. of Software, Soongsil University) ;
  • Ha, Hyunsoo (Dept. of Software Convergence, Soongsil University Seoul) ;
  • Lee, Sangjun (Dept. of Software, Soongsil University)
  • Received : 2021.02.09
  • Accepted : 2021.06.12
  • Published : 2021.12.31
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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.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (No. IITP-2021-2018-0-01419) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and results of a study on the "HPC Support" Project, supported by the Ministry of Science and ICT and NIPA.

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