Guik Jung, Hyunsoo Ha, Sangjun Lee, Journal of Information Processing Systems Vol. 17, No. 6, pp. 1071-1082, Dec. 2021  

10.3745/JIPS.01.0083
Keywords: Anormal Detection, Continuously Learning, Kubernetes, Non-disruptive Operation, Smart Factory
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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.

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