Design and Performance Analysis of Docker-Based Smart Manufacturing Platform Based on Deep Learning Model

Abstract

Breakdown of equipment causes very large damage to the factory. Research is continuously being conducted to prevent break down of equipment by detecting abnormal signs before equipment failure. This paper proposes an anomaly detection for system architecture based on a docker container. A docker is a virtualized container with many performance and scalability advantages. We have used the deep learning model of Autoencoder to effectively anomaly detection and its performance has been proven through experiments.