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A Study on the Build of Equipment Predictive Maintenance Solutions Based on On-device Edge Computer

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(4), pp.165-172
  • DOI : 10.9708/jksci.2020.25.04.165
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 24, 2020
  • Accepted : April 16, 2020
  • Published : April 30, 2020

Yong-Hwan Lee 1,  Suh, Jin Hyung 2

1주식회사 이안에스아이티
2경인여자대학교

Accredited

ABSTRACT

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

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This paper was written with support from the National Research Foundation of Korea.
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