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Implementation of Smart Devices and Applications for Monitoring the Load Power of Industrial Manufacturing Machine

산업용 생산 장비의 부하 전력 모니터링을 위한 스마트 디바이스와 애플리케이션의 구현

  • Wahyutama, Aria Bisma (Department of Information and Communication Engineering, Changwon National University) ;
  • Yoo, Bongsoo (Research and Development Team, GLPI Company) ;
  • Hwang, Mintae (Department of Information and Communication Engineering, Changwon National University)
  • Received : 2021.12.24
  • Accepted : 2022.01.07
  • Published : 2022.03.31

Abstract

This paper contains the results of developing smart devices and applications to monitor the load power of the industrial manufacturing machine and evaluate its performance. The smart devices in this paper are divided into two functionalities, which are collecting load power along with operating environment data of industrial manufacturing machines and transmitting the data to servers. Load power data collected from the smart devices are uploaded to MariaDB inside the Amazon Web Service (AWS) server. Using the RESTFul API, the uploaded power data can be retrieved and shown on the web and mobile application in the form of a graph to provide monitoring capability. To evaluate the performance of the developed system, the response time from MariaDB to web and mobile applications was measured. The results is ranging from 0.0256 to 0.0545 seconds in a 4G (LTE) network environment and from 0.6126 to 1.2978 seconds in a 3G network environment, which is considered a satisfactory result.

본 논문은 산업용 생산 장비의 부하 전력을 모니터링하기 위한 스마트 디바이스와 애플리케이션을 개발하고 성능을 평가한 결과를 담고 있다. 스마트 디바이스는 산업용 생산 장비의 부하 전력 및 동작 환경 데이터를 수집해 이를 서버로 전송하는 기능을 가진 스마트 전력 디바이스와 이의 경량화 버전에 해당하는 에너지 미터링 디바이스로 구분해 하드웨어와 더불어 펌웨어를 개발하였다. 이들 디바이스에서 수집한 부하 전력 데이터는 AWS(Amazon Web Services) 서버내 MariaDB에 저장될 수 있도록 하였으며, RESTFul API를 이용해 웹사이트 및 모바일 애플리케이션 상에서 그래프 형태로 부하 전력을 모니터링할 수 있도록 구현하였다. 개발 시스템의 성능을 평가하기 위해 MariaDB에서 웹사이트 및 모바일 애플리케이션까지의 응답 시간을 측정하였으며, 4G(LTE) 네트워크 환경에서는 0.0256~0.0545초, 3G 네트워크 환경에서는 0.6126~1.2978초로 만족스러운 결과를 보여줌을 알 수 있었다.

Keywords

Acknowledgement

This work was supported by Gyeongnam SW Convergence Cluster 2.0 under the contract and the "Leaders in INdustry-university Cooperation +" Project by the Ministry of Education and National Research Foundation of Korea.

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