Table of Contents Author Guidelines Submit a Manuscript
Wireless Communications and Mobile Computing
Volume 2018 (2018), Article ID 5795037, 13 pages
https://doi.org/10.1155/2018/5795037
Research Article

SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education, 1600 Gajeon-ri, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31253, Republic of Korea

Correspondence should be addressed to Chang-Heon Oh; rk.ca.hcetaerok@hohc

Received 28 July 2017; Accepted 18 December 2017; Published 29 January 2018

Academic Editor: Yong Ren

Copyright © 2018 Hyun-Jun Shin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

CPS is potential application in various fields, such as medical, healthcare, energy, transportation, and defense, as well as Industry 4.0 in Germany. Although studies on the equipment aging and prediction of problem have been done by combining CPS with Industry 4.0, such studies were based on small numbers and majority of the papers focused primarily on CPS methodology. Therefore, it is necessary to study active self-protection to enable self-management functions, such as self-healing by applying CPS in shop-floor. In this paper, we have proposed modeling of shop-floor and a dynamic reconfigurable CPS scheme that can predict the occurrence of anomalies and self-protection in the model. For this purpose, SVM was used as a machine learning technology and it was possible to restrain overloading in manufacturing process. In addition, we design CPS framework based on machine learning for Industry 4.0, simulate it, and perform. Simulation results show the simulation model autonomously detects the abnormal situation and it is dynamically reconfigured through self-healing.