Design and Development of an Edge-Computing Platform Towards 5G Technology Adoption for Improving Equipment Predictive Maintenance

被引：22

作者：

Mourtzis, Dimitris ^{[1
]}

Angelopoulos, John ^{[1
]}

Panopoulos, Nikos ^{[1
]}

机构：

[1] Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Authomat, Patras 26504, Greece

来源：

3RD INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING | 2022年 / 200卷

基金：

欧盟地平线“2020”;

关键词：

Edge Computing; Predictive Maintenance; 5G; MACHINE-TOOLS; MODEL;

D O I：

10.1016/j.procs.2022.01.259

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

During the last decades numerous innovations and inventions have been presented under the framework of Industry 4.0. The ultimate goal of the latest industrial revolution is the digitalization of modem manufacturing systems. Therefore, several techniques and technologies have been investigated by the academia and the industrial practitioners, such as Edge Computing. The abovementioned paradigm adheres to the distributed computing paradigm, which is an analogous to the trend of decentralization of manufacturing systems. However, there are still several challenges to be addressed in the integration of such decentralized computing systems in real-life industries. Therefore, in this paper, the design and preliminary development of an Edge-computing platform is proposed, in order to distribute the computational load to the nodes and by extension to enable the utilization of machine learning techniques for the calculation of Remaining Useful Life (RUL) of critical machine tool components. Moreover, the proposed framework promotes the utilization of 5G cellular networks, in order to take advantage of the ultra-low latency and increased bandwidth offered by this technology. (C) 2022 The Authors. Published by Elsevier B.V.

引用

页码：611 / 619

页数：9

共 27 条

[1]

Arkenberg Chris, 2020, UNBUNDLING CLOUD INT

[2] A systematic literature review of machine learning methods applied to predictive maintenance [J].

Carvalho, Thyago P. ;

Soares, Fabrizzio A. A. M. N. ;

Vita, Roberto ;

Francisco, Robert da P. ;

Basto, Joao P. ;

Alcala, Symone G. S. .

COMPUTERS & INDUSTRIAL ENGINEERING, 2019, 137

[3]

Deloitte, 2017, Predictive Maintenance: Next Generation Maintenance Systems

[4] Adaptive Sequential Offloading Game for Multi-Cell Mobile Edge Computing [J].

Deng, Maofei ;

Tian, Hui ;

Lyu, Xinchen .

2016 23RD INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT), 2016,

[5] Remaining useful life prediction based on state assessment using edge computing on deep learning [J].

Hsu, Hsin-Yao ;

Srivastava, Gautam ;

Wu, Hsin-Te ;

Chen, Mu-Yen .

COMPUTER COMMUNICATIONS, 2020, 160 :91-100

[6] Model transformation between OPC UA and UML [J].

Lee, Byunghun ;

Kim, Dae-Kyoo ;

Yang, Hyosik ;

Oh, Sungsoo .

COMPUTER STANDARDS & INTERFACES, 2017, 50 :236-250

[7]

Lee J., 2007, Bearing Data Set

[8] A survey on computation offloading modeling for edge computing [J].

Lin, Hai ;

Zeadally, Sherali ;

Chen, Zhihong ;

Labiod, Houda ;

Wang, Lusheng .

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2020, 169

[9] Smart manufacturing process and system automation - A critical review of the standards and envisioned scenarios [J].

Lu, Yuqian ;

Xu, Xun ;

Wang, Lihui .

JOURNAL OF MANUFACTURING SYSTEMS, 2020, 56 :312-325

[10] A Survey on Mobile Edge Computing: The Communication Perspective [J].

Mao, Yuyi ;

You, Changsheng ;

Zhang, Jun ;

Huang, Kaibin ;

Letaief, Khaled B. .

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (04) :2322-2358

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