A microservice architecture for predictive analytics in manufacturing

被引:8
|
作者
Nikolakis, N. [1 ]
Marguglio, A. [2 ]
Veneziano, G. [2 ]
Greco, P. [2 ]
Panicucci, S. [3 ]
Cerquitelli, T. [4 ]
Macii, E. [5 ]
Andolina, S. [6 ]
Alexopoulos, K. [1 ]
机构
[1] Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras, Greece
[2] Engn Ingn Informat SpA, Palermo, Italy
[3] COMAU SpA, Turin, Italy
[4] Politecn Torino, Dept Control & Comp Engn, Turin, Italy
[5] Politecn Torino, Interuniv Dept Reg & Urban Studies & Planning, Turin, Italy
[6] SynArea Consultants Srl, Turin, Italy
来源
30TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING (FAIM2021) | 2020年 / 51卷
基金
欧盟地平线“2020”;
关键词
Machine learning; Service-oriented platform; Plug-and-play approach; Microservice architecture; Robotics industry; REQUIREMENTS; DEPLOYMENT; SERVICE;
D O I
10.1016/j.promfg.2020.10.153
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multitenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform. (C) 2020 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:1091 / 1097
页数:7
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