Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance

被引：137

作者：

Aivaliotis, P. ^{[1
]}

Georgoulias, K. ^{[1
]}

Arkouli, Z. ^{[1
]}

Makris, S. ^{[1
]}

机构：

[1] Univ Patras, Lab Mfg Syst & Automat, Rion 26504, Greece

来源：

52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS) | 2019年 / 81卷

基金：

欧盟地平线“2020”;

关键词：

Digital Twin; physics-based modelling; predictive maintenance;

D O I：

10.1016/j.procir.2019.03.072

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a methodology for advanced physics-based modeling aiming to enable the Digital Twin (DT) concept in predictive maintenance applications. The proposed methodology consists of two main pillars; Digital model creation and Digital Twin enabling. Following this procedure, which is analyzed in the paper, the user is able to define, create and utilize the digital model of a resource, as well as to enable the DT concept. A digital model of an industrial robot was created aiming to validate the proposed methodology which can be utilized potentially in predictive maintenance applications. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：417 / 422

页数：6

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