Research on Equipment Maintenance Guidance Technology Based on MR and Digital Twin

2021 ◽  
Author(s):  
Yihao Li ◽  
Zhili Zhang ◽  
Xiangyang Li ◽  
Shiyin Guan
Keyword(s):  
Equipment Maintenance ◽  
Digital Twin

scholarly journals Digital twin model of equipment maintenance management in modern enterprises

2021 ◽  
Vol 1986 (1) ◽  
pp. 012088
Author(s):  
Yabin Wang ◽  
Shuai Wang ◽  
Jinguo Wang
Keyword(s):  
Maintenance Management ◽  
Equipment Maintenance ◽  
Digital Twin ◽  
Twin Model

scholarly journals Research on Application of Digital Twin in Simulation of Equipment Maintenance Support

2020 ◽  
Vol 782 ◽  
pp. 032088
Author(s):  
Yukun Chen ◽  
Jian Chen ◽  
Cong Li ◽  
Jun Xu ◽  
Boao Xu
Keyword(s):  
Equipment Maintenance ◽  
Digital Twin ◽  
Research On Application

Improving equipment maintenance by means of a preplanning technique.

1955 ◽  
Author(s):  
John V. Moore ◽  
Eli Saltz ◽  
Arthur J. Hoehn
Keyword(s):  
Equipment Maintenance

Heat and dust medical-equipment maintenance in the Punjab

IEE Review ◽  
1992 ◽  
Vol 38 (6) ◽  
pp. 219
Author(s):  
Andrew (AM) Gammie
Keyword(s):  
Medical Equipment ◽  
Equipment Maintenance ◽  
Medical Equipment Maintenance

1006-P: Personalized Hybrid Closed-Loop Therapy Using a Digital Twin in Patients with Type 1 Diabetes: At-Home Data

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1006-P
Author(s):  
BENYAMIN GROSMAN ◽  
ANIRBAN ROY ◽  
DI WU ◽  
NEHA PARIKH ◽  
LOUIS J. LINTEREUR ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Digital Twin ◽  
At Home

The Digital Twin Theory

2019 ◽  
Vol 2019 (1) ◽  
pp. 27-30 ◽  
Author(s):  
Andreas Deuter ◽  
◽  
Florian Pethig ◽  
Keyword(s):  
Digital Twin

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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